MIT News - Computer science and technology MIT News is dedicated to communicating to the media and the public the news and achievements of the students, faculty, staff and the greater MIT community. en Mon, 09 Mar 2020 00:00:00 -0400 The elephant in the server room Catherine D’Ignazio’s new book, “Data Feminism,” examines problems of bias and power that beset modern information. Mon, 09 Mar 2020 00:00:00 -0400 Peter Dizikes | MIT News Office <p>Suppose you would like to know mortality rates for women during childbirth, by country, around the world. Where would you look? One option is the <a href="" target="_blank">WomanStats</a> Project, the website of an academic research effort investigating the links between the security and activities of nation-states, and the security of the women who live in them.</p> <p>The project, founded in 2001, meets a need by patching together data from around the world. Many countries are indifferent to collecting statistics about women’s lives. But even where countries try harder to gather data, there are clear challenges to arriving at useful numbers — whether it comes to women’s physical security, property rights, and government participation, among many other issues. &nbsp;</p> <p>For instance: In some countries, violations of women’s rights may be reported more regularly than in other places. That means a more responsive legal system may create the appearance of greater problems, when it provides relatively more support for women. The WomanStats Project notes many such complications.</p> <p>Thus the WomanStats Project offers some answers — for example, Australia, Canada, and much of Western Europe have low childbirth mortality rates — while also showing what the challenges are to taking numbers at face value. This, according to MIT professor Catherine D’Ignazio, makes the site unusual, and valuable.</p> <p>“The data never speak for themselves,” says D’Ignazio, referring to the general problem of finding reliable numbers about women’s lives. “There are always humans and institutions speaking for the data, and different people have their own agendas. The data are never innocent.”</p> <p>Now D’Ignazio, an assistant professor in MIT’s Department of Urban Studies and Planning, has taken a deeper look at this issue in a new book, co-authored with Lauren Klein, an associate professor of English and quantitative theory and methods at Emory University. In the book, “<a href="" target="_blank">Data Feminism</a>,” published this month by the MIT Press, the authors use the lens of intersectional feminism to scrutinize how data science reflects the social structures it emerges from.</p> <p>“Intersectional feminism examines unequal power,” write D’Ignazio and Klein, in the book’s introduction. “And in our contemporary world, data is power too. Because the power of data is wielded unjustly, it must be challenged and changed.”</p> <p><strong>The 4 percent problem</strong></p> <p>To see a clear case of power relations generating biased data, D’Ignazio and Klein note, consider research led by MIT’s own Joy Buolamwini, who as a graduate student in a class studying facial-recognition programs, observed that the software in question could not “see” her face. Buolamwini found that for the facial-recognition system in question, the software was based on a set of faces which were 78 percent male and 84 percent white; only 4 percent were female and dark-skinned, like herself.&nbsp;</p> <p>Subsequent media coverage of Buolamwini’s work, D’Ignazio and Klein write, contained “a hint of shock.” But the results were probably less surprising to those who are not white males, they think.&nbsp;&nbsp;</p> <p>“If the past is racist, oppressive, sexist, and biased, and that’s your training data, that is what you are tuning for,” D’Ignazio says.</p> <p>Or consider another example, from tech giant Amazon, which tested an automated system that used AI to sort through promising CVs sent in by job applicants. One problem: Because a high percentage of company employees were men, the algorithm favored men’s names, other things being equal.&nbsp;</p> <p>“They thought this would help [the] process, but of course what it does is train the AI [system] to be biased toward women, because they themselves have not hired that many women,” D’Ignazio observes.</p> <p>To Amazon’s credit, it did recognize the problem. Moreover, D’Ignazio notes, this kind of issue is a problem that can be addressed. “Some of the technologies can be reformed with a more participatory process, or better training data. … If we agree that’s a good goal, one path forward is to adjust your training set and include more people of color, more women.”</p> <p><strong>“Who’s on the team? Who had the idea? Who’s benefiting?” </strong></p> <p>Still, the question of who participates in data science is, as the authors write, “the elephant in the server room.” As of 2011, only 26 percent of all undergraduates receiving computer science degrees in the U.S. were women. That is not only a low figure, but actually a decline from past levels: In 1985, 37 percent of computer science graduates were women, the highest mark on record.</p> <p>As a result of the lack of diversity in the field, D’Ignazio and Klein believe, many data projects are radically limited in their ability to see all facets of the complex social situations they purport to measure.&nbsp;</p> <p>“We want to try to tune people in to these kinds of power relationships and why they matter deeply,” D’Ignazio says. “Who’s on the team? Who had the idea? Who’s benefiting from the project? Who’s potentially harmed by the project?”</p> <p>In all, D’Ignazio and Klein outline seven principles of data feminism, from examining and challenging power, to rethinking binary systems and hierarchies, and embracing pluralism. (Those statistics about gender and computer science graduates are limited, they note, by only using the “male” and “female” categories, thus excluding people who identify in different terms.)</p> <p>People interested in data feminism, the authors state, should also “value multiple forms of knowledge,” including firsthand knowledge that may lead us to question seemingly official data. Also, they should always consider the context in which data are generated, and “make labor visible” when it comes to data science. This last principle, the researchers note, speaks to the problem that even when women and other excluded people contribute to data projects, they often receive less credit for their work.</p> <p>For all the book’s critique of existing systems, programs, and practices, D’Ignazio and Klein are also careful to include examples of positive, successful efforts, such as the WomanStats project, which has grown and thrived over two decades.</p> <p>“For people who are data people but are new to feminism, we want to provide them with a very accessible introduction, and give them concepts and tools they can use in their practice,” D’Ignazio says. “We’re not imagining that people already have feminism in their toolkit. On the other hand, we are trying to speak to folks who are very tuned in to feminism or social justice principles, and highlight for them the ways data science is both problematic, but can be marshalled in the service of justice.”</p> Catherine D’Ignazio is the co-author of a new book, “Data Feminism,” published by MIT Press in March 2020. Image: Diana Levine and MIT PressData, Women, Faculty, Research, Books and authors, MIT Press, Diversity and inclusion, Ethics, Technology and society, Artificial intelligence, Machine learning, Computer science and technology, Urban studies and planning, School of Architecture and Planning “Doing machine learning the right way” Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable, and robust. Sat, 07 Mar 2020 23:59:59 -0500 Rob Matheson | MIT News Office <p>The work of MIT computer scientist Aleksander Madry is fueled by one core mission: “doing machine learning the right way.”</p> <p>Madry’s research centers largely on making machine learning — a type of artificial intelligence — more accurate, efficient, and robust against errors. In his classroom and beyond, he also worries about questions of ethical computing, as we approach an age where artificial intelligence will have great impact on many sectors of society.</p> <p>“I want society to truly embrace machine learning,” says Madry, a recently tenured professor in the Department of Electrical Engineering and Computer Science. “To do that, we need to figure out how to train models that people can use safely, reliably, and in a way that they understand.”</p> <p>Interestingly, his work with machine learning dates back only a couple of years, to shortly after he joined MIT in 2015. In that time, his research group has published several critical papers demonstrating that certain models can be easily tricked to produce inaccurate results — and showing how to make them more robust.</p> <p>In the end, he aims to make each model’s decisions more interpretable by humans, so researchers can peer inside to see where things went awry. At the same time, he wants to enable nonexperts to deploy the improved models in the real world for, say, helping diagnose disease or control driverless cars.</p> <p>“It’s not just about trying to crack open the machine-learning black box. I want to open it up, see how it works, and pack it back up, so people can use it without needing to understand what’s going on inside,” he says.</p> <p><strong>For the love of algorithms</strong></p> <p>Madry was born in Wroclaw, Poland, where he attended the University of Wroclaw as an undergraduate in the mid-2000s. While he harbored interest in computer science and physics, “I actually never thought I’d become a scientist,” he says.</p> <p>An avid video gamer, Madry initially enrolled in the computer science program with intentions of programming his own games. But in joining friends in a few classes in theoretical computer science and, in particular, theory of algorithms, he fell in love with the material. Algorithm theory aims to find efficient optimization procedures for solving computational problems, which requires tackling difficult mathematical questions. “I realized I enjoy thinking deeply about something and trying to figure it out,” says Madry, who wound up double-majoring in physics and computer science.</p> <p>When it came to delving deeper into algorithms in graduate school, he went to his first choice: MIT. Here, he worked under both Michel X. Goemans, who was a major figure in applied math and algorithm optimization, and Jonathan A. Kelner, who had just arrived to MIT as a junior faculty working in that field. For his PhD dissertation, Madry developed algorithms that solved a number of longstanding problems in graph algorithms, earning the 2011 George M. Sprowls Doctoral Dissertation Award for the best MIT doctoral thesis in computer science.</p> <p>After his PhD, Madry spent a year as a postdoc at Microsoft Research New England, before teaching for three years at the Swiss Federal Institute of Technology Lausanne — which Madry calls “the Swiss version of MIT.” But his alma mater kept calling him back: “MIT has the thrilling energy I was missing. It’s in my DNA.”</p> <p><strong>Getting adversarial</strong></p> <p>Shortly after joining MIT, Madry found himself swept up in a novel science: machine learning. In particular, he focused on understanding the re-emerging paradigm of deep learning. That’s an artificial-intelligence application that uses multiple computing layers to extract high-level features from raw input — such as using pixel-level data to classify images. MIT’s campus was, at the time, buzzing with new innovations in the domain.</p> <p>But that begged the question: Was machine learning all hype or solid science? “It seemed to work, but no one actually understood how and why,” Madry says.</p> <p>Answering that question set his group on a long journey, running experiment after experiment on deep-learning models to understand the underlying principles. A major milestone in this journey was an influential paper they published in 2018, developing a methodology for making machine-learning models more resistant to “adversarial examples.” Adversarial examples are slight perturbations to input data that are imperceptible to humans — such as changing the color of one pixel in an image — but cause a model to make inaccurate predictions. They illuminate a major shortcoming of existing machine-learning tools.</p> <p>Continuing this line of work, Madry’s group showed that the existence of these mysterious adversarial examples may contribute to how machine-learning models make decisions. In particular, models designed to differentiate images of, say, cats and dogs, make decisions based on features that do not align with how humans make classifications. Simply changing these features can make the model consistently misclassify cats as dogs, without changing anything in the image that’s really meaningful to humans.</p> <p>Results indicated some models — which may be used to, say, identify abnormalities in medical images or help autonomous cars identify objects in the road —&nbsp;aren’t exactly up to snuff. “People often think these models are superhuman, but they didn’t actually solve the classification problem we intend them to solve,” Madry says. “And their complete vulnerability to adversarial examples was a manifestation of that fact. That was an eye-opening finding.”</p> <p>That’s why Madry seeks to make machine-learning models more interpretable to humans. New models he’s developed show how much certain pixels in images the system is trained on can influence the system’s predictions. Researchers can then tweak the models to focus on pixels clusters more closely correlated with identifiable features — such as detecting an animal’s snout, ears, and tail. In the end, that will help make the models more humanlike —&nbsp;or “superhumanlike” —&nbsp;in their decisions. To further this work, Madry and his colleagues recently founded the <a href="">MIT Center for Deployable Machine Learning</a>, a collaborative research effort within the <a href="" target="_blank">MIT Quest for Intelligence</a> that is working toward building machine-learning tools ready for real-world deployment.&nbsp;</p> <p>“We want machine learning not just as a toy, but as something you can use in, say, an autonomous car, or health care. Right now, we don’t understand enough to have sufficient confidence in it for those critical applications,” Madry says.</p> <p><strong>Shaping education and policy</strong></p> <p>Madry views artificial intelligence and decision making (“AI+D” is one of the three <a href="">new academic units</a> in the Department of Electrical Engineering and Computer Science) as “the interface of computing that’s going to have the biggest impact on society.”</p> <p>In that regard, he makes sure to expose his students to the human aspect of computing. In part, that means considering consequences of what they’re building. Often, he says, students will be overly ambitious in creating new technologies, but they haven’t thought through potential ramifications on individuals and society. “Building something cool isn’t a good enough reason to build something,” Madry says. “It’s about thinking about not if we can build something, but if we should build something.”</p> <p>Madry has also been engaging in conversations about laws and policies to help regulate machine learning. A point of these discussions, he says, is to better understand the costs and benefits of unleashing machine-learning technologies on society.</p> <p>“Sometimes we overestimate the power of machine learning, thinking it will be our salvation. Sometimes we underestimate the cost it may have on society,” Madry says. “To do machine learning right, there’s still a lot still left to figure out.”</p> Alexander MadryImage: Ian MacLellanComputer science and technology, Algorithms, Artificial intelligence, Machine learning, Computer vision, Technology and society, Faculty, Profile, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering, MIT Schwarzman College of Computing, Quest for Intelligence Showing robots how to do your chores By observing humans, robots learn to perform complex tasks, such as setting a table. Thu, 05 Mar 2020 23:59:59 -0500 Rob Matheson | MIT News Office <p>Training interactive robots may one day be an easy job for everyone, even those without programming expertise. Roboticists are developing automated robots that can learn new tasks solely by observing humans. At home, you might someday show a domestic robot how to do routine chores. In the workplace, you could train robots like new employees, showing them how to perform many duties.</p> <p>Making progress on that vision, MIT researchers have designed a system that lets these types of robots learn complicated tasks that would otherwise stymie them with too many confusing rules. One such task is setting a dinner table under certain conditions. &nbsp;</p> <p>At its core, the researchers’ “Planning with Uncertain Specifications” (PUnS) system gives robots the humanlike planning ability to simultaneously weigh many ambiguous —&nbsp;and potentially contradictory —&nbsp;requirements to reach an end goal. In doing so, the system always chooses the most likely action to take, based on a “belief” about some probable specifications for the task it is supposed to perform.</p> <p>In their work, the researchers compiled a dataset with information about how eight objects — a mug, glass, spoon, fork, knife, dinner plate, small plate, and bowl — could be placed on a table in various configurations. A robotic arm first observed randomly selected human demonstrations of setting the table with the objects. Then, the researchers tasked the arm with automatically setting a table in a specific configuration, in real-world experiments and in simulation, based on what it had seen.</p> <p>To succeed, the robot had to weigh many possible placement orderings, even when items were purposely removed, stacked, or hidden. Normally, all of that would confuse robots too much. But the researchers’ robot made no mistakes over several real-world experiments, and only a handful of mistakes over tens of thousands of simulated test runs. &nbsp;</p> <p>“The vision is to put programming in the hands of domain experts, who can program robots through intuitive ways, rather than describing orders to an engineer to add to their code,” says first author Ankit Shah, a graduate student in the Department of Aeronautics and Astronautics (AeroAstro) and the Interactive Robotics Group, who emphasizes that their work is just one step in fulfilling that vision. “That way, robots won’t have to perform preprogrammed tasks anymore. Factory workers can teach a robot to do multiple complex assembly tasks. Domestic robots can learn how to stack cabinets, load the dishwasher, or set the table from people at home.”</p> <p>Joining Shah on the paper are AeroAstro and Interactive Robotics Group graduate student Shen Li and Interactive Robotics Group leader Julie Shah, an associate professor in AeroAstro and the Computer Science and Artificial Intelligence Laboratory.</p> <div class="cms-placeholder-content-video"></div> <p><strong>Bots hedging bets</strong></p> <p>Robots are fine planners in tasks with clear “specifications,” which help describe the task the robot needs to fulfill, considering its actions, environment, and end goal. Learning to set a table by observing demonstrations, is full of uncertain specifications. Items must be placed in certain spots, depending on the menu and where guests are seated, and in certain orders, depending on an item’s immediate availability or social conventions. Present approaches to planning are not capable of dealing with such uncertain specifications.</p> <p>A popular approach to planning is “reinforcement learning,” a trial-and-error machine-learning technique that rewards and penalizes them for actions as they work to complete a task. But for tasks with uncertain specifications, it’s difficult to define clear rewards and penalties. In short, robots never fully learn right from wrong.</p> <p>The researchers’ system, called PUnS (for Planning with Uncertain Specifications), enables a robot to hold a “belief” over a range of possible specifications. The belief itself can then be used to dish out rewards and penalties. “The robot is essentially hedging its bets in terms of what’s intended in a task, and takes actions that satisfy its belief, instead of us giving it a clear specification,” Ankit Shah says.</p> <p>The system is built on “linear temporal logic” (LTL), an expressive language that enables robotic reasoning about current and future outcomes. The researchers defined templates in LTL that model various time-based conditions, such as what must happen now, must eventually happen, and must happen until something else occurs. The robot’s observations of 30 human demonstrations for setting the table yielded a probability distribution over 25 different LTL formulas. Each formula encoded a slightly different preference — or specification — for setting the table. That probability distribution becomes its belief.</p> <p>“Each formula encodes something different, but when the robot considers various combinations of all the templates, and tries to satisfy everything together, it ends up doing the right thing eventually,” Ankit Shah says.</p> <p><strong>Following criteria</strong></p> <p>The researchers also developed several criteria that guide the robot toward satisfying the entire belief over those candidate formulas. One, for instance, satisfies the most likely formula, which discards everything else apart from the template with the highest probability. Others satisfy the largest number of unique formulas, without considering their overall probability, or they satisfy several formulas that represent highest total probability. Another simply minimizes error, so the system ignores formulas with high probability of failure.</p> <p>Designers can choose any one of the four criteria to preset before training and testing. Each has its own tradeoff between flexibility and risk aversion. The choice of criteria depends entirely on the task. In safety critical situations, for instance, a designer may choose to limit possibility of failure. But where consequences of failure are not as severe, designers can choose to give robots greater flexibility to try different approaches.</p> <p>With the criteria in place, the researchers developed an algorithm to convert the robot’s belief — the probability distribution pointing to the desired formula — into an equivalent reinforcement learning problem. This model will ping the robot with a reward or penalty for an action it takes, based on the specification it’s decided to follow.</p> <p>In simulations asking the robot to set the table in different configurations, it only made six mistakes out of 20,000 tries. In real-world demonstrations, it showed behavior similar to how a human would perform the task. If an item wasn’t initially visible, for instance, the robot would finish setting the rest of the table without the item. Then, when the fork was revealed, it would set the fork in the proper place. “That’s where flexibility is very important,” Ankit Shah says. “Otherwise it would get stuck when it expects to place a fork and not finish the rest of table setup.”</p> <p>Next, the researchers hope to modify the system to help robots change their behavior based on verbal instructions, corrections, or a user’s assessment of the robot’s performance. “Say a person demonstrates to a robot how to set a table at only one spot. The person may say, ‘do the same thing for all other spots,’ or, ‘place the knife before the fork here instead,’” Ankit Shah says. “We want to develop methods for the system to naturally adapt to handle those verbal commands, without needing additional demonstrations.”&nbsp;&nbsp;</p> Roboticists are developing automated robots that can learn new tasks solely by observing humans. At home, you might someday show a domestic robot how to do routine chores.Image: Christine Daniloff, MITResearch, Computer science and technology, Algorithms, Artificial intelligence, Machine learning, Robots, Robotics, Assistive technology, Aeronautical and astronautical engineering, Computer Science and Artificial Intelligence Laboratory (CSAIL), School of Engineering Novel method for easier scaling of quantum devices System “recruits” defects that usually cause disruptions, using them to instead carry out quantum operations. Thu, 05 Mar 2020 23:59:59 -0500 Rob Matheson | MIT News Office <p>In an advance that may help researchers scale up quantum devices, an MIT team has developed a method to “recruit” neighboring quantum bits made of nanoscale defects in diamond, so that instead of causing disruptions they help carry out quantum operations.</p> <p>Quantum devices perform operations using quantum bits, called “qubits,” that can represent the two states corresponding to classic binary bits — a 0 or 1 — or a “quantum superposition” of both states simultaneously. The unique superposition state can enable quantum computers to solve problems that are practically impossible for classical computers, potentially spurring breakthroughs in biosensing, neuroimaging, machine learning, and other applications.</p> <p>One promising qubit candidate is a defect in diamond, called a nitrogen-vacancy (NV) center, which holds electrons that can be manipulated by light and microwaves. In response, the defect emits photons that can carry quantum information. Because of their solid-state environments, however, NV centers are always surrounded by many other unknown defects with different spin properties, called “spin defects.” When the measurable NV-center qubit interacts with those spin defects, the qubit loses its coherent quantum state — “decoheres”—&nbsp;and operations fall apart. Traditional solutions try to identify these disrupting defects to protect the qubit from them.</p> <p>In a paper published Feb. 25 in <em>Physical Letters Review</em>, the researchers describe a method that uses an NV center to probe its environment and uncover the existence of several nearby spin defects. Then, the researchers can pinpoint the defects’ locations and control them to achieve a coherent quantum state — essentially leveraging them as additional qubits.</p> <p>In experiments, the team generated and detected quantum coherence among three electronic spins — scaling up the size of the quantum system from a single qubit (the NV center) to three qubits (adding two nearby spin defects). The findings demonstrate a step forward in scaling up quantum devices using NV centers, the researchers say. &nbsp;</p> <p>“You always have unknown spin defects in the environment that interact with an NV center. We say, ‘Let’s not ignore these spin defects, which [if left alone] could cause faster decoherence. Let’s learn about them, characterize their spins, learn to control them, and ‘recruit’ them to be part of the quantum system,’” says the lead co-author Won Kyu Calvin Sun, a graduate student in the Department of Nuclear Science and Engineering and a member of the Quantum Engineering group. “Then, instead of using a single NV center [or just] one qubit, we can then use two, three, or four qubits.”</p> <p>Joining Sun on the paper are lead author Alexandre Cooper ’16 of Caltech; Jean-Christophe Jaskula, a research scientist in the MIT Research Laboratory of Electronics (RLE) and member of the Quantum Engineering group at MIT; and Paola Cappellaro, a professor in the Department of Nuclear Science and Engineering, a member of RLE, and head of the Quantum Engineering group at MIT.</p> <p><strong>Characterizing defects</strong></p> <p>NV centers occur where carbon atoms in two adjacent places in a diamond’s lattice structure are missing — one atom is replaced by a nitrogen atom, and the other space is an empty “vacancy.” The NV center essentially functions as an atom, with a nucleus and surrounding electrons that are extremely sensitive to tiny variations in surrounding electrical, magnetic, and optical fields. Sweeping microwaves across the center, for instance, makes it change, and thus control, the spin states of the nucleus and electrons.</p> <p>Spins are measured using a type of magnetic resonance spectroscopy. This method plots the frequencies of electron and nucleus spins in megahertz as a “resonance spectrum” that can dip and spike, like a heart monitor. Spins of an NV center under certain conditions are well-known. But the surrounding spin defects are unknown and difficult to characterize.</p> <p>In their work, the researchers identified, located, and controlled two electron-nuclear spin defects near an NV center. They first sent microwave pulses at specific frequencies to control the NV center. Simultaneously, they pulse another microwave that probes the surrounding environment for other spins. They then observed the resonance spectrum of the spin defects interacting with the NV center.</p> <p>The spectrum dipped in several spots when the probing pulse interacted with nearby electron-nuclear spins, indicating their presence. The researchers then swept a magnetic field across the area at different orientations. For each orientation, the defect would “spin” at different energies, causing different dips in the spectrum. Basically, this allowed them to measure each defect’s spin in relation to each magnetic orientation. They then plugged the energy measurements into a model equation with unknown parameters. This equation is used to describe the quantum interactions of an electron-nuclear spin defect under a magnetic field. Then, they could solve the equation to successfully characterize each defect.</p> <p><strong>Locating and controlling</strong></p> <p>After characterizing the defects, the next step was to characterize the interaction between the defects and the NV, which would simultaneously pinpoint their locations. To do so, they again swept the magnetic field at different orientations, but this time looked for changes in energies describing the interactions between the two defects and the NV center. The stronger the interaction, the closer they were to one another. They then used those interaction strengths to determine where the defects were located, in relation to the NV center and to each other. That generated a good map of the locations of all three defects in the diamond.</p> <p>Characterizing the defects and their interaction with the NV center allow for full control, which involves a few more steps to demonstrate. First, they pump the NV center and surrounding environment with a sequence of pulses of green light and microwaves that help put the three qubits in a well-known quantum state. Then, they use another sequence of pulses that ideally entangles the three qubits briefly, and then disentangles them, which enables them to detect the three-spin coherence of the qubits.</p> <p>The researchers verified the three-spin coherence by measuring a major spike in the resonance spectrum. The measurement of the spike recorded was essentially the sum of the frequencies of the three qubits. If the three qubits for instance had little or no entanglement, there would have been four separate spikes of smaller height.</p> <p>“We come into a black box [environment with each NV center]. But when we probe the NV environment, we start seeing dips and wonder which types of spins give us those dips. Once we [figure out] the spin of the unknown defects, and their interactions with the NV center, we can start controlling their coherence,” Sun says. “Then, we have full universal control of our quantum system.”</p> <p>Next, the researchers hope to better understand other environmental noise surrounding qubits. That will help them develop more robust error-correcting codes for quantum circuits. Furthermore, because on average the process of NV center creation in diamond creates numerous other spin defects, the researchers say they could potentially scale up the system to control even more qubits. “It gets more complex with scale. But if we can start finding NV centers with more resonance spikes, you can imagine starting to control larger and larger quantum systems,” Sun says.</p> An MIT team found a way to “recruit” normally disruptive quantum bits (qubits) in diamond to, instead, help carry out quantum operations. This approach could be used to help scale up quantum computing systems. Image: Christine Daniloff, MITResearch, Computer science and technology, Quantum computing, Nuclear science and engineering, Nanoscience and nanotechnology, Sensors, Research Laboratory of Electronics, Materials Science and Engineering, Physics, School of Engineering 3 Questions: Joe Steinmeyer on guiding students into the world of STEM Since 2009, Steinmeyer has taught more than 400 students in the MITES, MOSTEC, SEED Academy, and E2 programs. Wed, 04 Mar 2020 12:30:01 -0500 Dora P. Gonzalez | Office of Engineering Outreach Programs <p><em>Joe Steinmeyer is a principal lecturer in the Department of Electrical Engineering and Computer Science (EECS) at MIT. His work includes the study of the intersection of biology and neuroscience with EECS, focusing on automation and control; and more recently, research in instrumentation and on novel ways to improve student learning. Steinmeyer&nbsp;SM ’10, PhD ’14 joined the Office of Engineering Outreach Programs (OEOP) instructional staff in 2009 and since then has taught more than 400 students in the Minority Introduction to Engineering and Science (MITES), MIT Online Science, Technology, and Engineering Community (MOSTEC), Saturday Engineering Enrichment and Discovery</em> (<em>SEED) Academy, and E2 programs. He is from Pittsburgh, Pennsylvania, and holds a bachelor’s degree in EECS from the University of Michigan in addition to his MIT degrees in EECS.</em></p> <p><strong>Q:</strong> What inspired you to become an OEOP instructor, and what keeps you coming back?</p> <p><strong>A: </strong>Coming out of undergrad, I was choosing between teaching as a career and engineering. I applied to PhD programs, but I also applied to Teach for America and almost went down that road. I got in MIT for grad school and decided to do research, but I wanted to keep teaching. The year after I got to MIT, around 2009, they were looking for an electronics instructor for MITES, and I was really excited because I have always liked the OEOP’s mission. Boston abounds with teaching opportunities, but few have a mission like the OEOP.</p> <p>I also liked that I could teach concepts I liked, so that’s how I got involved.</p> <p>Since I became a lecturer at MIT I’ve done more education-focused research, including some papers on the MITES curriculum, and devices we are using to teach EECS. For the past couple of years, I’ve also been trying to develop ways to analyze what students are doing in hardware when we are working from different locations, like with MOSTEC. You can analyze students’ programming capabilities through the internet, but how do you actually help them or give them a similar level of guidance in debugging a circuit, which is decoupled from a computer, when you’re not looking over their shoulder like you could in MITES and SEED? That has been an ongoing research project for me.</p> <p>I stay engaged because I like the mission of preparing students to be in a good position for college. OEOP programs are unique in that way, and there is also a lot of freedom with what we can teach students. It’s fun to teach them electronics because there is no one way to do programming, it’s an evolving field.</p> <p>I believe students benefit from a programming-focused curriculum, because that is one of the great have/have-not situations in education today. The schools with more resources will have programming curricula, where schools with less resources would not.</p> <p><strong>Q:</strong> How do you help students gain confidence to pursue a career in STEM?</p> <p><strong>A:</strong> First by having sort of a judgement-free zone. Every student comes in with different background experiences, and I’ve learned to adjust curricula for the individual person. When I first started as an instructor, I had this vision that everyone would have to do the same kind of project. But a student that comes in with no experience may not end up moving as far along as someone who came in with lots of previous experience, so having a rough idea of what you want everyone to do, and tailoring that for people, works best.</p> <p>I am also a big fan of letting students develop projects that they come up with, so they have a vested interest in their work. I see computation as an essential skill in every modern STEM field. Programming is used in every engineering field now, which also allows students to apply EECS concepts to something they already are interested in or care about. A couple of years ago we had a student who was really into dance, so we did a dance-focused project. Other students are interested in medical-leaning applications. We also do a lot of traditional EECS-themed projects like games, because those can be done in a short period of time.</p> <p>STEM education for those who want to self-learn can be extremely daunting and scary. If you go on any of the common forums where people can learn how to program, people can be very harsh and mean, and a student who goes on for the first time can feel discouraged and think programming isn’t for them. So I let students learn about the environment, but also try to ‘bumper bowl’ or guide the experience a little bit.</p> <p><strong>Q:</strong> What is the most challenging part of the OEOP instructor experience? And the most rewarding?</p> <p><strong>A:</strong> The vast differences in educational backgrounds of the students is a challenge, but it’s not one that I don’t like; I find that actually rewarding. It requires you to find what’s the right mix of challenging students but not breaking them down.</p> <p>The most rewarding part is seeing students a few years down the line, where they end up or what they are doing, it’s really fulfilling. I have been an instructor for MITES for 10 years so I have a couple early MITES kids who are in PhD programs now. It has been really nice to see students’ journeys.</p> <p>I had a student who came from East LA [Los Angeles, California], who was extremely smart, but had a lot of confidence issues. She worked really hard in the MITES electronics class, and at the time, they had to do individual presentations of their work, and she was really nervous about explaining her project but she did really well. She went through the college application process and got into Harvard and Brown. After visiting Brown she decided that was her college, and throughout her undergrad years she was a teaching assistant for MITES. She invited me to go to her graduation at Brown and it was a really fulfilling moment for me. It was neat to see her evolve into this really confident young woman. She then got into Harvard for her PhD and is doing very interesting hearing/ear research. Stories like this motivate me.</p> <p>Programming moves so fast and transforms so fast that there are no more books to learn from, it’s sort of like going out on to the web and scraping information from people. I find it rewarding to see how students go from not knowing that they can teach themselves from the internet, to learning how to look up information that’s out there, loosely organized, and use it to solve a problem with their final projects. It’s also nice to see how much students mature once they are in college. At the end they are a well-seasoned person who can have their pick of what they want to do with life, that’s my goal. I don’t want to see anyone get forced into a certain career path, it doesn’t have to be EECS, if they can get to a spot and they can make a choice, and they’re not forced into it, it’s success.</p> Joe Steinmeyer and SEED Academy students Lea Grohmann (left), Daysia Charles (center), and Yenifer Lemus (right) prepare for their final electronics presentations.Photo: Gretchen ErtlElectrical engineering and computer science (EECS), 3 Questions, Diversity and inclusion, Technology and society, Alumni/ae, Office of Engineering Outreach Program (OEOP), Computer science and technology, Faculty Integrating electronics onto physical prototypes In place of flat “breadboards,” 3D-printed CurveBoards enable easier testing of circuit design on electronics products. Tue, 03 Mar 2020 23:59:59 -0500 Rob Matheson | MIT News Office <p>MIT researchers have invented a way to integrate “breadboards” — flat platforms widely used for electronics prototyping — directly onto physical products. The aim is to provide a faster, easier way to test circuit functions and user interactions with products such as smart devices and flexible electronics.</p> <p>Breadboards are rectangular boards with arrays of pinholes drilled into the surface. Many of the holes have metal connections and contact points between them. Engineers can plug components of electronic systems — from basic circuits to full computer processors — into the pinholes where they want them to connect. Then, they can rapidly test, rearrange, and retest the components as needed.</p> <p>But breadboards have remained that same shape for decades. For that reason, it’s difficult to test how the electronics will look and feel on, say, wearables and various smart devices. Generally, people will first test circuits on traditional breadboards, then slap them onto a product prototype. If the circuit needs to be modified, it’s back to the breadboard for testing, and so on.</p> <p>In a paper being presented at CHI (Conference on Human Factors in Computing Systems), the researchers describe “CurveBoards,” 3D-printed objects with the structure and function of a breadboard integrated onto their surfaces. Custom software automatically designs the objects, complete with distributed pinholes that can be filled with conductive silicone to test electronics. The end products are accurate representations of the real thing, but with breadboard surfaces.</p> <p>CurveBoards “preserve an object’s look and feel,” the researchers write in their paper, while enabling designers to try out component configurations and test interactive scenarios during prototyping iterations. In their work, the researchers printed CurveBoards for smart bracelets and watches, Frisbees, helmets, headphones, a teapot, and a flexible, wearable e-reader.</p> <p>“On breadboards, you prototype the function of a circuit. But you don’t have context of its form — how the electronics will be used in a real-world prototype environment,” says first author Junyi Zhu, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “Our idea is to fill this gap, and merge form and function testing in very early stage of prototyping an object. … &nbsp;CurveBoards essentially add an additional axis to the existing [three-dimensional] XYZ axes of the object — the ‘function’ axis.”</p> <p>Joining Zhu on the paper are CSAIL graduate students Lotta-Gili Blumberg, Martin Nisser, and Ethan Levi Carlson; Department of Electrical Engineering and Computer Science (EECS) undergraduate students Jessica Ayeley Quaye and Xin Wen; former EECS undergraduate students Yunyi Zhu and Kevin Shum; and Stefanie Mueller, the X-Window Consortium Career Development Assistant Professor in EECS.</p> <div class="cms-placeholder-content-video"></div> <p><strong>Custom software and hardware</strong></p> <p>A core component of the CurveBoard is custom design-editing software. Users import a 3D model of an object. Then, they select the command “generate pinholes,” and the software automatically maps all pinholes uniformly across the object. Users then choose automatic or manual layouts for connectivity channels. The automatic option lets users explore a different layout of connections across all pinholes with the click of a button. For manual layouts, interactive tools can be used to select groups of pinholes and indicate the type of connection between them. The final design is exported to a file for 3D printing.</p> <p>When a 3D object is uploaded, the software essentially forces its shape into a “quadmesh” — where the object is represented as a bunch of small squares, each with individual parameters. In doing so, it creates a fixed spacing between the squares. Pinholes — which are cones, with the wide end on the surface and tapering down —&nbsp;will be placed at each point where the corners of the squares touch. For channel layouts, some geometric techniques ensure the chosen channels will connect the desired electrical components without crossing over one another.</p> <p>In their work, the researchers 3D printed objects using a flexible, durable, nonconductive silicone. To provide connectivity channels, they created a custom conductive silicone that can be syringed into the pinholes and then flows through the channels after printing. The silicone is a mixture of a silicone materials designed to have minimal electricity resistance, allowing various types electronics to function.</p> <p>To validate the CurveBoards, the researchers printed a variety of smart products. Headphones, for instance, came equipped with menu controls for speakers and music-streaming capabilities. An interactive bracelet included a digital display, LED, and photoresistor for heart-rate monitoring, and a step-counting sensor. A teapot included a small camera to track the tea’s color, as well as colored lights on the handle to indicate hot and cold areas. They also printed a wearable e-book reader with a flexible display.</p> <p><strong>Better, faster prototyping</strong></p> <p>In a user study, the team investigated the benefits of CurveBoards prototyping. They split six participants with varying prototyping experience into two sections: One used traditional breadboards and a 3D-printed object, and the other used only a CurveBoard of the object. Both sections designed the same prototype but switched back and forth between sections after completing designated tasks. In the end, five of six of the participants preferred prototyping with the CurveBoard. Feedback indicated the CurveBoards were overall faster and easier to work with.</p> <p>But CurveBoards are not designed to replace breadboards, the researchers say. Instead, they’d work particularly well as a so-called “midfidelity” step in the prototyping timeline, meaning between initial breadboard testing and the final product. “People love breadboards, and there are cases where they’re fine to use,” Zhu says. “This is for when you have an idea of the final object and want to see, say, how people interact with the product. It’s easier to have a CurveBoard instead of circuits stacked on top of a physical object.”</p> <p>Next, the researchers hope to design general templates of common objects, such as hats and bracelets. Right now, a new CurveBoard must built for each new object. Ready-made templates, however, would let designers quickly experiment with basic circuits and user interaction, before designing their specific CurveBoard.</p> <p>Additionally, the researchers want to move some early-stage prototyping steps entirely to the software side. The idea is that people can design and test circuits — and possibly user interaction — entirely on the 3D model generated by the software. After many iterations, they can 3D print a more finalized CurveBoard. “That way you’ll know exactly how it’ll work in the real world, enabling fast prototyping,” Zhu says. “That would be a more ‘high-fidelity’ step for prototyping.”</p> CurveBoards are 3D breadboards — which are commonly used to prototype circuits — that can be designed by custom software, 3D printed, and directly integrated into the surface of physical objects, such as smart watches, bracelets, helmets, headphones, and even flexible electronics. CurveBoards can give designers an additional prototyping technique to better evaluate how circuits will look and feel on physical products that users interact with.Image: Dishita Turakhia and Junyi ZhuResearch, Computer science and technology, 3-D printing, Design, Manufacturing, electronics, Computer graphics, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering QS World University Rankings rates MIT No. 1 in 12 subjects for 2020 Institute ranks second in five subject areas. Tue, 03 Mar 2020 19:01:01 -0500 MIT News Office <p>MIT has been honored with 12 No. 1 subject rankings in the QS World University Rankings for 2020.</p> <p>The Institute received a No. 1 ranking in the following QS subject areas: Architecture/Built Environment; Chemistry; Computer Science and Information Systems; Chemical Engineering; Civil and Structural Engineering; Electrical and Electronic Engineering; Mechanical, Aeronautical and Manufacturing Engineering; Linguistics; Materials Science; Mathematics; Physics and Astronomy; and Statistics and Operational Research.</p> <p>MIT also placed second in five subject areas: Accounting and Finance; Biological Sciences; Earth and Marine Sciences; Economics and Econometrics; and Environmental Sciences.</p> <p>Quacquarelli Symonds Limited subject rankings, published annually, are designed to help prospective students find the leading schools in their field of interest. Rankings are based on research quality and accomplishments, academic reputation, and graduate employment.</p> <p>MIT has been ranked as the No. 1 university in the world by QS World University Rankings for eight straight years.</p> Afternoon light streams into MIT’s Lobby 7.Image: Jake BelcherRankings, Computer science and technology, Linguistics, Chemical engineering, Civil and environmental engineering, Mechanical engineering, Chemistry, Materials science, Mathematics, Physics, Economics, EAPS, Business and management, Accounting, Finance, DMSE, School of Engineering, School of Science, School of Architecture and Planning, Sloan School of Management, School of Humanities Arts and Social Sciences, Electrical Engineering & Computer Science (eecs), Architecture, Biology, Aeronautical and astronautical engineering Through ReACT, refugee learners become “CEOs of their own lives” Computer and data science graduates learned to forge their own destinies while gaining employable skills. Wed, 26 Feb 2020 14:05:01 -0500 Duyen Nguyen | MIT Open Learning <p>For graduates of the MIT Refugee Action Hub’s (ReACT) computer and data science (CDS) certificate program, commencement means more than the completion of courses, workshops, and internships — it establishes the students as pioneers of a new, empowering educational model.&nbsp;</p> <p>During a virtual commencement ceremony that streamed live on Jan. 28, Vice President for Open Learning Sanjay Sarma addressed the graduates as “the CEOs of [their] own lives,” highlighting their initiative and determination to overcome the challenges that communities in crisis face in accessing educational and professional opportunities.</p> <p><strong>New skills, new opportunities</strong></p> <p>Selected from over 1,000 applicants from 42 countries, the 28 members of this year’s class are the second cohort to complete the yearlong program. Many of the graduates tuned in to the celebration from Amman, Jordan, where the CDS certificate program first launched in 2018. Others joined from other countries in the Middle East, Europe, and Africa. An accomplished, highly motivated group, this year’s graduates earned internships at multinational companies and global humanitarian agencies such as Hikma, Samsung, and <span>the United Nations Children's Fund (UNICEF)</span>.&nbsp;</p> <p>All are familiar with “the hunger for knowledge” that motivates displaced learners around the world to overcome adversity, says Admir Masic,&nbsp;ReACT’s faculty founder and the Esther and Harold E. Edgerton Career Development Assistant Professor in the MIT Department of Civil and Environmental Engineering. ReACT was inspired by Masic’s own journey as a teenage refugee from Bosnia.</p> <p>Mohammad Hizzani, a member of the graduating class, credits ReACT with giving him the resources to realize his potential. “ReACT gave me not just the knowledge, it gave me access to opportunities I never dreamed of,” he shared at the ceremony.</p> <p>As an intern with UNICEF, Hizzani drew on the knowledge he gained from two <em>MITx</em> computer programming and data science courses to write codes to analyze data gathered by the organization’s teams. "ReACT gave me confidence, it gave me hope — [it was] where people finally started to appreciate my intelligence, my skills, and my hard work.” Hizzani is currently a PhD student in electrical and computer engineering at the University of Lisbon, Portugal.&nbsp;</p> <p><strong>Preparing for an agile future</strong></p> <p>Since its founding in May 2017, ReACT has created two free learning programs for refugees, delivered wherever they are in the world: the CDS program and a track in the <em>MITx</em> MicroMasters program in data, economics, and development policy. Combining online and in-person learning with paid professional internships, both programs give students innovative education-to-employment paths.&nbsp;</p> <p>For many learners, the traditional four-year model of higher education is out of reach. For others, it’s a struggle&nbsp;to keep up with the ever-evolving future of work. In his speech to the graduates, Sarma stressed that, “The way we work is changing very rapidly. No longer is it going to be enough to get educated for four years and then be ready for life. The fact is you have to be educated, and you have to educate yourself, every day of your life.” It seems clear that this cohort is up to the challenge: Hala Fadel MBA ’01, ReACT co-founder and a member of the MIT Corporation, noted that these learners are “driven to achieve,” despite early and sometimes ongoing hardship.</p> <p>ReACT joined MIT Open Learning in June 2018. Since then, it has become a touchstone of the organization’s larger vision around agile education: a model of learning that empowers learners with flexible options. As plans for a third CDS class in Jordan progress, the ReACT programs’ success signals a future in which more educational and professional pathways will be available for displaced learners and learners from under-resourced parts of the world.&nbsp;</p> <p>As Masic remarked, “We live in a new world where education has no borders.” With their commitment to excellence despite all odds, it seems clear that the ReACT graduates’ potential for future success is equally boundless.</p> The ReACT computer and data sciences cohort gathered with MIT Open Learning faculty and staff in Amman, Jordan, in 2019. Photo: MIT Open LearningOffice of Open Learning, Civil and environmental engineering, MITx, Massive open online courses (MOOCs), Education, teaching, academics, Computer science and technology, Classes and programs, Global Protecting sensitive metadata so it can’t be used for surveillance System ensures hackers eavesdropping on large networks can’t find out who’s communicating and when they’re doing so. Wed, 26 Feb 2020 00:00:00 -0500 Rob Matheson | MIT News Office <p>MIT researchers have designed a scalable system that secures the metadata — such as who’s corresponding and when — of millions of users in communications networks, to help protect the information against possible state-level surveillance.</p> <p>Data encryption schemes that protect the content of online communications are prevalent today. Apps like WhatsApp, for instance, use “end-to-end encryption” (E2EE), a scheme that ensures third-party eavesdroppers can’t read messages sent by end users.</p> <p>But most of those schemes overlook metadata, which contains information about who’s talking, when the messages are sent, the size of message, and other information. Many times, that’s all a government or other hacker needs to know to track an individual. This can be especially dangerous for, say, a government whistleblower or people living in oppressive regimes talking with journalists.</p> <p>Systems that fully protect user metadata with cryptographic privacy are complex, and they suffer scalability and speed issues that have so far limited their practicality. Some methods can operate quickly but provide much weaker security. In a paper being presented at the USENIX Symposium on Networked Systems Design and Implementation, the MIT researchers describe “XRD” (for Crossroads), a metadata-protection scheme that can handle cryptographic communications from millions of users in minutes, whereas traditional methods with the same level of security would take hours to send everyone’s messages.</p> <p>“There is a huge lack in protection for metadata, which is sometimes very sensitive. The fact that I’m sending someone a message at all is not protected by encryption,” says first author Albert Kwon PhD ’19, a recent graduate from the Computer Science and Artificial Intelligence Laboratory (CSAIL). “Encryption can protect content well. But how can we fully protect users from metadata leaks that a state-level adversary can leverage?”</p> <p>Joining Kwon on the paper are David Lu, an undergraduate in the Department of Electrical Engineering and Computer Science; and Srinivas Devadas, the Edwin Sibley Webster Professor of Electrical Engineering and Computer Science in CSAIL.</p> <p><strong>New spin on mix nets</strong></p> <p>Starting in 2013, disclosures of classified information by Edward Snowden revealed widespread global surveillance by the U.S. government. Although the mass collection of metadata by the National Security Agency was subsequently discontinued, in 2014 former director of the NSA and the Central Intelligence Agency Michael Hayden explained that the government can often rely solely on metadata to find the information it’s seeking. As it happens, this is right around the time Kwon started his PhD studies.</p> <p>“That was like a punch to the cryptography and security communities,” Kwon says. “That meant encryption wasn’t really doing anything to stop spying in that regard.”</p> <p>Kwon spent most of his PhD program focusing on metadata privacy. With XRD, Kwon says he “put a new spin” on a traditional E2EE metadata-protecting scheme, called “mix nets,” which was invented decades ago but suffers from scalability issues.</p> <p>Mix nets use chains of servers, known as mixes, and public-private key encryption. The first server receives encrypted messages from many users and decrypts a single layer of encryption from each message. Then, it shuffles the messages in random order and transmits them to the next server, which does the same thing, and so on down the chain. The last server decrypts the final encryption layer and sends the message to the target receiver.</p> <p>Servers only know the identities of the immediate source (the previous server) and immediate destination (the next server). Basically, the shuffling and limited identity information breaks the link between source and destination users, making it very difficult for eavesdroppers to get that information. As long as one server in the chain is “honest”— meaning it follows protocol — metadata is almost always safe.</p> <p>However, “active attacks” can occur, in which a malicious server in a mix net tampers with the messages to reveal user sources and destinations. In short, the malicious server can drop messages or modify sending times to create communications patterns that reveal direct links between users.</p> <p>Some methods add cryptographic proofs between servers to ensure there’s been no tampering. These rely on public key cryptography, which is secure, but it’s also slow and limits scaling. For XRD, the researchers invented a far more efficient version of the cryptographic proofs, called “aggregate hybrid shuffle,” that guarantees servers are receiving and shuffling message correctly, to detect any malicious server activity.</p> <p>Each server has a secret private key and two shared public keys. Each server must know all the keys to decrypt and shuffle messages. Users encrypt messages in layers, using each server’s secret private key in its respective layer. When a server receives messages, it decrypts and shuffles them using one of the public keys combined with its own private key. Then, it uses the second public key to generate a proof confirming that it had, indeed, shuffled every message without dropping or manipulating any. All other servers in the chain use their secret private keys and the other servers’ public keys in a way that verifies this proof. If, at any point in the chain, a server doesn’t produce the proof or provides an incorrect proof, it’s immediately identified as malicious.</p> <p>This relies on a clever combination of the popular public key scheme with one called “authenticated encryption,” which uses only private keys but is very quick at generating and verifying the proofs. In this way, XRD achieves tight security from public key encryption while running quickly and efficiently.&nbsp;&nbsp;&nbsp;</p> <p>To further boost efficiency, they split the servers into multiple chains and divide their use among users. (This is another traditional technique they improved upon.) Using some statistical techniques, they estimate how many servers in each chain could be malicious, based on IP addresses and other information. From that, they calculate how many servers need to be in each chain to guarantee there’s at least one honest server.&nbsp; Then, they divide the users into groups that send duplicate messages to multiple, random chains, which further protects their privacy while speeding things up.</p> <p><strong>Getting to real-time</strong></p> <p>In computer simulations of activity from 2 million users sending messages on a network of 100 servers, XRD was able to get everyone’s messages through in about four minutes. Traditional systems using the same server and user numbers, and providing the same cryptographic security, took one to two hours.</p> <p>“This seems slow in terms of absolute speed in today’s communication world,” Kwon says. “But it’s important to keep in mind that the fastest systems right now [for metadata protection] take hours, whereas ours takes minutes.”</p> <p>Next, the researchers hope to make the network more robust to few users and in instances where servers go offline in the midst of operations, and to speed things up. “Four minutes is acceptable for sensitive messages and emails where two parties’ lives are in danger, but it’s not as natural as today’s internet,” Kwon says. “We want to get to the point where we’re sending metadata-protected messages in near real-time.”</p> In a new metadata-protecting scheme, users send encrypted messages to multiple chains of servers, with each chain mathematically guaranteed to have at least one hacker-free server. Each server decrypts and shuffles the messages in random order, before shooting them to the next server in line. Image: courtesy of the researchersResearch, Computer science and technology, Algorithms, Cyber security, Data, Technology and society, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering Bringing deep learning to life MIT duo uses music, videos, and real-world examples to teach students the foundations of artificial intelligence. Mon, 24 Feb 2020 14:10:01 -0500 Kim Martineau | MIT Quest for Intelligence <p>Gaby Ecanow loves listening to music, but never considered writing her own until taking <a href="" target="_blank">6.S191</a> (Introduction to Deep Learning). By her second class, the second-year MIT student had composed an original Irish folk song with the help of a recurrent neural network, and was considering how to adapt the model to create her own Louis the Child-inspired dance beats.</p> <p>“It was cool,” she says. “It didn’t sound at all like a machine had made it.”&nbsp;</p> <p>This year, 6.S191 kicked off as usual, with students spilling into the aisles of Stata Center’s Kirsch Auditorium during Independent Activities Period (IAP). But the opening lecture featured a twist: a&nbsp;<a href=";" target="_blank">recorded welcome</a>&nbsp;from former President Barack Obama. The video was quickly revealed to be an AI-generated fabrication, one of many twists that&nbsp;<a href="" target="_blank">Alexander Amini</a>&nbsp;’17 and&nbsp;<a href="" target="_blank">Ava Soleimany</a>&nbsp;’16 introduce throughout their for-credit course to make the equations and code come alive.&nbsp;</p> <div class="cms-placeholder-content-video"></div> <p>As&nbsp;hundreds of their peers look on, Amini and Soleimany take turns at the podium. If they appear at ease, it’s because they know the material cold; they designed the curriculum themselves, and have taught it for the past three years.&nbsp;The course covers the technical foundations of deep learning and its societal implications through lectures and software labs focused on real-world applications. On the final day, students compete for prizes by pitching their own ideas for research projects. In the weeks leading up to class, Amini and Soleimany spend hours updating the labs, refreshing their lectures, and honing their presentations.</p> <p>A branch of&nbsp;machine learning,&nbsp;deep learning harnesses massive data and algorithms&nbsp;modeled loosely on how the brain processes information to make predictions. The class has been credited with helping to spread machine-learning tools into research labs across MIT. That’s by design, says Amini, a graduate student in MIT’s&nbsp;<a href="">Department of Electrical Engineering and Computer Science</a>&nbsp;(EECS), and Soleimany, a graduate student at MIT and Harvard University.</p> <p>Both are using machine learning in their own research — Amini in engineering robots, and Soleimany in developing diagnostic tools for cancer — and they wanted to make sure the curriculum would prepare students to do the same. In addition to the lab on developing a music-generating AI,&nbsp;they offer labs on building a face-recognition model with convolutional neural networks and a bot that uses reinforcement learning to play the vintage Atari video game, Pong.&nbsp;After students master the basics, those taking the class for credit go on to create applications of their own.&nbsp;</p> <p>This year, 23 teams&nbsp;presented projects. Among the prize winners was Carmen&nbsp;Martin, a graduate student in the&nbsp;<a href="" target="_blank">Harvard-MIT Program in Health Sciences and Technology</a>&nbsp;(HST), who&nbsp;proposed using a type of neural net called a graph convolutional network to predict the spread of coronavirus. She combined several data streams: airline ticketing data to measure population fluxes, real-time confirmation of new infections, and a ranking of how well countries are equipped to prevent and respond to&nbsp;a pandemic.&nbsp;</p> <p>“The goal is to train the model to predict cases to guide national governments and the World Health Organization in their recommendations to limit new cases and save lives,” she says.</p> <p>A second prize winner, EECS graduate student Samuel Sledzieski, proposed building a model to predict protein interactions using only their amino acid sequences. Predicting protein behavior is key to designing drug targets, among other clinical applications, and Sledzieski wondered if deep learning could speed up the search for viable protein pairs.&nbsp;</p> <p>“There’s still work to be done, but I’m excited by how far I was able to get in three days,” he says. “Having easy-to-follow examples in TensorFlow and Keras helped me understand how to actually build and train these models myself.”&nbsp;He plans to continue the work in his current lab rotation with&nbsp;<a href="">Bonnie Berger</a>, the Simons Professor of Mathematics in EECS and the&nbsp;<a href="" target="_blank">Computer Science and Artificial Intelligence Laboratory</a>&nbsp;(CSAIL).&nbsp;</p> <p>Each year, students also hear about emerging deep-learning applications from companies sponsoring the course. David Cox, co-director of the&nbsp;<a href="">MIT-IBM Watson AI Lab</a>, covered neuro-symbolic AI, a hybrid approach that combines symbolic programs with deep learning’s expert pattern-matching ability. Alex Wiltschko, a senior researcher at Google Brain, spoke about using a network analysis tool to predict the scent of small molecules. Chuan Li, chief scientific officer at Lambda Labs, discussed neural rendering, a tool for reconstructing and generating graphics scenes. Animesh Garg, a senior researcher at NVIDIA, covered strategies for developing robots that perceive and act more human-like.</p> <p>With 350 students taking the&nbsp;live course each year, and&nbsp;more than a million people who have watched the lectures online, Amini and Soleimany have become prominent ambassadors for deep learning. Yet, it was tennis that first brought them together.&nbsp;</p> <p>Amini competed nationally as a high school student in Ireland&nbsp;and built an award-winning&nbsp;AI model to help amateur and pro tennis players improve their strokes; Soleimany was a two-time captain of the MIT women’s tennis team. They met on the court as undergraduates&nbsp;and discovered they shared a passion for machine learning.&nbsp;</p> <p>After finishing their undergraduate degrees, they decided to challenge themselves and fill what they saw as an increasing need at MIT for a foundational course in deep learning.&nbsp;6.S191 was launched in 2017 by two grad students, Nick Locascio and Harini Suresh, and Amini and Soleimany had a vision for transforming the course into something more. They created a series of software labs, introduced new cutting-edge topics like robust and ethical AI, and added content to appeal to a broad range of students, from computer scientists to aerospace engineers and MBAs.</p> <p>“Alexander and I are constantly brainstorming, and those discussions are key to how 6.S191 and some of our own collaborative research projects have developed,” says Soleimany.&nbsp;</p> <p>They cover one of those research collaborations&nbsp;in class. During the computer vision lab, students learn about algorithmic bias and how to test for and address racial and gender bias in face-recognition tools. The lab is based on an algorithm that Amini and Soleimany developed with their respective advisors,&nbsp;<a href="" target="_blank">Daniela Rus</a>, director of CSAIL, and&nbsp;<a href="" target="_blank">Sangeeta Bhatia</a>, the John J. and Dorothy Wilson Professor of HST and EECS. This year they also covered hot topics in robotics, including recent work of Amini’s on driverless cars.&nbsp;</p> <p>But they don’t plan to stop there.&nbsp;“We’re committed to making 6.S191 the best that it can be, each year we teach it,” says Amini “and that means moving the course forward as deep learning continues to evolve.”&nbsp;</p> A crash course in deep learning organized and taught by grad students Alexander Amini (right) and Ava Soleimany reaches more than 350 MIT students each year; more than a million other people have watched their lectures online over the past three years.Photo: Gretchen ErtlQuest for Intelligence, Independent Activities Period, Electrical engineering and computer science (EECS), Harvard-MIT Health Sciences and Technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT-IBM Watson AI Lab, School of Engineering, Artificial intelligence, Algorithms, Computer science and technology, Machine learning, Classes and programs, Faculty A human-machine collaboration to defend against cyberattacks PatternEx merges human and machine expertise to spot and respond to hacks. Fri, 21 Feb 2020 14:12:18 -0500 Zach Winn | MIT News Office <p>Being a cybersecurity analyst at a large company today is a bit like looking for a needle in a haystack — if that haystack were hurtling toward you at fiber optic speed.</p> <p>Every day, employees and customers generate loads of data that establish a normal set of behaviors. An attacker will also generate data while using any number of techniques to infiltrate the system; the goal is to find that “needle” and stop it before it does any damage.</p> <p>The data-heavy nature of that task lends itself well to the number-crunching prowess of machine learning, and an influx of AI-powered systems have indeed flooded the cybersecurity market over the years. But such systems can come with their own problems, namely a never-ending stream of false positives that can make them more of a time suck than a time saver for security analysts.</p> <p>MIT startup PatternEx starts with the assumption that algorithms can’t protect a system on their own. The company has developed a closed loop approach whereby machine-learning models flag possible attacks and human experts provide feedback. The feedback is then incorporated into the models, improving their ability to flag only the activity analysts care about in the future.</p> <p>“Most machine learning systems in cybersecurity have been doing anomaly detection,” says Kalyan Veeramachaneni, a co-founder of PatternEx and a principal research scientist at MIT. “The problem with that, first, is you need a baseline [of normal activity]. Also, the model is usually unsupervised, so it ends up showing a lot of alerts, and people end up shutting it down. The big difference is that PatternEx allows the analyst to inform the system and then it uses that feedback to filter out false positives.”</p> <p>The result is an increase in analyst productivity. When compared to a generic anomaly detection software program, PatternEx’s Virtual Analyst Platform successfully identified 10 times more threats through the same number of daily alerts, and its advantage persisted even when the generic system gave analysts five times more alerts per day.</p> <p>First deployed in 2016, today the company’s system is being used by security analysts at large companies in a variety of industries along with firms that offer cybersecurity as a service.</p> <p><strong>Merging human and machine approaches to cybersecurity</strong></p> <p>Veeramachaneni came to MIT in 2009 as a postdoc and now directs a research group in the Laboratory for Information and Decision Systems. His work at MIT primarily deals with big data science and machine learning, but he didn’t think deeply about applying those tools to cybersecurity until a brainstorming session with PatternEx co-founders Costas Bassias, Uday Veeramachaneni, and Vamsi Korrapati in 2013.</p> <p>Ignacio Arnaldo, who worked with Veeramachaneni as a postdoc at MIT between 2013 and 2015, joined the company shortly after. Veeramachaneni and Arnaldo knew from their time building tools for machine-learning researchers at MIT that a successful solution would need to seamlessly integrate machine learning with human expertise.</p> <p>“A lot of the problems people have with machine learning arise because the machine has to work side by side with the analyst,” Veeramachaneni says, noting that detected attacks still must be presented to humans in an understandable way for further investigation. “It can’t do everything by itself. Most systems, even for something as simple as giving out a loan, is augmentation, not machine learning just taking decisions away from humans.”</p> <p>The company’s first partnership was with a large online retailer, which allowed the founders to train their models to identify potentially malicious behavior using real-world data. One by one, they trained their algorithms to flag different types of attacks using sources like Wi-Fi access logs, authentication logs, and other user behavior in the network.</p> <p>The early models worked best in retail, but Veeramachaneni knew how much businesses in other industries were struggling to apply machine learning in their operations from his many conversations with company executives at MIT (a subject PatternEx recently published <a href="">a paper</a> on).</p> <p>“MIT has done an incredible job since I got here 10 years ago bringing industry through the doors,” Veeramachaneni says. He estimates that in the past six years as a member of MIT’s Industrial Liaison Program he’s had 200 meetings with members of the private sector to talk about the problems they’re facing. He has also used those conversations to make sure his lab’s research is addressing relevant problems.</p> <p>In addition to enterprise customers, the company began offering its platform to security service providers and teams that specialize in hunting for undetected cyberattacks in networks.</p> <p>Today analysts can build machine learning models through PatternEx’s platform without writing a line of code, lowering the bar for people to use machine learning as part of a larger trend in the industry toward what Veeramachaneni calls the democratization of AI.</p> <p>“There’s not enough time in cybersecurity; it can’t take hours or even days to understand why an attack is happening,” Veeramachaneni says. “That’s why getting the analyst the ability to build and tweak machine learning models &nbsp;is the most critical aspect of our system.”</p> <p><strong>Giving security analysts an army</strong></p> <p>PatternEx’s Virtual Analyst Platform is designed to make security analysts feel like they have an army of assistants combing through data logs and presenting them with the most suspicious behavior on their network.</p> <p>The platform uses machine learning models to go through more than 50 streams of data and identify suspicious behavior. It then presents that information to the analyst for feedback, along with charts and other data visualizations that help the analyst decide how to proceed. After the analyst determines whether or not the behavior is an attack, that feedback is incorporated back into the models, which are updated across PatternEx’s entire customer base.</p> <p>“Before machine learning, someone would catch an attack, probably a little late, they might name it, and then they’ll announce it, and all the other companies will call and find out about it and go in and check their data,” Veeramachaneni says. “For us, if there’s an attack, we take that data, and because we have multiple customers, we have to transfer that in real time to other customer’s data to see if it’s happening with them too. We do that very efficiently on a daily basis.”</p> <p>The moment the system is up and running with new customers, it is able to identify 40 different types of cyberattacks using 170 different prepackaged machine learning models. Arnaldo notes that as the company works to grow those figures, customers are also adding to PatternEx’s model base by building solutions on the platform that address specific threats they’re facing.</p> <p>Even if customers aren’t building their own models on the platform, they can deploy PatternEx’s system out of the box, without any machine learning expertise, and watch it get smarter automatically.</p> <p>By providing that flexibility, PatternEx is bringing the latest tools in artificial intelligence to the people who understand their industries most intimately. It all goes back to the company’s founding principle of empowering humans with artificial intelligence instead of replacing them.</p> <p>“The target users of the system are not skilled data scientists or machine learning experts — profiles that are hard for cybersecurity teams to hire — but rather domain experts already on their payroll that have the deepest understanding of their data and uses cases,” Arnaldo says.</p> PatternEx’s Virtual Analyst Platform uses machine learning models to detect suspicious activity on a network. That activity is then presented to human analysts for feedback that improves the systems’ ability to flag activity analysts care about.Innovation and Entrepreneurship (I&E), Startups, Computer Science and Artificial Intelligence Laboratory (CSAIL), Machine learning, Artificial intelligence, Computer science and technology, Data, Cyber security, MIT Schwarzman College of Computing, Laboratory for Information and Decision Systems (LIDS) A road map for artificial intelligence policy In a Starr Forum talk, Luis Videgaray, director of MIT’s AI Policy for the World Project, outlines key facets of regulating new technologies. Thu, 20 Feb 2020 14:08:04 -0500 Peter Dizikes | MIT News Office <p>The rapid development of artificial intelligence technologies around the globe has led to increasing calls for robust AI policy: laws that let innovation flourish while protecting people from privacy violations, exploitive surveillance, biased algorithms, and more.</p> <p>But the drafting and passing of such laws has been anything but easy.</p> <p>“This is a very complex problem,” Luis Videgaray PhD ’98, director of MIT’s AI Policy for the World Project, said in a lecture on Wednesday afternoon. “This is not something that will be solved in a single report. This has got to be a collective conversation, and it will take a while. It will be years in the making.”</p> <p>Throughout his talk, Videgaray outlined an ambitious vision of AI policy around the globe, one that is sensitive to economic and political dynamics, and grounded in material fairness and democratic deliberation.&nbsp;&nbsp;&nbsp;</p> <p>“Trust is probably the most important problem we have,” Videgaray said.</p> <p>Videgaray’s talk, “From Principles to Implementation: The Challenge of AI Policy Around the World,” was part of the Starr Forum series of public discussions about topics of global concern. The Starr Forum is hosted by MIT’s Center for International Studies. Videgaray gave his remarks to a standing-room crowd of over 150 in MIT’s Building E25.</p> <p>Videgaray, who is also a senior lecturer at the MIT Sloan School of Management, previously served as the finance minister of Mexico from 2012 to 2016, and foreign minister of Mexico from 2017 to 2018. Videgaray has also worked extensively in investment banking.</p> <p><strong>Information lag and media hype</strong></p> <p>In his talk, Videgaray began by outlining several “themes” related to AI that he thinks policymakers should keep in mind. These include government uses of AI; the effects of AI on the economy, including the possibility it could help giant tech firms consolidate market power; social responsibility issues, such as privacy, fairness, and bias; and the implications of AI for democracy, at a time when bots can influence political discussion. Videgaray also noted a “geopolitics” of AI regulation — from China’s comprehensive efforts to control technology to the looser methods used in the U.S.</p> <p>Videgaray observed that it is difficult for AI regulators to stay current with technology.</p> <p>“There’s an information lag,” Videgaray said. “Things that concern computer scientists today might become the concerns of policymakers a few years in the future.”</p> <p>Moreover, he noted, media hype can distort perceptions of AI and its applications. Here Videgaray contrasted the <a href="">recent report</a> of MIT’s Task Force on the Future of Work, which finds uncertainty about how many jobs will be replaced with technology, with a recent television documentary presenting a picture of automated vehicles replacing all truck drivers.</p> <p>“Clearly the evidence is nowhere near [indicating] that all jobs in truck driving, in long-distance driving, are going to be lost,” he said. “That is not the case.”</p> <p>With these general issues in mind, what should policymakers do about AI now? Videgaray offered several concrete suggestions. For starters: Policymakers should no longer just outline general philosophical principles, something that has been done many times, with a general convergence of ideas occurring.</p> <p>“Working on principles has very, very small marginal returns,” Videgaray said. “We can go to the next phase … principles are a necessary but not sufficient condition for AI policy. Because policy is about making hard choices in uncertain conditions.”</p> <p>Indeed, he emphasized, more progress can be made by having many AI policy decisions be particular to specific industries. When it comes to, say, medical diagnostics, policymakers want technology “to be very accurate, but you also want it to be explainable, you want it to be fair, without bias, you want the information to be secure … there are many objectives that can conflict with each other. So, this is all about the tradeoffs.”&nbsp;</p> <p>In many cases, he said, algorithm-based AI tools could go through a rigorous testing process, as required in some other industries: “Pre-market testing makes sense,” Videgaray said. “We do that for drugs, clinical trials, we do that for cars, why shouldn’t we do pre-market testing for algorithms?”</p> <p>But while Videgaray sees value in industry-specific regulations, he is not as keen on having a patchwork of varying state-level AI laws being used to regulate technology in the U.S.</p> <p>“Is this a problem for Facebook, for Google? I don’t think so,” Videgaray said. “They have enough resources to navigate through this complexity. But what about startups? What about students from MIT or Cornell or Stanford that are trying to start something, and would have to go through, at the extreme, 55 [pieces of] legislation?”</p> <p><strong>A collaborative conversation</strong></p> <p>At the event, Videgaray was introduced by Kenneth Oye, a professor of political science at MIT who studies technological regulation, and who asked Videgaray questions after the lecture. Among other things, Oye suggested U.S. states could serve as a useful laboratory for regulatory innovation.</p> <p>“In an area characterized by significant uncertainty, complexity, and controversy, there can be benefits to experimentation, having different models being pursued in different areas to see which works best or worse,” Oye suggested.</p> <p>Videgaray did not necessarily disagree, but emphasized the value of an eventual convergence in regulation. The U.S. banking industry, he noted, also followed this trajectory, until “eventually the regulation we have for finance [became] federal,” rather than determined by states.</p> <p>Prior to his remarks, Videgaray acknowledged some audience members, including his PhD thesis adviser at MIT, James Poterba, the Mitsui Professor of Economics, whom Videgaray called “one of the best teachers, not only in economics but about a lot of things in life.” Mexico’s Consul General in Boston, Alberto Fierro, also attended the event.</p> <p>Ultimately, Videgaray emphasized to the audience, the future of AI policy will be collaborative.</p> <p>“You cannot just go to a computer lab and say, ‘Okay, get me some AI policy,’” he stressed. “This has got to be a collective conversation.”</p> Luis Videgaray, director of MIT’s AI Policy for the World Project, talking at his Starr Forum lecture, hosted by the Center for International Studies, on February 19, 2020.Images: courtesy of Laura Kerwin, Center for International StudiesArtificial intelligence, Law, Ethics, Computer science and technology, Political science, Economics, Special events and guest speakers, Global, Center for International Studies, School of Humanities, Arts, and Social Sciences, Sloan School of Management Cryptographic “tag of everything” could protect the supply chain Tiny, battery-free ID chip can authenticate nearly any product to help combat losses to counterfeiting. Thu, 20 Feb 2020 00:00:00 -0500 Rob Matheson | MIT News Office <p>To combat supply chain counterfeiting, which can cost companies billions of dollars annually, MIT researchers have invented a cryptographic ID tag that’s small enough to fit on virtually any product and verify its authenticity.</p> <p>A 2018 report from the Organization for Economic Co-operation and Development estimates about $2 trillion worth of counterfeit goods&nbsp;will be sold worldwide in 2020. That’s bad news for consumers and companies that order parts from different sources worldwide to build products.</p> <p>Counterfeiters tend to use complex routes that include many checkpoints, making it challenging to verifying their origins and authenticity. Consequently, companies can end up with imitation parts. Wireless ID tags are becoming increasingly popular for authenticating assets as they change hands at each checkpoint. But these tags come with various size, cost, energy, and security tradeoffs that limit their potential.</p> <p>Popular radio-frequency identification (RFID) tags, for instance, are too large to fit on tiny objects such as medical and industrial components, automotive parts, or silicon chips. RFID tags also contain no tough security measures. Some tags are built with encryption schemes to protect against cloning and ward off hackers, but they’re large and power hungry. Shrinking the tags means giving up both the antenna package — which enables radio-frequency communication —&nbsp;and the ability to run strong encryption.</p> <p>In a paper presented yesterday at the IEEE International Solid-State Circuits Conference (ISSCC), the researchers describe an ID chip that navigates all those tradeoffs. It’s millimeter-sized and runs on relatively low levels of power supplied by photovoltaic diodes. It also transmits data at far ranges, using a power-free “backscatter” technique that operates at a frequency hundreds of times higher than RFIDs. Algorithm optimization techniques also enable the chip to run a popular cryptography scheme that guarantees secure communications using extremely low energy. &nbsp;&nbsp;</p> <p>“We call it the ‘tag of everything.’ And everything should mean everything,” says co-author Ruonan Han, an associate professor in the Department of Electrical Engineering and Computer Science and head of the Terahertz Integrated Electronics Group in the Microsystems Technology Laboratories (MTL). “If I want to track the logistics of, say, a single bolt or tooth implant or silicon chip, current RFID tags don’t enable that. We built a low-cost, tiny chip without packaging, batteries, or other external components, that stores and transmits sensitive data.”</p> <p>Joining Han on the paper are: graduate students Mohamed I. Ibrahim and Muhammad Ibrahim Wasiq Khan, and former graduate student Chiraag S. Juvekar; former postdoc associate Wanyeong Jung; former postdoc Rabia Tugce Yazicigil, who is currently an assistant professor at Boston University and a visiting scholar at MIT; and Anantha P. Chandrakasan, who is the dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science.</p> <p><strong>System integration</strong></p> <p>The work began as a means of creating better RFID tags. The team wanted to do away with packaging, which makes the tags bulky and increases manufacturing cost. They also wanted communication in the high terahertz frequency between microwave and infrared radiation — around 100 gigahertz and 10 terahertz —&nbsp;that enables chip integration of an antenna array and wireless communications at greater reader distances. Finally, they wanted cryptographic protocols because RFID tags can be scanned by essentially any reader and transmit their data indiscriminately.</p> <p>But including all those functions would normally require building a fairly large chip. Instead, the researchers came up with “a pretty big system integration,” Ibrahim says, that enabled putting everything on a monolithic — meaning, not layered —&nbsp;silicon chip that was only about 1.6 square millimeters.</p> <p>One innovation is an array of small antennas that transmit data back and forth via backscattering between the tag and reader. Backscatter, used commonly in RFID technologies, happens when a tag reflects an input signal back to a reader with slight modulations that correspond to data transmitted. In the researchers’ system, the antennas use some signal splitting and mixing techniques to backscatter signals in the terahertz range. Those signals first connect with the reader and then send data for encryption.</p> <p>Implemented into the antenna array is a “beam steering”&nbsp;function, where the antennas focus signals toward a reader, making them more efficient, increasing signal strength and range, and reducing interference. This is the first demonstration of beam steering by a backscattering tag, according to the researchers.</p> <p>Tiny holes in the antennas allow light from the reader to pass through to photodiodes underneath that convert the light into about 1 volt of electricity. That powers up the chip’s processor, which runs the chip’s “elliptic-curve-cryptography” (ECC) scheme. ECC uses a combination of private keys (known only to a user)&nbsp;and public keys (disseminated widely) to keep communications private. In the researchers’ system, the tag uses a private key and a reader’s public key to identify itself only to valid readers. That means any eavesdropper who doesn’t possess the reader’s private key should not be able to identify which tag is part of the protocol by monitoring just the wireless link. &nbsp;</p> <p>Optimizing the cryptographic code and hardware lets the scheme run on an energy-efficient and small processor, Yazicigil says. “It’s always a tradeoff,” she says. “If you tolerate a higher-power budget and larger size, you can include cryptography. But the challenge is having security in such a small tag with a low-power budget.”</p> <p><strong>Pushing the limits</strong></p> <p>Currently, the signal range sits around 5 centimeters, which is considered a far-field range — and allows for convenient use of a portable tag scanner. Next, the researchers hope to “push the limits” of the range even further, Ibrahim says. Eventually, they’d like many of the tags to ping one reader positioned somewhere far away in, say, a receiving room at a supply chain checkpoint. Many assets could then be verified rapidly.</p> <p>“We think we can have a reader as a central hub that doesn’t have to come close to the tag, and all these chips can beam steer their signals to talk to that one reader,” Ibrahim says.</p> <p>The researchers also hope to fully power the chip through the terahertz signals themselves, eliminating any need for photodiodes.</p> <p>The chips are so small, easy to make, and inexpensive that they can also be embedded into larger silicon computer chips, which are especially popular targets for counterfeiting.</p> <p>“The U.S. semiconductor industry suffered $7 billion to $10 billion in losses annually because of counterfeit chips,” Wasiq Khan says. “Our chip can be seamlessly integrated into other electronic chips for security purposes, so it could have huge impact on industry. Our chips cost a few cents each, but the technology is priceless,” he quipped.</p> MIT researchers’ millimeter-sized ID chip integrates a cryptographic processor, an antenna array that transmits data in the high terahertz range, and photovoltaic diodes for power.Image: courtesy of the researchers, edited by MIT NewsResearch, Computer science and technology, Algorithms, Cyber security, internet of things, electronics, Industry, Microsystems Technology Laboratory, Electrical Engineering & Computer Science (eecs), School of Engineering Benjamin Chang: Might technology tip the global scales? MIT graduate student is assessing the impacts of artificial intelligence on military power, with a focus on the US and China. Wed, 19 Feb 2020 14:30:01 -0500 Leda Zimmerman | MIT Political Science <p>The United States and China seem locked in an ever-tightening embrace, superpowers entangled in a web of economic and military concerns. "Every issue critical to world order — whether climate change, terrorism, or trade — is clearly and closely intertwined with U.S.-China relations," says Benjamin Chang, a fourth-year PhD candidate in political science concentrating in international relations and security studies. "Competition between these nations will shape all outcomes anyone cares about in the next 50 years or more."</p> <p>Little surprise, then, that Chang is homing in on this relationship for his thesis, which broadly examines the impact of artificial intelligence on military power. As China and the United States circle each other as rivals and uneasy partners on the global stage, Chang hopes to learn what the integration of artificial intelligence in different domains might mean for the balance of power.</p> <p>"There is a set of questions related to how technology will be used in the world in general, where the U.S. and China are the two actors with the most influence," says Chang. "I want to know, for instance, how AI will affect strategic stability between them."</p> <p><strong>The nuclear balance</strong></p> <p>In the domain of military power, one question Chang has been pursuing is whether the use of AI in nuclear strategy offers a battlefield advantage. "For the U.S., the main issue involves locating China's elusive mobile missile launchers," Chang says. "The U.S. has satellite and other remote sensors that provide too much intelligence for human analysts, but AI, with its image classifiers based on deep learning, could sort through all this data to locate Chinese assets in a timely fashion."</p> <p>While Chang's data draws on publicly available information about each side's military capabilities, these sources can't provide specific numbers for China's nuclear arsenal. "We don't know if China has 250 or 300 nukes, so I design programs to run combat simulations with high and low numbers of weapons to try and isolate the effects of AI on combat outcomes." Chang credits J. Chappell Lawson, Vipin Narang, and Eric Heginbotham — his advisors in international relations and security studies — for helping shape his research methodology.</p> <p>If the United States develops the capacity to locate these mobile nuclear assets quickly, "that could change the battlefield outcome and hold China's arsenal at risk," says Chang. "And if China feels it isn't able to protect its nuclear arsenal, it might have an incentive to use it or lose it."</p> <p>In subsequent research, Chang will examine the impacts of AI on cybersecurity and on autonomous weaponry such as drones.</p> <p><strong>A start in policy debate</strong></p> <p>Pondering international and security issues began early for Chang. "I developed a big interest in these subjects through policy debate, which motivated me to read hundreds of pages and gave me a breadth and depth of knowledge on disparate topics," he says. "Debate exposed me to the study of military affairs and got me interested in America's role in the world generally."</p> <p>Chang's engagement with policy deepened at Princeton University, where he earned his BA summa cum laude from the Woodrow Wilson School of Public and International Affairs. While he knew he wanted to focus on foreign policy of some kind, his special focus on China came fortuitously: He was assigned to a junior seminar where students developed a working paper on "Building the Rule of Law in China." He took a series of Mandarin language courses, and produced a thesis comparing 19th century American nationalist behavior with modern-day Chinese nationalism.</p> <p>By graduation, Chang knew he wanted to aim for a career in national security and policy by way of a graduate school education. But he sought real-world seasoning first: a two-year stint as an analyst at Long Term Strategy Group, a Washington defense research firm. At LTSG, Chang facilitated wargames simulating Asia-Pacific conflicts, and wrote monographs on Chinese foreign policy, nuclear signaling, and island warfare doctrine.</p> <p><strong>Bridging a divide</strong></p> <p>Today, he is applying this expertise. "I'm trying to use my computer science understanding to bridge the gap between people working at a highly technical level of AI, and folks in security studies who are perhaps less familiar with the technology," he says. Propelled by a National Science Foundation Graduate Research Fellowship and a research fellowship with the Center for Security and Emerging Technology, Chang continues with his simulations and is beginning to write up some of his analysis. He thinks some of his findings might prove surprising.</p> <p>"There is an assumption — based on China's vast collection of personal data and surveillance of citizens — that AI is the means by which China will leapfrog the U.S. in military power," says Chang. "But I think this is wrong." In fact, the United States "has much more military-relevant data than China does, because it collects on so many platforms — in the deep ocean, and from satellites — that are a holdover from fighting the Soviet Union."</p> <p>Among Chang's research challenges: the fact that AI is not a mature technology and hasn't been fully implemented in modern militaries. "There's not yet much literature or data to draw on when assessing its impact," he notes. Also, he would like to nail down a good definition of AI for his field. "With current definitions of AI, thinking about its influence is a bit like investigating the effect of explosives on international affairs: you could be talking about nuclear weapons or dynamite and gunpowder," he says. "In my dissertation I'm attempting a scoping of AI so that it's more amenable to good political science analysis."</p> <p>Getting these ideas down on paper will be Chang's job for at least the next year. The writing occasionally feels like a struggle. "Some days I'll sit there and it won't come out, and other days, after a long walk along the Charles, I can write all day, and it feels good."</p> MIT political science PhD candidate Benjamin ChangPhoto: Benjamin ChangPolitical science, School of Humanities Arts and Social Sciences, Artificial intelligence, Security studies and military, China, Policy, Students, Graduate, postdoctoral, Computer science and technology Bringing artificial intelligence into the classroom, research lab, and beyond Through the Undergraduate Research Opportunities Program, students work to build AI tools with impact. Thu, 13 Feb 2020 16:50:01 -0500 Kim Martineau | MIT Quest for Intelligence <p>Artificial intelligence is reshaping how we live, learn, and work, and this past fall, MIT undergraduates got to explore and build on some of the tools coming out of research labs at MIT. Through the&nbsp;<a href="" target="_blank">Undergraduate Research Opportunities Program</a>&nbsp;(UROP), students worked with researchers at the MIT Quest for Intelligence and elsewhere on projects to improve AI literacy and K-12 education, understand face recognition and how the brain forms new memories, and speed up tedious tasks like cataloging new library material. Six projects are featured below.</p> <p><strong>Programming Jibo to forge an emotional bond with kids</strong></p> <p>Nicole Thumma met her first robot when she was 5, at a museum.&nbsp;“It was incredible that I could have a conversation, even a simple conversation, with this machine,” she says. “It made me think&nbsp;robots&nbsp;are&nbsp;the most complicated manmade thing, which made me want to learn more about them.”</p> <p>Now a senior at MIT, Thumma spent last fall writing dialogue for the social robot Jibo, the brainchild of&nbsp;<a href="">MIT Media Lab</a> Associate Professor&nbsp;<a href="">Cynthia Breazeal</a>. In a UROP project co-advised by Breazeal and researcher&nbsp;<a href="">Hae Won Park</a>, Thumma scripted mood-appropriate dialogue to help Jibo bond with students while playing learning exercises together.</p> <p>Because emotions are complicated, Thumma riffed on a set of basic feelings in her dialogue — happy/sad, energized/tired, curious/bored. If Jibo was feeling sad, but energetic and curious, she might program it to say, “I'm feeling blue today, but something that always cheers me up is talking with my friends, so I'm glad I'm playing with you.​” A tired, sad, and bored Jibo might say, with a tilt of its head, “I don't feel very good. It's like my wires are all mixed up today. I think this activity will help me feel better.”&nbsp;</p> <p>In these brief interactions, Jibo models its vulnerable side and teaches kids how to express their emotions. At the end of an interaction, kids can give Jibo a virtual token to pick up its mood or energy level. “They can see what impact they have on others,” says Thumma. In all, she wrote 80 lines of dialogue, an experience that led to her to stay on at MIT for an MEng in robotics. The Jibos she helped build are now in kindergarten classrooms in Georgia, offering emotional and intellectual support as they read stories and play word games with their human companions.</p> <p><strong>Understanding why familiar faces stand out</strong></p> <p>With a quick glance, the faces of friends and acquaintances jump out from those of strangers. How does the brain do it?&nbsp;<a href="">Nancy Kanwisher</a>’s lab in the&nbsp;<a href="">Department of Brain and Cognitive Sciences</a> (BCS) is building computational models to understand the face-recognition process.&nbsp;<a href="">Two key findings</a>: the brain starts to register the gender and age of a face before recognizing its identity, and that face perception is more robust for familiar faces.</p> <p>This fall, second-year student Joanne Yuan worked with postdoc&nbsp;<a href="">Katharina Dobs</a>&nbsp;to understand&nbsp;why this is so.&nbsp;In earlier experiments, subjects were shown multiple photographs of familiar faces of American celebrities and unfamiliar faces of German celebrities while their brain activity was measured with magnetoencephalography. Dobs found that subjects processed age and gender before the celebrities’ identity regardless of whether the face was familiar. But they were much better at unpacking the gender and identity of faces they knew, like Scarlett Johansson, for example. Dobs suggests that the improved gender and identity recognition for familiar faces is due to a feed-forward mechanism rather than top-down retrieval of information from memory.&nbsp;</p> <p>Yuan has explored both hypotheses with a type of model, convolutional neural networks (CNNs), now widely used in face-recognition tools. She trained a CNN on the face images and studied its layers to understand its processing steps. She found that the model, like Dobs’ human subjects, appeared to process gender and age before identity, suggesting that both CNNs and the brain are primed for face recognition in similar ways. In another experiment, Yuan trained two CNNs on familiar and unfamiliar faces and found that the CNNs, again like humans, were better at identifying the familiar faces.</p> <p>Yuan says she enjoyed exploring two fields — machine learning and neuroscience — while gaining an appreciation for the simple act of recognizing faces. “It’s pretty complicated and there’s so much more to learn,” she says.</p> <p><strong>Exploring memory formation</strong></p> <p>Protruding from the branching dendrites of brain cells are microscopic nubs that grow and change shape as memories form. Improved imaging techniques have allowed researchers to move closer to these nubs, or spines, deep in the brain to learn more about their role in creating and consolidating memories.</p> <p><a href="">Susumu Tonegawa</a>, the Picower Professor of Biology and Neuroscience, has&nbsp;pioneered a technique for labeling clusters of brain cells, called “engram cells,” that are linked to specific memories in mice. Through conditioning, researchers train a mouse, for example, to recognize an environment. By tracking the evolution of dendritic spines in cells linked to a single memory trace, before and after the learning episode, researchers can estimate where memories may be physically stored.&nbsp;</p> <p>But it takes time. Hand-labeling spines in a stack of 100 images can take hours — more, if the researcher needs to consult images from previous days to verify that a spine-like nub really is one, says&nbsp;Timothy O’Connor, a software engineer in BCS helping with the project.&nbsp;With 400 images taken in a typical session, annotating the images can take longer than collecting them, he adds.</p> <p>O’Connor&nbsp;contacted the Quest&nbsp;<a href="">Bridge</a>&nbsp;to see if the process could be automated. Last fall, undergraduates Julian Viera and Peter Hart began work with Bridge AI engineer Katherine Gallagher to train a neural network to automatically pick out the spines. Because spines vary widely in shape and size, teaching the computer what to look for is one big challenge facing the team as the work continues. If successful, the tool could be useful to a hundred other labs across the country.</p> <p>“It’s exciting to work on a project that could have a huge amount of impact,” says Viera. “It’s also cool to be learning something new in computer science and neuroscience.”</p> <p><strong>Speeding up the archival process</strong></p> <p>Each year, Distinctive Collections at the MIT Libraries receives&nbsp;a large volume of personal letters, lecture notes, and other materials from donors inside and outside of MIT&nbsp;that tell MIT’s story and document the history of science and technology.&nbsp;Each of these unique items must be organized and described, with a typical box of material taking up to 20 hours to process and make available to users.</p> <p>To make the work go faster, Andrei Dumitrescu and Efua Akonor, undergraduates at MIT and Wellesley College respectively, are working with Quest Bridge’s Katherine Gallagher to develop an automated system for processing archival material donated to MIT. Their goal: to&nbsp;develop a machine-learning pipeline that can categorize and extract information from scanned images of the records. To accomplish this task, they turned to the U.S. Library of Congress (LOC), which has digitized much of its extensive holdings.&nbsp;</p> <p>This past fall, the students pulled images of about&nbsp;70,000 documents, including correspondence, speeches, lecture notes, photographs, and books&nbsp;housed at the LOC, and trained a classifier to distinguish a letter from, say, a speech. They are now using optical character recognition and a text-analysis tool&nbsp;to extract key details like&nbsp;the date, author, and recipient of a letter, or the date and topic of a lecture. They will soon incorporate object recognition to describe the content of a&nbsp;photograph,&nbsp;and are looking forward to&nbsp;testing&nbsp;their system on the MIT Libraries’ own digitized data.</p> <p>One&nbsp;highlight of the project was learning to use Google Cloud. “This is the real world, where there are no directions,” says Dumitrescu. “It was fun to figure things out for ourselves.”&nbsp;</p> <p><strong>Inspiring the next generation of robot engineers</strong></p> <p>From smartphones to smart speakers, a growing number of devices live in the background of our daily lives, hoovering up data. What we lose in privacy we gain in time-saving personalized recommendations and services. It’s one of AI’s defining tradeoffs that kids should understand, says third-year student Pablo&nbsp;Alejo-Aguirre.&nbsp;“AI brings us&nbsp;beautiful and&nbsp;elegant solutions, but it also has its limitations and biases,” he says.</p> <p>Last year, Alejo-Aguirre worked on an AI literacy project co-advised by Cynthia Breazeal and graduate student&nbsp;<a href="">Randi Williams</a>. In collaboration with the nonprofit&nbsp;<a href="">i2 Learning</a>, Breazeal’s lab has developed an AI curriculum around a robot named Gizmo that teaches kids how to&nbsp;<a href="">train their own robot</a>&nbsp;with an Arduino micro-controller and a user interface based on Scratch-X, a drag-and-drop programming language for children.&nbsp;</p> <p>To make Gizmo accessible for third-graders, Alejo-Aguirre developed specialized programming blocks that give the robot simple commands like, “turn left for one second,” or “move forward for one second.” He added Bluetooth to control Gizmo remotely and simplified its assembly, replacing screws with acrylic plates that slide and click into place. He also gave kids the choice of rabbit and frog-themed Gizmo faces.&nbsp;“The new design is a lot sleeker and cleaner, and the edges are more kid-friendly,” he says.&nbsp;</p> <p>After building and testing several prototypes, Alejo-Aguirre and Williams demoed their creation last summer at a robotics camp. This past fall, Alejo-Aguirre manufactured 100 robots that are now in two schools in Boston and a third in western Massachusetts.&nbsp;“I’m proud of the technical breakthroughs I made through designing, programming, and building the robot, but I’m equally proud of the knowledge that will be shared through this curriculum,” he says.</p> <p><strong>Predicting stock prices with machine learning</strong></p> <p>In search of a practical machine-learning application to learn more about the field, sophomores Dolapo Adedokun and Daniel Adebi hit on stock picking. “We all know buy, sell, or hold,” says Adedokun. “We wanted to find an easy challenge that anyone could relate to, and develop a guide for how to use machine learning in that context.”</p> <p>The two friends approached the Quest Bridge with their own idea for a UROP project after they were turned away by several labs because of their limited programming experience, says Adedokun. Bridge engineer Katherine Gallagher, however, was willing to take on novices. “We’re building machine-learning tools for non-AI specialists,” she says. “I was curious to see how Daniel and Dolapo would approach the problem and reason through the questions they encountered.”</p> <p>Adebi wanted to learn more about reinforcement learning, the trial-and-error AI technique that has allowed computers to surpass humans at chess, Go, and a growing list of video games. So, he and Adedokun worked with Gallagher to structure an experiment to see how reinforcement learning would fare against another AI technique, supervised learning, in predicting stock prices.</p> <p>In reinforcement learning, an agent is turned loose in an unstructured environment with one objective: to maximize a specific outcome (in this case, profits) without being told explicitly how to do so. Supervised learning, by contrast, uses labeled data to accomplish a goal, much like a problem set with the correct answers included.</p> <p>Adedokun and Adebi trained both models on seven years of stock-price data, from 2010-17, for Amazon, Microsoft, and Google. They then compared profits generated by the reinforcement learning model and a trading algorithm based on the supervised model’s price predictions for the following 18 months; they found that their reinforcement learning model produced higher returns.</p> <p>They developed a Jupyter notebook to share what they learned and explain how they built and tested their models. “It was a valuable exercise for all of us,” says Gallagher. “Daniel and Dolapo got hands-on experience with machine-learning fundamentals, and I got insight into the types of obstacles users with their background might face when trying to use the tools we’re building at the Bridge.”</p> Students participating in MIT Quest for Intelligence-funded UROP projects include: (clockwise from top left) Nicole Thumma, Joanne Yuan, Julian Viera, Andrei Dumitrescu, Pablo Alejo-Aguirre, and Dolapo Adedokun.Photo panel: Samantha SmileyQuest for Intelligence, Brain and cognitive sciences, Media Lab, Libraries, School of Engineering, School of Science, Artifical intelligence, Algorithms, Computer science and technology, Machine learning, Undergraduate Research Opportunities Program (UROP), Students, Undergraduate, Electrical engineering and computer science (EECS) SENSE.nano awards seed grants in optoelectronics, interactive manufacturing The mission of SENSE.nano is to foster the development and use of novel sensors, sensing systems, and sensing solutions. Thu, 13 Feb 2020 16:40:01 -0500 MIT.nano <p>SENSE.nano has announced the recipients of the third annual SENSE.nano seed grants. This year’s grants serve to advance innovations in sensing technologies for augmented and virtual realities (AR/VR) and advanced manufacturing systems.</p> <p>A center of excellence powered by MIT.nano, SENSE.nano received substantial interest in its 2019 call for proposals, making for stiff competition. Proposals were reviewed and evaluated by a committee consisting of industry and academia thought-leaders and were selected for funding following significant discussion. Ultimately, two projects were awarded $75,000 each to further research related to detecting movement in molecules and monitoring machine health.&nbsp;</p> <p>“SENSE.nano strives to&nbsp;convey the breadth and depth of sensing research at MIT," says Brian Anthony, co-leader of SENSE.nano, associate director of MIT.nano, and a principal&nbsp;research scientist in the Department of Mechanical Engineering. “As we work to grow SENSE.nano’s research footing and to attract partners, it is encouraging to know that so much important research — in sensors; sensor systems; and sensor science, engineering — is taking place at the Institute.”</p> <p>The projects receiving grants are:</p> <p><strong>P. Donald Keathley and Karl Berggren: Nanostructured optical-field samplers for visible to near-infrared time-domain spectroscopy</strong></p> <p>Research Scientist Phillip “Donnie” Keathley and Professor Karl Berggren from the Department of Electrical Engineering and Computer Science are developing a field-sampling technique using nanoscale structures and light waves to sense vibrational motion of molecules. Keathley is a member of Berggren’s quantum nanostructures and nanofabrication group in the Research Laboratory of Electronics (RLE). The two are investigating an all-on-chip nanoantenna device for sampling weak sub-femtojoule-level electronic fields, in the near-infrared and visible spectrums.</p> <p>Current technology for sampling these spectra of optical energy requires a large apparatus — there is no compact device with enough sensitivity to detect the low-energy signals. Keathley and Berggren propose using plasmonic nanoantennas for measuring low-energy pulses. This technology could have significant impacts on the medical and food-safety industries by revolutionizing the accurate detection and identification of chemicals and bio-chemicals.</p> <p><strong>Jeehwan Kim: Interactive manufacturing enabled by simultaneous sensing and recognition</strong></p> <p>Jeehwan Kim, associate professor with a dual appointment in mechanical engineering and materials science and engineering, proposes an ultra-sensitive sensor system using neuromorphic chips to improve advanced manufacturing through real-time monitoring of machines. Machine failures compromise productivity and cost. Sensors that can instantly process data to provide real-time feedback would be a valuable tool for preventive maintenance of factory machines.</p> <p>Kim’s group, also part of RLE, aims to develop single-crystalline gallium nitride sensors that, when connected to AI chips, will create a feedback loop with the factory machines. Failure patterns would be recognized by the AI hardware, creating an intelligent manufacturing system that can predict and prevent failures. These sensors will have the sensitivity to navigate noisy factory environments, be small enough to form dense arrays, and have the power efficiency to be used on a large number of manufacturing machines.</p> <p>The mission of SENSE.nano is to foster the development and use of novel sensors, sensing systems, and sensing solutions in order to provide previously unimaginable insight into the condition of our world. Two new calls for seed grant proposals will open later this year in conjunction with the Immersion Lab NCSOFT collaboration and then with the SENSE.nano 2020 symposium.</p> <p>In addition to seed grants and the annual conference, SENSE.nano recently launched Talk SENSE — a monthly series for MIT students to further engage with these topics and connect with experts working in sensing technologies.</p> A center of excellence powered by MIT.nano, SENSE.nano received substantial interest in its 2019 call for proposals, making for stiff competition.Photo: David SellaMIT.nano, Mechanical engineering, Electrical engineering and computer science (EECS), Materials Science and Engineering, Nanoscience and nanotechnology, Awards, honors and fellowships, Augmented and virtual reality, Computer science and technology, Artificial intelligence, Research, Funding, Grants, Sensors, School of Engineering, Research Laboratory of Electronics MIT researchers identify security vulnerabilities in voting app Mobile voting application could allow hackers to alter individual votes and may pose privacy issues for users. Thu, 13 Feb 2020 03:00:00 -0500 Abby Abazorius | MIT News Office <p>In recent years, there has been a growing interest in using internet and mobile technology to increase access to the voting process. At the same time, computer security experts caution that paper ballots are the only secure means of voting.</p> <p>Now, MIT researchers are raising another concern: They say they have uncovered security vulnerabilities in a mobile voting application that was used during the 2018 midterm elections in West Virginia. Their security analysis of the application, called Voatz, pinpoints a number of weaknesses, including the opportunity for hackers to alter, stop, or expose how an individual user has voted. Additionally, the researchers found that Voatz’s use of a third-party vendor for voter identification and verification poses potential privacy issues for users.</p> <p>The findings are described in a new <a href="">technical paper</a> by Michael Specter, a graduate student in MIT’s Department of Electrical Engineering and Computer Science (EECS) and a member of MIT’s <a href="">Internet Policy Research Initiative</a>, and James Koppel, also a graduate student in EECS. The research was conducted under the guidance of Daniel Weitzner, a principal research scientist at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and founding director of the Internet Policy Research Initiative.</p> <p>After uncovering these security vulnerabilities, the researchers disclosed their findings to the Department of Homeland Security’s Cybersecurity and Infrastructure Agency (CISA). The researchers, along with the Boston University/MIT Technology Law Clinic, worked in close coordination with election security officials within CISA to ensure that impacted elections officials and the vendor were aware of the findings before the research was made public. This included preparing written summaries of the findings with proof-of-concept code, and direct discussions with affected elections officials on calls arranged by CISA.</p> <p>In addition to its use in the 2018 West Virginia elections, the app was deployed in elections in Denver, Oregon, and Utah, as well as at the 2016 Massachusetts Democratic Convention and the 2016 Utah Republican Convention. Voatz was not used during the 2020 Iowa caucuses.</p> <p>The findings underscore the need for transparency in the design of voting systems, according to the researchers.</p> <p>“We all have an interest in increasing access to the ballot, but in order to maintain trust in our elections system, we must assure that voting systems meet the high technical and operation security standards before they are put in the field,” says Weitzner. “We cannot experiment on our democracy.”&nbsp;&nbsp;&nbsp; &nbsp;</p> <p>“The consensus of security experts is that running a secure election over the internet is not possible today,” adds Koppel. “The reasoning is that weaknesses anywhere in a large chain can give an adversary undue influence over an election, and today’s software is shaky enough that the existence of unknown exploitable flaws is too great a risk to take.”</p> <p><strong>Breaking down the results</strong></p> <p>The researchers were initially inspired to perform a security analysis of Voatz based on Specter’s <a href="">research</a> with Ronald Rivest, Institute Professor at MIT; Neha Narula, director of the MIT Digital Currency Initiative; and Sunoo Park SM ’15, PhD ’18 , exploring the feasibility of using blockchain systems in elections. According to the researchers, Voatz claims to use a permissioned blockchain to ensure security, but has not released any source code or public documentation for how their system operates.</p> <p>Specter, who co-teaches an MIT <a href="">Independent Activities Period course</a> founded by Koppel that is focused on reverse engineering software, broached the idea of reverse engineering Voatz’s application, in an effort to better understand how its system worked. To ensure that they did not interfere with any ongoing elections or expose user records, Specter and Koppel reverse-engineered the application and then created a model of Voatz’s server.</p> <p>They found that an adversary with remote access to the device can alter or discover a user’s vote, and that the server, if hacked, could easily change those votes. “It does not appear that the app’s protocol attempts to verify [genuine votes] with the back-end blockchain,” Specter explains.</p> <p>“Perhaps most alarmingly, we found that a passive network adversary, like your internet service provider, or someone nearby you if you’re on unencrypted Wi-Fi, could detect which way you voted in some configurations of the election. Worse, more aggressive attackers could potentially detect which way you’re going to vote and then stop the connection based on that alone.”</p> <p>In addition to detecting vulnerabilities with Voatz’s voting process, Specter and Koppel found that the app poses privacy issues for users. As the app uses an external vendor for voter ID verification, a third party could potentially access a voter’s photo, driver’s license data, or other forms of identification, if that vendor’s platform isn’t also secure.&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;</p> <p>“Though Voatz’s privacy policy does talk about sending some information to third parties, as far as we can tell the fact that any third party is getting the voter’s driver’s license and selfie isn’t explicitly mentioned,” Specter notes.</p> <p><strong>Calls for increased openness</strong></p> <p>Specter and Koppel say that their findings point to the need for openness when it comes to election administration, in order to ensure the integrity of the election process. Currently, they note, the election process in states that use paper ballots is designed to be transparent, and citizens and political party representatives are given opportunities to observe the voting process.</p> <p>In contrast, Koppel notes, “Voatz’s app and infrastructure were completely closed-source; we were only able to get access to the app itself.&nbsp;&nbsp;&nbsp; &nbsp;</p> <p>“I think this type of analysis is extremely important. Right now, there’s a drive to make voting more accessible, by using internet and mobile-based voting systems. The problem here is that sometimes those systems aren’t made by people who have expertise in keeping voting systems secure, and they’re deployed before they can get proper review,” says Matthew Green, an associate professor at the Johns Hopkins Information Security Institute. In the case of Voatz, he adds, “It looks like there were many good intentions here, but the result lacks key features that would protect a voter and protect the integrity of elections.”</p> <p>Going forward, the researchers caution that software developers should prove their systems are as secure as paper ballots.</p> <p>“The biggest issue is transparency,” says Specter. “When you have part of the election that is opaque, that is not viewable, that is not public, that has some sort of proprietary component, that part of the system is inherently suspect and needs to be put under a lot of scrutiny.”</p> Cyber security, Voting and elections, Computer science and technology, Apps, Technology and society, Internet Policy Research Initiative, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering “Sensorized” skin helps soft robots find their bearings Flexible sensors and an artificial intelligence model tell deformable robots how their bodies are positioned in a 3D environment. Wed, 12 Feb 2020 23:59:59 -0500 Rob Matheson | MIT News Office <p>For the first time, MIT researchers have enabled a soft robotic arm to understand its configuration in 3D space, by leveraging only motion and position data from its own “sensorized” skin.</p> <p>Soft robots constructed from highly compliant materials, similar to those found in living organisms, are being championed as safer, and more adaptable, resilient, and bioinspired alternatives to traditional rigid robots. But giving autonomous control to these deformable robots is a monumental task because they can move in a virtually infinite number of directions at any given moment. That makes it difficult to train planning and control models that drive automation.</p> <p>Traditional methods to achieve autonomous control use large systems of multiple motion-capture cameras that provide the robots feedback about 3D movement and positions. But those are impractical for soft robots in real-world applications.</p> <p>In a paper being published in the journal <em>IEEE Robotics and Automation Letters</em>, the researchers describe a system of soft sensors that cover a robot’s body to provide “proprioception” — meaning awareness of motion and position of its body. That feedback runs into a novel deep-learning model that sifts through the noise and captures clear signals to estimate the robot’s 3D configuration. The researchers validated their system on a soft robotic arm resembling an elephant trunk, that can predict its own position as it autonomously swings around and extends.</p> <p>The sensors can be fabricated using off-the-shelf materials, meaning any lab can develop their own systems, says Ryan Truby, a postdoc in the MIT Computer Science and Artificial Laboratory (CSAIL) who is co-first author on the paper along with CSAIL postdoc Cosimo Della Santina.</p> <p>“We’re sensorizing soft robots to get feedback for control from sensors, not vision systems, using a very easy, rapid method for fabrication,” he says. “We want to use these soft robotic trunks, for instance, to orient and control themselves automatically, to pick things up and interact with the world. This is a first step toward that type of more sophisticated automated control.”</p> <p>One future aim is to help make artificial limbs that can more dexterously handle and manipulate objects in the environment. “Think of your own body: You can close your eyes and reconstruct the world based on feedback from your skin,” says co-author Daniela Rus, director of CSAIL and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science. “We want to design those same capabilities for soft robots.”</p> <p><strong>Shaping soft sensors</strong></p> <p>A longtime goal in soft robotics has been fully integrated body sensors. Traditional rigid sensors detract from a soft robot body’s natural compliance, complicate its design and fabrication, and can cause various mechanical failures. Soft-material-based sensors are a more suitable alternative, but require specialized materials and methods for their design, making them difficult for many robotics labs to fabricate and integrate in soft robots.</p> <p>While working in his CSAIL lab one day looking for inspiration for sensor materials, Truby made an interesting connection. “I found these sheets of conductive materials used for electromagnetic interference shielding, that you can buy anywhere in rolls,” he says. These materials have “piezoresistive” properties, meaning they change in electrical resistance when strained. Truby realized they could make effective soft sensors if they were placed on certain spots on the trunk. As the sensor deforms in response to the trunk’s stretching and compressing, its electrical resistance is converted to a specific output voltage. The voltage is then used as a signal correlating to that movement.</p> <p>But the material didn’t stretch much, which would limit its use for soft robotics. Inspired by kirigami —&nbsp;a variation of origami that includes making cuts in a material — Truby designed and laser-cut rectangular strips of conductive silicone sheets into various patterns, such as rows of tiny holes or crisscrossing slices like a chain link fence. That made them far more flexible, stretchable, “and beautiful to look at,” Truby says.</p> <p><img alt="" src="/sites/" style="width: 500px; height: 281px;" /></p> <p><img alt="" src="/sites/" style="width: 500px; height: 281px;" /></p> <p><em style="font-size: 10px;">Credit: Ryan L. Truby, MIT&nbsp;CSAIL</em></p> <p>The researchers’ robotic trunk comprises three segments, each with four fluidic actuators (12 total) used to move the arm. They fused one sensor over each segment, with each sensor covering and gathering data from one embedded actuator in the soft robot. They used “plasma bonding,” a technique that energizes a surface of a material to make it bond to another material. It takes roughly a couple hours to shape dozens of sensors that can be bonded to the soft robots using a handheld plasma-bonding device.</p> <p><img alt="" src="/sites/" style="width: 500px; height: 281px;" /></p> <p><span style="font-size:10px;"><em>Credit: Ryan L. Truby, MIT&nbsp;CSAIL</em></span></p> <p><strong>“Learning” configurations</strong></p> <p>As hypothesized, the sensors did capture the trunk’s general movement. But they were really noisy. “Essentially, they’re nonideal sensors in many ways,” Truby says. “But that’s just a common fact of making sensors from soft conductive materials. Higher-performing and more reliable sensors require specialized tools that most robotics labs do not have.”</p> <p>To estimate the soft robot’s configuration using only the sensors, the researchers built a deep neural network to do most of the heavy lifting, by sifting through the noise to capture meaningful feedback signals. The researchers developed a new model to kinematically describe the soft robot’s shape that vastly reduces the number of variables needed for their model to process.</p> <p>In experiments, the researchers had the trunk swing around and extend itself in random configurations over approximately an hour and a half. They used the traditional motion-capture system for ground truth data. In training, the model analyzed data from its sensors to predict a configuration, and compared its predictions to that ground truth data which was being collected simultaneously. In doing so, the model “learns” to map signal patterns from its sensors to real-world configurations. Results indicated, that for certain and steadier configurations, the robot’s estimated shape matched the ground truth.</p> <p>Next, the researchers aim to explore new sensor designs for improved sensitivity and to develop new models and deep-learning methods to reduce the required training for every new soft robot. They also hope to refine the system to better capture the robot’s full dynamic motions.</p> <p>Currently, the neural network and sensor skin are not sensitive to capture subtle motions or dynamic movements. But, for now, this is an important first step for learning-based approaches to soft robotic control, Truby says: “Like our soft robots, living systems don’t have to be totally precise. Humans are not precise machines, compared to our rigid robotic counterparts, and we do just fine.”</p> MIT researchers have created a “sensorized” skin, made with kirigami-inspired sensors, that gives soft robots greater awareness of the motion and position of their bodies.Ryan L. Truby, MIT CSAILResearch, Computer science and technology, Algorithms, Robots, Robotics, Soft robotics, Design, Machine learning, Materials Science and Engineering, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering Automated system can rewrite outdated sentences in Wikipedia articles Text-generating tool pinpoints and replaces specific information in sentences while retaining humanlike grammar and style. Wed, 12 Feb 2020 13:51:56 -0500 Rob Matheson | MIT News Office <p>A system created by MIT researchers could be used to automatically update factual inconsistencies in Wikipedia articles, reducing time and effort spent by human editors who now do the task manually.</p> <p>Wikipedia comprises millions of articles that are in constant need of edits to reflect new information. That can involve article expansions, major rewrites, or more routine modifications such as updating numbers, dates, names, and locations. Currently, humans across the globe volunteer their time to make these edits.&nbsp;&nbsp;</p> <p>In a paper being presented at the AAAI Conference on Artificial Intelligence, the researchers describe a text-generating system that pinpoints and replaces specific information in relevant Wikipedia sentences, while keeping the language similar to how humans write and edit.</p> <p>The idea is that humans would type into an interface an unstructured sentence with updated information, without needing to worry about style or grammar. The system would then search Wikipedia, locate the appropriate page and outdated sentence, and rewrite it in a humanlike fashion. In the future, the researchers say, there’s potential to build a fully automated system that identifies and uses the latest information from around the web to produce rewritten sentences in corresponding Wikipedia articles that reflect updated information.</p> <p>“There are so many updates constantly needed to Wikipedia articles. It would be beneficial to automatically modify exact portions of the articles, with little to no human intervention,” says Darsh Shah, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and one of the lead authors. “Instead of hundreds of people working on modifying each Wikipedia article, then you’ll only need a few, because the model is helping or doing it automatically. That offers dramatic improvements in efficiency.”</p> <p>Many other bots exist that make automatic Wikipedia edits. Typically, those work on mitigating vandalism or dropping some narrowly defined information into predefined templates, Shah says. The researchers’ model, he says, solves a harder artificial intelligence problem: Given a new piece of unstructured information, the model automatically modifies the sentence in a humanlike fashion. “The other [bot] tasks are more rule-based, while this is a task requiring reasoning over contradictory parts in two sentences and generating a coherent piece of text,” he says.</p> <p>The system can be used for other text-generating applications as well, says co-lead author and CSAIL graduate student Tal Schuster. In their paper, the researchers also used it to automatically synthesize sentences in a popular fact-checking dataset that helped reduce bias, without manually collecting additional data. “This way, the performance improves for automatic fact-verification models that train on the dataset for, say, fake news detection,” Schuster says.</p> <p>Shah and Schuster worked on the paper with their academic advisor Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science and a professor in CSAIL.</p> <p><strong>Neutrality masking and fusing</strong></p> <p>Behind the system is a fair bit of text-generating ingenuity in identifying contradictory information between, and then fusing together, two separate sentences. It takes as input an “outdated” sentence from a Wikipedia article, plus a separate “claim” sentence that contains the updated and conflicting information. The system must automatically delete and keep specific words in the outdated sentence, based on information in the claim, to update facts but maintain style and grammar. That’s an easy task for humans, but a novel one in machine learning.</p> <p>For example, say there’s a required update to this sentence (in bold): “Fund A considers <strong>28 of their 42</strong> minority stakeholdings in operationally active companies to be of particular significance to the group.” The claim sentence with updated information may read: “Fund A considers <strong>23 of 43</strong> minority stakeholdings significant.” The system would locate the relevant Wikipedia text for “Fund A,” based on the claim. It then automatically strips out the outdated numbers (28 and 42) and replaces them with the new numbers (23 and 43), while keeping the sentence exactly the same and grammatically correct. (In their work, the researchers ran the system on a dataset of specific Wikipedia sentences, not on all Wikipedia pages.)</p> <p>The system was trained on a popular dataset that contains pairs of sentences, in which one sentence is a claim and the other is a relevant Wikipedia sentence. Each pair is labeled in one of three ways: “agree,” meaning the sentences contain matching factual information; “disagree,” meaning they contain contradictory information; or “neutral,” where there’s not enough information for either label. The system must make all disagreeing pairs agree, by modifying the outdated sentence to match the claim. That requires using two separate models to produce the desired output.</p> <p>The first model is a fact-checking classifier — pretrained to label each sentence pair as “agree,” “disagree,” or “neutral” — that focuses on disagreeing pairs. Running in conjunction with the classifier is a custom “neutrality masker” module that identifies which words in the outdated sentence contradict the claim. The module removes the minimal number of words required to “maximize neutrality” — meaning the pair can be labeled as neutral. That’s the starting point: While the sentences don’t agree, they no longer contain obviously contradictory information. The module creates a binary “mask” over the outdated sentence, where a 0 gets placed over words that most likely require deleting, while a 1 goes on top of keepers.</p> <p>After masking, a novel two-encoder-decoder framework is used to generate the final output sentence. This model learns compressed representations of the claim and the outdated sentence. Working in conjunction, the two encoder-decoders fuse the dissimilar words from the claim, by sliding them into the spots left vacant by the deleted words (the ones covered with 0s) in the outdated sentence.</p> <p>In one test, the model scored higher than all traditional methods, using a technique called “SARI” that measures how well machines delete, add, and keep words compared to the way humans modify sentences. They used a dataset with manually edited Wikipedia sentences, which the model hadn’t seen before. Compared to several traditional text-generating methods, the new model was more accurate in making factual updates and its output more closely resembled human writing. In another test, crowdsourced humans scored the model (on a scale of 1 to 5) based on how well its output sentences contained factual updates and matched human grammar. The model achieved average scores of 4 in factual updates and 3.85 in matching grammar.</p> <p><strong>Removing bias</strong></p> <p>The study also showed that the system can be used to augment datasets to eliminate bias when training detectors of “fake news,” a form of propaganda containing disinformation created to mislead readers in order to generate website views or steer public opinion. Some of these detectors train on datasets of agree-disagree sentence pairs to “learn” to verify a claim by matching it to given evidence.</p> <p>In these pairs, the claim will either match certain information with a supporting “evidence” sentence from Wikipedia (agree) or it will be modified by humans to include information contradictory to the evidence sentence (disagree). The models are trained to flag claims with refuting evidence as “false,” which can be used to help identify fake news.</p> <p>Unfortunately, such datasets currently come with unintended biases, Shah says: “During training, models use some language of the human written claims as “give-away” phrases to mark them as false, without relying much on the corresponding evidence sentence. This reduces the model’s accuracy when evaluating real-world examples, as it does not perform fact-checking.”</p> <p>The researchers used the same deletion and fusion techniques from their Wikipedia project to balance the disagree-agree pairs in the dataset and help mitigate the bias. For some “disagree” pairs, they used the modified sentence’s false information to regenerate a fake “evidence” supporting sentence. Some of the give-away phrases then exist in both the “agree” and “disagree” sentences, which forces models to analyze more features. Using their augmented dataset, the researchers reduced the error rate of a popular fake-news detector by 13 percent.</p> <p>“If you have a bias in your dataset, and you’re fooling your model into just looking at one sentence in a disagree pair to make predictions, your model will not survive the real world,” Shah says. “We make models look at both sentences in all agree-disagree pairs.”</p> MIT researchers have created an automated text-generating system that pinpoints and replaces specific information in relevant Wikipedia sentences, while keeping the language similar to how humans write and edit.Image: Christine Daniloff, MITResearch, Computer science and technology, Algorithms, Machine learning, Data, Internet, Crowdsourcing, Social media, Technology and society, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering Bridging the gap between human and machine vision Researchers develop a more robust machine-vision architecture by studying how human vision responds to changing viewpoints of objects. Tue, 11 Feb 2020 16:40:01 -0500 Kris Brewer | Center for Brains, Minds and Machines <p>Suppose you look briefly from a few feet away at a person you have never met before. Step back a few paces and look again. Will you be able to recognize her face? “Yes, of course,” you probably are thinking. If this is true, it would mean that our visual system, having seen a single image of an object such as a specific face, recognizes it robustly despite changes to the object’s position and scale, for example. On the other hand, we know that state-of-the-art classifiers, such as vanilla deep networks, will fail this simple test.</p> <p>In order to recognize a specific face under a range of transformations, neural networks need to be trained with many examples of the face under the different conditions. In other words, they can achieve invariance through memorization, but cannot do it if only one image is available. Thus, understanding how human vision can pull off this remarkable feat is relevant for engineers aiming to improve their existing classifiers. It also is important for neuroscientists modeling the primate visual system with deep networks. In particular, it is possible that the invariance with one-shot learning exhibited by biological vision requires a rather different computational strategy than that of deep networks.&nbsp;</p> <p>A new paper by MIT PhD candidate in electrical engineering and computer science Yena Han and colleagues in <em>Nature Scientific Reports</em> entitled “Scale and translation-invariance for novel objects in human vision” discusses how they study this phenomenon more carefully to create novel biologically inspired networks.</p> <p>"Humans can learn from very few examples, unlike deep networks. This is a huge difference with vast implications for engineering of vision systems and for understanding how human vision really works," states co-author Tomaso Poggio — director of the Center for Brains, Minds and Machines (CBMM) and the Eugene McDermott Professor of Brain and Cognitive Sciences at MIT. "A key reason for this difference is the relative invariance of the primate visual system to scale, shift, and other transformations. Strangely, this has been mostly neglected in the AI community, in part because the psychophysical data were so far less than clear-cut. Han's work has now established solid measurements of basic invariances of human vision.”</p> <p>To differentiate invariance rising from intrinsic computation with that from experience and memorization, the new study measured the range of invariance in one-shot learning. A one-shot learning task was performed by presenting Korean letter stimuli to human subjects who were unfamiliar with the language. These letters were initially presented a single time under one specific condition and tested at different scales or positions than the original condition. The first experimental result is that — just as you guessed — humans showed significant scale-invariant recognition after only a single exposure to these novel objects. The second result is that the range of position-invariance is limited, depending on the size and placement of objects.</p> <p>Next, Han and her colleagues performed a comparable experiment in deep neural networks designed to reproduce this human performance. The results suggest that to explain invariant recognition of objects by humans, neural network models should explicitly incorporate built-in scale-invariance. In addition, limited position-invariance of human vision is better replicated in the network by having the model neurons’ receptive fields increase as they are further from the center of the visual field. This architecture is different from commonly used neural network models, where an image is processed under uniform resolution with the same shared filters.</p> <p>“Our work provides a new understanding of the brain representation of objects under different viewpoints. It also has implications for AI, as the results provide new insights into what is a good architectural design for deep neural networks,” remarks Han, CBMM researcher and lead author of the study.</p> <p>Han and Poggio were joined by Gemma Roig and Gad Geiger in the work.</p> Yena Han (left) and Tomaso Poggio stand with an example of the visual stimuli used in a new psychophysics study.Photo: Kris BrewerCenter for Brains Minds and Machines, Brain and cognitive sciences, Machine learning, Artificial intelligence, Computer vision, Research, School of Science, Computer science and technology, Electrical Engineering & Computer Science (eecs), School of Engineering Brainstorming energy-saving hacks on Satori, MIT’s new supercomputer Three-day hackathon explores methods for making artificial intelligence faster and more sustainable. Tue, 11 Feb 2020 11:50:01 -0500 Kim Martineau | MIT Quest for Intelligence <p>Mohammad Haft-Javaherian planned to spend an hour at the&nbsp;<a href="">Green AI Hackathon</a>&nbsp;— just long enough to get acquainted with MIT’s new supercomputer,&nbsp;<a href="">Satori</a>. Three days later, he walked away with $1,000 for his winning strategy to shrink the carbon footprint of artificial intelligence models trained to detect heart disease.&nbsp;</p> <p>“I never thought about the kilowatt-hours I was using,” he says. “But this hackathon gave me a chance to look at my carbon footprint and find ways to trade a small amount of model accuracy for big energy savings.”&nbsp;</p> <p>Haft-Javaherian was among six teams to earn prizes at a hackathon co-sponsored by the&nbsp;<a href="">MIT Research Computing Project</a>&nbsp;and&nbsp;<a href="">MIT-IBM Watson AI Lab</a> Jan. 28-30. The event was meant to familiarize students with Satori, the computing cluster IBM&nbsp;<a href="">donated</a> to MIT last year, and to inspire new techniques for building energy-efficient AI models that put less planet-warming carbon dioxide into the air.&nbsp;</p> <p>The event was also a celebration of Satori’s green-computing credentials. With an architecture designed to minimize the transfer of data, among other energy-saving features, Satori recently earned&nbsp;<a href="">fourth place</a>&nbsp;on the Green500 list of supercomputers. Its location gives it additional credibility: It sits on a remediated brownfield site in Holyoke, Massachusetts, now the&nbsp;<a href="">Massachusetts Green High Performance Computing Center</a>, which runs largely on low-carbon hydro, wind and nuclear power.</p> <p>A postdoc at MIT and Harvard Medical School, Haft-Javaherian came to the hackathon to learn more about Satori. He stayed for the challenge of trying to cut the energy intensity of his own work, focused on developing AI methods to screen the coronary arteries for disease. A new imaging method, optical coherence tomography, has given cardiologists a new tool for visualizing defects in the artery walls that can slow the flow of oxygenated blood to the heart. But even the experts can miss subtle patterns that computers excel at detecting.</p> <p>At the hackathon, Haft-Javaherian ran a test on his model and saw that he could cut its energy use eight-fold by reducing the time Satori’s graphics processors sat idle. He also experimented with adjusting the model’s number of layers and features, trading varying degrees of accuracy for lower energy use.&nbsp;</p> <p>A second team, Alex Andonian and Camilo Fosco, also won $1,000 by showing they could train a classification model nearly 10 times faster by optimizing their code and losing a small bit of accuracy. Graduate students in the Department of Electrical Engineering and Computer Science (EECS), Andonian and Fosco are currently training a classifier to tell legitimate videos from AI-manipulated fakes, to compete in Facebook’s&nbsp;<a href="">Deepfake Detection Challenge</a>. Facebook launched the contest last fall to crowdsource ideas for stopping the spread of misinformation on its platform ahead of the 2020 presidential election.</p> <p>If a technical solution to deepfakes is found, it will need to run on millions of machines at once, says Andonian. That makes energy efficiency key. “Every optimization we can find to train and run more efficient models will make a huge difference,” he says.</p> <p>To speed up the training process, they tried streamlining their code and lowering the resolution of their 100,000-video training set by eliminating some frames. They didn’t expect a solution in three days, but Satori’s size worked in their favor. “We were able to run 10 to 20 experiments at a time, which let us iterate on potential ideas and get results quickly,” says Andonian.&nbsp;</p> <p>As AI continues to improve at tasks like reading medical scans and interpreting video, models have grown bigger and more calculation-intensive, and thus, energy intensive. By one&nbsp;<a href="">estimate</a>, training a large language-processing model produces nearly as much carbon dioxide as the cradle-to-grave emissions from five American cars. The footprint of the typical model is modest by comparison, but as AI applications proliferate its environmental impact is growing.&nbsp;</p> <p>One way to green AI, and tame the exponential growth in demand for training AI, is to build smaller models. That’s the approach that a third hackathon competitor, EECS graduate student Jonathan Frankle, took. Frankle is looking for signals early in the training process that point to subnetworks within the larger, fully-trained network that can do the same job.&nbsp;The idea builds on his award-winning&nbsp;<a href="">Lottery Ticket Hypothesis</a>&nbsp;paper from last year that found a neural network could perform with 90 percent fewer connections if the right subnetwork was found early in training.</p> <p>The hackathon competitors were judged by John Cohn, chief scientist at the MIT-IBM Watson AI Lab, Christopher Hill, director of MIT’s Research Computing Project, and Lauren Milechin, a research software engineer at MIT.&nbsp;</p> <p>The judges recognized four&nbsp;other teams: Department of Earth, Atmospheric and Planetary Sciences (EAPS) graduate students Ali Ramadhan,&nbsp;Suyash Bire, and James Schloss,&nbsp;for adapting the programming language Julia for Satori; MIT Lincoln Laboratory postdoc Andrew Kirby, for adapting code he wrote as a graduate student to Satori using a library designed for easy programming of computing architectures; and Department of Brain and Cognitive Sciences graduate students Jenelle Feather and Kelsey Allen, for applying a technique that drastically simplifies models by cutting their number of parameters.</p> <p>IBM developers were on hand to answer questions and gather feedback.&nbsp;&nbsp;“We pushed the system — in a good way,” says Cohn. “In the end, we improved the machine, the documentation, and the tools around it.”&nbsp;</p> <p>Going forward, Satori will be joined in Holyoke by&nbsp;<a href="">TX-Gaia</a>, Lincoln Laboratory’s new supercomputer.&nbsp;Together, they will provide feedback on the energy use of their workloads. “We want to raise awareness and encourage users to find innovative ways to green-up all of their computing,” says Hill.&nbsp;</p> Several dozen students participated in the Green AI Hackathon, co-sponsored by the MIT Research Computing Project and MIT-IBM Watson AI Lab. Photo panel: Samantha SmileyQuest for Intelligence, MIT-IBM Watson AI Lab, Electrical engineering and computer science (EECS), EAPS, Lincoln Laboratory, Brain and cognitive sciences, School of Engineering, School of Science, Algorithms, Artificial intelligence, Computer science and technology, Data, Machine learning, Software, Climate change, Awards, honors and fellowships, Hackathon, Special events and guest speakers Hey Alexa! Sorry I fooled you ... MIT’s new system TextFooler can trick the types of natural-language-processing systems that Google uses to help power its search results, including audio for Google Home. Fri, 07 Feb 2020 11:20:01 -0500 Rachel Gordon | MIT CSAIL <p>A human can likely tell the difference between a turtle and a rifle. Two years ago, Google’s AI wasn’t so <a href="">sure</a>. For quite some time, a subset of computer science research has been dedicated to better understanding how machine-learning models handle these “adversarial” attacks, which are inputs deliberately created to trick or fool machine-learning algorithms.&nbsp;</p> <p>While much of this work has focused on <a href="">speech</a> and <a href="">images</a>, recently, a team from MIT’s <a href="">Computer Science and Artificial Intelligence Laboratory</a> (CSAIL) tested the boundaries of text. They came up with “TextFooler,” a general framework that can successfully attack natural language processing (NLP) systems — the types of systems that let us interact with our Siri and Alexa voice assistants — and “fool” them into making the wrong predictions.&nbsp;</p> <p>One could imagine using TextFooler for many applications related to internet safety, such as email spam filtering, hate speech flagging, or “sensitive” political speech text detection — which are all based on text classification models.&nbsp;</p> <p>“If those tools are vulnerable to purposeful adversarial attacking, then the consequences may be disastrous,” says Di Jin, MIT PhD student and lead author on a new paper about TextFooler. “These tools need to have effective defense approaches to protect themselves, and in order to make such a safe defense system, we need to first examine the adversarial methods.”&nbsp;</p> <p>TextFooler works in two parts: altering a given text, and then using that text to test two different language tasks to see if the system can successfully trick machine-learning models.&nbsp;&nbsp;</p> <p>The system first identifies the most important words that will influence the target model’s prediction, and then selects the synonyms that fit contextually. This is all while maintaining grammar and the original meaning to look “human” enough, until the prediction is altered.&nbsp;</p> <p>Then, the framework is applied to two different tasks — text classification, and entailment (which is the relationship between text fragments in a sentence), with the goal of changing the classification or invalidating the entailment judgment of the original models.&nbsp;</p> <p>In one example, TextFooler’s input and output were:</p> <p>“The characters, cast in impossibly contrived situations, are totally estranged from reality.”&nbsp;</p> <p>“The characters, cast in impossibly engineered circumstances, are fully estranged from reality.”&nbsp;</p> <p>In this case, when testing on an NLP model, it gets the example input right, but then gets the modified input wrong.&nbsp;</p> <p>In total, TextFooler successfully attacked three target models, including “BERT,” the popular open-source NLP model. It fooled the target models with an accuracy of over 90 percent to under 20 percent, by changing only 10 percent of the words in a given text. The team evaluated success on three criteria: changing the model's prediction for classification or entailment; whether it looked similar in meaning to a human reader, compared with the original example; and whether the text looked natural enough.&nbsp;</p> <p>The researchers note that while attacking existing models is not the end goal, they hope that this work will help more abstract models generalize to new, unseen data.&nbsp;</p> <p>“The system can be used or extended to attack any classification-based NLP models to test their robustness,” says Jin. “On the other hand, the generated adversaries can be used to improve the robustness and generalization of deep-learning models via adversarial training, which is a critical direction of this work.”&nbsp;</p> <p>Jin wrote the paper alongside MIT Professor Peter Szolovits, Zhijing Jin of the University of Hong Kong, and Joey Tianyi Zhou of A*STAR, Singapore. They will present the paper at the AAAI Conference on Artificial Intelligence in New York.&nbsp;</p> CSAIL PhD student Di Jin led the development of the TextFooler system.Photo: Jason Dorfman/MIT CSAILComputer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Machine learning, Algorithms, Data, Natural language processing, Artificial intelligence, Electrical Engineering & Computer Science (eecs), School of Engineering, Technology and society Giving cryptocurrency users more bang for their buck Routing scheme boosts efficiency in networks that help speed up blockchain transactions. Thu, 30 Jan 2020 13:43:32 -0500 Rob Matheson | MIT News Office <p>A new cryptocurrency-routing scheme co-invented by MIT researchers can boost the efficiency — and, ultimately, profits — of certain networks designed to speed up notoriously slow blockchain transactions.&nbsp;&nbsp;</p> <p>Cryptocurrencies hold promise for peer-to-peer financial transactions, potentially making banks and credit cards obsolete. But there’s a scalability issue: Bitcoin, for instance, processes only a handful of transactions per second, while major credit cards process hundreds or thousands. That’s because the blockchain — the digital ledger cryptocurrencies are built on — takes a really long time to process transactions.&nbsp;</p> <p>A new solution is “payment channel networks” (PCNs), where transactions are completed with minimal involvement from the blockchain. Pairs of PCN users form off-blockchain escrow accounts with a dedicated amount of money, forming a large, interconnected network of joint accounts. Users route payments through these &nbsp;accounts, only pinging the blockchain to establish and close the accounts, which speeds things up dramatically. Accounts can also collect a tiny fee when transactions get routed through them.</p> <p>Inefficient routing schemes, however, slow down even these fast solutions. They deplete users’ balances in these accounts frequently, forcing them to invest a lot of money in each account or frequently rebalance their accounts on the blockchain. In a paper being presented next month at the USENIX Symposium on Networked Systems Design and Implementation, the researchers introduce “Spider,” a more efficient routing scheme that lets users invest only a fraction of funds in each account and process roughly four times more transactions before rebalancing on the blockchain.</p> <p>“It’s important to have balanced, high-throughput routing in PCNs to ensure the money that users put into joint accounts is used efficiently,” says first author Vibhaalakshmi Sivaraman, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “This should be efficient and a lucrative business. That means routing as many transactions as possible, with as little funds as possible, to give PCNs the best bang for their buck.”</p> <p>Joining Sivaraman on the paper are former postdoc Shaileshh Bojja Venkatakrishnan, CSAIL graduate students Parimarjan Negi and Lei Yang, and Mohammad Alizadeh, an associate professor of electrical engineering and computer science and a CSAIL researcher; Radhika Mittal of the University of Illinois at Urbana-Champaign; and Kathleen Ruan and Giulia Fanti of Carnegie Mellon University.</p> <p><strong>Packet payments</strong></p> <p>PCNs rely heavily on bidirectional joint accounts — where both parties can receive and send money — so money can be routed between any users. User B can have a joint account with user A, while also linking separately to user C. Users A and C are not directly connected, but user A can send money to user C via the A-B and B-C joint accounts.</p> <p>To exchange funds, each party must approve and update the balances in their joint accounts. Payments can only be routed on channels with sufficient funds to handle the transactions, causing major issues.</p> <p>Traditional schemes send transactions along the shortest path possible, without being aware of any given user’s balance or the rate of sending on that account. This can cause one of the users in the joint account to handle too many transactions and drop to a zero balance, making it unable to route further transactions. What’s more, users can only send a payment in full. If a user wants to send, say, 10 bitcoins, current schemes try to push the full amount on the shortest path possible. If that path can’t support all 10 bitcoins at once, they’ll search for the next shortest path, and so on — which can slow down or completely fail the transaction.</p> <p>Inspired by a technique for internet communications called packet switching, Spider splits each full transaction into smaller “packets” that are sent across different channels at different rates. This lets the scheme route chunks of these large payments through potentially low-funded accounts. Each packet is then far more likely to reach its destination without slowing down the network or being rejected in any given account for its size.</p> <p>“Shortest-path routing can cause imbalances between accounts that deplete key payment channels and paralyze the system,” Sivaraman says. “Routing money in a way that the funds of both users in each joint account are balanced allows us to reuse the same initial funds to support as many transactions as possible.”</p> <p><br /> <strong>All queued up</strong></p> <p>Another innovation was creating queues at congested accounts. If an account can’t handle incoming transactions that require it to send money, instead of rejecting them, it queues them up. Then, it waits for any transactions that will replenish its funds — within a reasonable time frame — to be able to process those transactions.</p> <p>“If you’re waiting on a queue, but I send you funds within the next second, you can then use any of those funds to send your waiting transactions,” Sivaraman says.</p> <p>The researchers also adopted an algorithm —&nbsp;built by Alizadeh and other researchers&nbsp;— that monitors data center congestion to identify queueing delays at congested accounts. This helps control the rate of transactions. Say user A sends funds to user C through user B, which has a long queue. The receiver C sends the sender A, along with the payment confirmation, one bit of information representing the transaction’s wait time at user B. If it’s too long, user A routes fewer transactions through user B. As the queueing time decreases, account A routes more transactions through B. In this manner, by monitoring the queues alone, Spider is able to ensure that the rate of transactions is both balanced and as high as possible.</p> <p>Ultimately, the more balanced the routing of PCNs, the smaller the capacity required — meaning, overall funds across all joint accounts — for high transaction throughput. In PCN simulations, Spider processed 95 percent of all transactions using only 25 percent of the capacity needed in traditional schemes.</p> <p>The researchers also ran tests on tricky transactions called “DAGs,” which are one-directional payments where one user inevitably runs out of funds and needs to rebalance on the blockchain. A key metric for the performance of PCNs on DAG transactions is the number of off-chain transactions enabled for each transaction on the blockchain. In this regard, Spider is able to process eight times as many off-chain transactions for each transaction on-chain. In contrast, traditional schemes only support twice as many off-chain transactions.</p> <p>“Even with extremely frequent rebalancing, traditional schemes can’t process all DAG transactions. But with very low-frequency rebalancing, Spider can complete them all,” Sivaraman says.</p> <p>Next, the researchers are making Spider more robust to DAG transactions, which can cause bottlenecks. They’re also exploring data privacy issues and ways to incentivize users to use Spider.</p> Spider, a new cryptocurrency-routing scheme, splits each full transaction into smaller “packets” that are sent across different channels at different rates.Image: Chelsea Turner, MITResearch, Computer science and technology, Algorithms, Cyber security, Technology and society, Networks, Finance, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering Demystifying artificial intelligence Doctoral candidate Natalie Lao wants to show that anyone can learn to use AI to make a better world. Wed, 29 Jan 2020 13:55:01 -0500 Kim Martineau | MIT Quest for Intelligence <p><a href="">Natalie Lao</a>&nbsp;was set on becoming an electrical engineer, like her parents, until she stumbled on course 6.S192 (<a href="">Making Mobile Apps</a>), taught by Professor <a href="">Hal Abelson</a>. Here was a blueprint for turning a smartphone into a tool for finding clean drinking water, or sorting pictures of faces, or doing just about anything. “I thought, I wish people knew building tech could be like this,” she said on a recent afternoon, taking a break from writing her dissertation.</p> <p>After shifting her focus as an MIT undergraduate&nbsp;to computer science, Lao joined Abelson’s lab, which was busy spreading its&nbsp;<a href="">App Inventor</a>&nbsp;platform and do-it-yourself philosophy to high school students around the world. App Inventor set Lao on her path to making it easy for anyone, from farmers to factory workers, to understand AI, and use it to improve their lives. Now in the third and final year of her PhD at MIT, Lao is also the co-founder of an AI startup to fight fake news, and the co-producer of a series of machine learning tutorials. It’s all part of her mission to help people find the creator and free thinker within.&nbsp;</p> <p>“She just radiates optimism and enthusiasm,” says Abelson, the Class of 1922 Professor in the Department of Electrical Engineering and Computer Science (EECS). “She’s a natural leader who knows how to get people excited and organized.”&nbsp;</p> <p>Lao was immersed in App Inventor, building modules to teach students to build face recognition models and store data in the cloud. Then, in 2016, the surprise election of Donald Trump to U.S. president forced her to think more critically about technology. She was less upset by Trump the politician as by revelations that social media-fueled propaganda and misinformation had tilted the race in Trump’s favor.</p> <p>When a friend, Elan Pavlov, a then-EECS postdoc, approached Lao about an idea he had for building a platform to combat fake news she was ready to dive in. Having grown up in rural, urban, and suburban parts of Tennessee and Ohio, Lao was used to hearing a range of political views. But now, social platforms were filtering those voices, and amplifying polarizing, often inaccurate, content. Pavlov’s idea stood out for its focus on identifying the people (and bots) spreading misinformation and disinformation, rather than the content itself.&nbsp;</p> <p>Lao recruited two friends,&nbsp;<a href="">Andrew Tsai</a>&nbsp;and&nbsp;<a href="">Keertan Kini</a>, to help build out the platform. They would later name it&nbsp;<a href="">HINTS</a>, or Human Interaction News Trustworthiness System, after an early page-ranking algorithm called HITS.&nbsp;</p> <p>In a demo last fall, Lao and Tsai highlighted a network of Twitter accounts that had shared conspiracy theories tied to the murder of Saudi journalist Jamal Khashoggi under the hashtag #khashoggi. When they looked at what else those accounts had shared, they found streams of other false and misleading news. Topping the list was the incorrect claim that then-U.S. Congressman Beto O’Rourke had funded a caravan of migrants headed for the U.S. border.</p> <p>The HINTS team hopes that by flagging the networks that spread fake news, social platforms will move faster to remove fake accounts and contain the propagation of misinformation.</p> <p>“Fake news doesn’t have any impact in a vacuum — real people have to read it and share it,” says Lao. “No matter what your political views, we’re concerned about facts and democracy. There’s fake news being pushed on both sides and it’s making the political divide even worse.”</p> <p>The HINTS team is now working with its first client, a media analytics firm based in Virginia. As CEO, Lao has called on her experience as a project manager from internships at GE, Google, and Apple, where, most recently, she led the rollout of the iPhone XR display screen. “I’ve never met anyone as good at managing people and tech,” says Tsai, an EECS master’s student who met Lao as a lab assistant for Abelson’s course 6.S198 (<a href="">Deep Learning Practicum</a>), and is now CTO of HINTS.</p> <p>As HINTS was getting off the ground, Lao co-founded a second startup,&nbsp;<a href="">ML Tidbits</a>, with EECS graduate student&nbsp;<a href="">Harini Suresh</a>. While learning to build AI models, both women grew frustrated by the tutorials on YouTube. “They were full of formulas, with very few pictures,” she says. “Even if the material isn’t that hard, it looks hard!”&nbsp;</p> <p>Convinced they could do better, Lao and Suresh reimagined a menu of intimidating topics like unsupervised learning and model-fitting as a set of inviting side dishes. Sitting cross-legged on a table, as if by a cozy fire, Lao and Suresh put viewers at ease with real-world anecdotes, playful drawings, and an engaging tone. Six more videos, funded by&nbsp;<a href="">MIT Sandbox</a>&nbsp;and the&nbsp;MIT-IBM Watson AI Lab, are planned for release this spring.&nbsp;</p> <p>If her audience learns one thing from ML Tidbits, Lao says, she hopes it’s that anyone can learn the basic underpinnings of AI. “I want them to think, ‘Oh, this technology isn't just something that professional computer scientists or mathematicians can touch. I can learn it too. I can form educated opinions and join discussions about how it should be used and regulated.’ ”</p> A PhD student in the MIT Department of Electrical Engineering and Computer Science, Natalie Lao has co-founded startups aimed at democratizing artificial intelligence and using AI to protect democracy by fighting false and misleading information.Photo: Andrew TsaiQuest for Intelligence, MIT-IBM Watson AI Lab, School of Engineering, Computer science and technology, Technology and society, STEM education, K-12 education, Apps, Invention, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical engineering and computer science (EECS), Artificial intelligence, Graduate, postdoctoral, Profile, education, Education, teaching, academics Accelerating the pace of engineering The 2019-20 School of Engineering MathWorks Fellows are using MATLAB and Simulink to advance discovery and innovation across disciplines. Tue, 28 Jan 2020 17:00:01 -0500 Lori LoTurco | School of Engineering <p>Founded in 1984 by Jack Little ’78 and Cleve Moler, MathWorks was built on the premise of providing engineers and scientists with more powerful and productive computation environments. In 1985, the company sold its very first order&nbsp;— 10 copies of its first product, MATLAB — to MIT.</p> <p>Decades later, engineers across MIT and around the world consistently rely on MathWorks products to accelerate the pace of discovery, innovation, and development in automotive, aerospace, electronics, biotech-pharmaceutical, and other industries.&nbsp;MathWorks’ products and support have had a significant impact on <em>MITx,</em> OpenCourseWare, and MIT’s digital learning efforts across campus, including the Department of Mathematics, one of the School of Engineering’s closest collaborators in the use of digital learning tools and educational technologies.</p> <p>“We have a strong belief in the importance of engineers and scientists,” says Little. “They act to increase human knowledge and profoundly improve our standard of living. We create products like MATLAB and Simulink to help them do their best work.”</p> <p>As the language of technical computing, MATLAB is a programming environment for algorithm development, data analysis, visualization, and numeric computation. It is used extensively by faculty, students, and researchers across MIT and by over 4 million users in industry, government, and academia in 185 countries.</p> <p>Simulink is a block diagram environment for simulation and model-based design of multidomain and embedded engineering systems, including automatic code generation, verification, and validation. It is used heavily in automotive, aerospace, and other applications that design complex real-time systems.</p> <p>This past summer, MathWorks celebrated 35 years of accelerating the pace of engineering and science. Shortly following this milestone, MathWorks awarded 11 engineering fellowships to graduate students within the School of Engineering who are active users of MATLAB or Simulink. The fellows are using the programs to advance discovery and innovation across disciplines.</p> <p>“PhD fellowships are an investment in the world’s long-term future, and there are few investments more valuable than that,” says Little.</p> <p>The 2019-20 MathWorks fellows are:</p> <p><a href="">Pasquale Antonante</a> is a PhD student in the Department of Aeronautics and Astronautics. He uses MATLAB and Simulink to build tools that make robots more accurate.</p> <p><a href="">Alireza Fallah</a> is a PhD student in the Department of Electrical Engineering and Computer Science. He uses Matlab and Symbolic Math Toolbox to develop better machine-learning algorithms.</p> <p><a href="">James Gabbard</a> is a SM/PhD student in the Department of Mechanical Engineering. He uses MATLAB to model fluids and materials.</p> <p><a href="">Nicolas Meirhaeghe</a><strong> </strong>is a PhD student in medical engineering and medical physics in the Bioastronautics Training Program at Harvard-MIT Division of Health Sciences and Technology. He uses MATLAB to visualize activity in the brain and understand how it is related to an individual’s behavior.</p> <p><a href="">Caroline Nielsen</a> is a PhD student in the Department of Chemical Engineering. She uses MATLAB to implement and test new applications of non-smooth analysis. She also intends to use MATLAB to in the next phase of her research, developing methods to simultaneously optimize for minimal resource use and operating costs.</p> <p><a href="">Bauyrzhan Primkulov</a><strong> </strong>is a PhD student in the Department of Civil and Environmental Engineering. He uses MATLAB to build computational models and explore how fluids interact in porous materials.</p> <p><a href="">Kate Reidy</a><strong> </strong>is a PhD student in the Department of Materials Science and Engineering. She studies how 2D materials — only a single atom thick — can be combined with 3D materials, and uses MATLAB to analyze the properties of different materials.</p> <p><a href="">Isabelle Su</a><strong> </strong>is a PhD student in civil and environmental engineering. She builds computational models with MATLAB to understand the mechanical properties of spider webs.</p> <p><a href="">Joy Zeng</a><strong> </strong>is a PhD student in chemical engineering. Her research is focused on the electrochemical transformation of carbon dioxide to fuels and commodity chemicals. She uses MATLAB to model chemical reactions.</p> <p><a href="">Benjamin "Jiahong" Zhang</a><strong> </strong>is a PhD student in computational science and engineering. He uses MATLAB to prototype new methods for rare event simulation, finding new methods by leveraging mathematical principles used in proofs and re-purposing them for computation.</p> <p><a href="">Paul Zhang</a><strong> </strong>is a PhD student in electrical engineering and computer science. He uses MATLAB to develop algorithms with applications in meshing — the use of simple shapes to study complex ones.</p> <p>For MathWorks, fostering engineering education is a priority, so when deciding where to focus philanthropic support, MIT — its very first customer — was an obvious choice.</p> <p>“We are so humbled by MathWorks' generosity, and their continued support of our engineering students through these fellowships,” says Anantha Chandrakasan, dean of the School of Engineering. “Our relationship with MathWorks is one that we revere — they have developed products that foster research and advancement across many disciplines, and through their support our students launch discoveries and innovation that align with MathWorks’ mission.”</p> MathWorks fellows with Anantha Chandrakasan (back row, center), dean of the MIT School of Engineering. Not pictured: Fellows Pasquale Antonante, Alireza Fallah, and Kate Reidy.Photo: David DegnerSchool of Engineering, MITx, OpenCourseWare, Mathematics, Electrical engineering and computer science (EECS), Mechanical engineering, Chemical engineering, Civil and environmental engineering, Awards, honors and fellowships, Harvard-MIT Health Sciences and Technology, Alumni/ae, Startups, Aeronautical and astronautical engineering, DMSE, Computer science and technology, School of Science A trapped-ion pair may help scale up quantum computers Qubits made from strontium and calcium ions can be precisely controlled by technology that already exists. Tue, 28 Jan 2020 15:20:01 -0500 Kylie Foy | Lincoln Laboratory <p>Of the many divergent approaches to building a practical quantum computer, one of the most promising paths leads toward ion traps. In these traps, single ions are held still and serve as the basic units of data, or qubits, of the computer. With the help of lasers, these qubits interact with each other to perform logic operations.&nbsp; &nbsp;&nbsp;</p> <p>Lab experiments with small numbers of trapped ions work well, but a lot of work remains in figuring out the basic parts of a scalable ion-trap quantum computer. What kind of ions should be used? What technologies will be able to control, manipulate, and read out the quantum information stored in those ions?</p> <p>Toward answering these questions, MIT Lincoln Laboratory researchers have turned to a promising pair: ions of calcium (Ca) and strontium (Sr). In a <a href="" target="_blank">paper published</a> in <em>npj Quantum Information</em>, the team describes using these ions to perform quantum logic operations and finds them to be favorable for multiple quantum computing architectures. Among their advantages, these ions can be manipulated by using visible and infrared light, as opposed to ultraviolet, which is needed by many types of ions being used in experiments. Unlike for ultraviolet light, technology that would be able to deliver visible and infrared light to a large array of trapped ions already exists.</p> <p>“What kind of quantum information processing architecture is feasible for trapped ions? If it turns out it will be much more difficult to use a certain ion species, it would be important to know early on, before you head far down that path,” says <a href="" target="_blank">John Chiaverini</a>, senior staff in the <a href="" target="_blank">Quantum Information and Integrated Nanosystems Group</a>. “We believe we won't have to invent a whole new engineered system, and not solve a whole new group of problems, using these ion species.”</p> <p><strong>Cold and calculating</strong></p> <p>To trap ions, scientists start with a steel vacuum chamber, housing electrodes on a chip that is chilled to nearly 450 degrees below zero Fahrenheit. Ca and Sr atoms stream into the chamber. Multiple lasers knock electrons from the atoms, turning the Ca and Sr atoms into ions. The electrodes generate electric fields that catch the ions and hold them 50 micrometers above the surface of the chip. Other lasers cool the ions, maintaining them in the trap.&nbsp;</p> <p>Then, the ions are brought together to form a Ca+/Sr+ crystal. Each type of ion plays a unique role in this partnership. The Sr ion houses the qubit for computation. To solve a problem, a quantum computer wants to know the energy level, or quantum state, of an ion's outermost electron. The electron could be in its lowest energy level or ground state (denoted |1⟩), some higher energy level or excited state (denoted |0), or both states at once. This strange ability to be in multiple states simultaneously is called superposition, and it is what gives quantum computers the power to try out many possible solutions to a problem at once.&nbsp;</p> <p>But superposition is hard to maintain. Once a qubit is observed — for example, by using laser light to see what energy level its electron is in — it collapses into either a 1 or 0. To make a practical quantum computer, scientists need to devise ways of measuring the states of only a subset of the computer's qubits while not disturbing the entire system.</p> <p>This need brings us back to the role of the Ca ion — the helper qubit. With a similar mass to the Sr ion, it takes away extra energy from the Sr ion to keep it cool and help it maintain its quantum properties. Laser pulses then nudge the two ions into entanglement, forming a gate through which the Sr ion can transfer its quantum information to the Ca ion.</p> <p>“When two qubits are entangled, their states are dependent on each other. They are so-called 'spookily correlated,'” Chiaverini said. This correlation means that reading out the state of one qubit tells you the state of the other. To read out this state, the scientists interrogate the Ca ion with a laser at a wavelength that only the Ca ion's electron will interact with, leaving the Sr ion unaffected. If the electron is in the ground state it will emit photons, which are collected by detectors. The ion will remain dark if in an excited metastable state.&nbsp;</p> <p>“What's nice about using this helper ion for reading out is that we can use wavelengths that don't impact the computational ions around it; the quantum information stays healthy. So, the helper ion does dual duty; it removes thermal energy from the Sr ion and has low crosstalk when I want to read out just that one qubit,” says Colin Bruzewicz, who built the system and led the experimentation.</p> <p>The fidelity of the Ca+/Sr+ entanglement in their experiment was 94 percent. Fidelity is the probability that the gate between the two qubits produced the quantum state it was expected to — that the entanglement worked. This system's fidelity is high enough to demonstrate the basic quantum logic functionality, but not yet high enough for a fully error-corrected quantum computer. The team also entangled ions in different configurations, such as the two ions on the ends of a Sr+/Ca+/Sr+ string, with similar fidelity.</p> <p><strong>A wavelength match</strong></p> <p>Currently, the ion-trap setup is large and choreographs the use of 12 different-colored lasers. These lasers stream through windows in the cryogenic chamber and are aimed to hit the ions. A practical quantum computer — one that can solve problems better than a classical computer — will need an array of thousands or even millions of ions. In that scenario, it would be practically impossible to hit precisely the right ions while not disturbing the quantum states in neighboring ions. Lincoln Laboratory researchers have been working for the past several years on a way deliver the lasers up through “gratings” in the chip the ions hover above. This <a href="" target="_blank">integrated-photonic chip</a> both simplifies the setup and ensures that the right laser hits the intended target. Last year, the team achieved the first-ever successful demonstration of a low-loss, integrated photonics platform with light delivery ranging from the visible to the infrared spectrum.</p> <p>Conveniently, the wavelengths required for cooling Ca and Sr ions, entangling them, and reading them out all fall within this same spectrum. This overlap simplifies the system's laser requirements, unlike other pairings of ions that each require widely different wavelengths. “These ions lend themselves to being used with integrated photonics. They're a wavelength match. It makes engineering sense to use them,” Bruzewicz says.</p> <p>In addition, many types of trapped ions that quantum scientists are exploring need ultraviolet light for excitation. But ultraviolet light can be difficult to work with. Waveguides and other photonic devices that carry the light to the ions tend to lose some of the light on the way. Delivering ultraviolet light to large-scale trapped-ion systems would require a lot more power, or the engineering of new materials that experience less loss.&nbsp;</p> <p>“It's much simpler working with this light than the ultraviolet, especially when you start to put a lot of these ions together. But that's the challenge — no one actually knows what kind of architecture will enable quantum computation that’s helpful. The jury is still out,” Chiaverini reflects. “In this instance, we are thinking about what might be most advantageous to scaling up a system. These ions are very amenable to that.”</p> An ion-trap chip (at center) is used to hold two calcium and strontium ions still as the qubits they house become entangled. The inset shows a magnified, false-color image of light scattering from each ion in the trap as they are laser cooled.Image: Lincoln LaboratoryLincoln Laboratory, Quantum computing, Photonics, Research, Computer science and technology, Computing Using artificial intelligence to enrich digital maps Model tags road features based on satellite images, to improve GPS navigation in places with limited map data. Thu, 23 Jan 2020 00:00:00 -0500 Rob Matheson | MIT News Office <p>A model invented by researchers at MIT and Qatar Computing Research Institute (QCRI) that uses satellite imagery to tag road features in digital maps could help improve GPS navigation.&nbsp;&nbsp;</p> <p>Showing drivers more details about their routes can often help them navigate in unfamiliar locations. Lane counts, for instance, can enable a GPS system to warn drivers of diverging or merging lanes. Incorporating information about parking spots can help drivers plan ahead, while mapping bicycle lanes can help cyclists negotiate busy city streets. Providing updated information on road conditions can also improve planning for disaster relief.</p> <p>But creating detailed maps is an expensive, time-consuming process done mostly by big companies, such as Google, which send<s>s</s> vehicles around with cameras strapped to their hoods to capture video and images of an area’s roads. Combining that with other data can create accurate, up-to-date maps. Because this process is expensive, however, some parts of the world are ignored.</p> <p>A solution is to unleash machine-learning models on satellite images — which are easier to obtain and updated fairly regularly — to automatically tag road features. But roads can be occluded by, say, trees and buildings, making it a challenging task. In a <a href="">paper</a> being presented at the Association for the Advancement of Artificial Intelligence conference, the MIT and QCRI researchers describe “RoadTagger,” which uses a combination of neural network architectures to automatically predict the number of lanes and road types (residential or highway) behind obstructions.</p> <p>In testing RoadTagger on occluded roads from digital maps of 20 U.S. cities, the model counted lane numbers with 77 percent accuracy and inferred road types with 93 percent accuracy. The researchers are also planning to enable RoadTagger to predict other features, such as parking spots and bike lanes.</p> <p>“Most updated digital maps are from places that big companies care the most about. If you’re in places they don’t care about much, you’re at a disadvantage with respect to the quality of map,” says co-author Sam Madden, a professor in the Department of Electrical Engineering and Computer Science (EECS) and a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “Our goal is to automate the process of generating high-quality digital maps, so they can be available in any country.”</p> <p>The paper’s co-authors are CSAIL graduate students Songtao He, Favyen Bastani, and Edward Park; EECS undergraduate student Satvat Jagwani; CSAIL professors Mohammad Alizadeh and Hari Balakrishnan; and QCRI researchers Sanjay Chawla, Sofiane Abbar, and Mohammad Amin Sadeghi.</p> <p><strong>Combining CNN and GNN</strong></p> <p>Qatar, where QCRI is based, is “not a priority for the large companies building digital maps,” Madden says. Yet, it’s constantly building new roads and improving old ones, especially in preparation for hosting the 2022 FIFA World Cup.</p> <p>“While visiting Qatar, we’ve had experiences where our Uber driver can’t figure out how to get where he’s going, because the map is so off,” Madden says. “If navigation apps don’t have the right information, for things such as lane merging, this could be frustrating or worse.”</p> <p>RoadTagger relies on a novel combination of a convolutional neural network (CNN) — commonly used for images-processing tasks — and a graph neural network (GNN). GNNs model relationships between connected nodes in a graph and have become popular for analyzing things like social networks and molecular dynamics. The model is “end-to-end,” meaning it’s fed only raw data and automatically produces output, without human intervention.</p> <p>The CNN takes as input raw satellite images of target roads. The GNN breaks the road into roughly 20-meter segments, or “tiles.” Each tile is a separate graph node, connected by lines along the road. For each node, the CNN extracts road features and shares that information with its immediate neighbors. Road information propagates along the whole graph, with each node receiving some information about road attributes in every other node. If a certain tile is occluded in an image, RoadTagger uses information from all tiles along the road to predict what’s behind the occlusion.</p> <p>This combined architecture represents a more human-like intuition, the researchers say. Say part of a four-lane road is occluded by trees, so certain tiles show only two lanes. Humans can easily surmise that a couple lanes are hidden behind the trees. Traditional machine-learning models — say, just a CNN — extract features only of individual tiles and most likely predict the occluded tile is a two-lane road.</p> <p>“Humans can use information from adjacent tiles to guess the number of lanes in the occluded tiles, but networks can’t do that,” He says. “Our approach tries to mimic the natural behavior of humans, where we capture local information from the CNN and global information from the GNN to make better predictions.”</p> <p><strong>Learning weights&nbsp;&nbsp;&nbsp; </strong></p> <p>To train and test RoadTagger, the researchers used a real-world map dataset, called OpenStreetMap, which lets users edit and curate digital maps around the globe. From that dataset, they collected confirmed road attributes from 688 square kilometers of maps of 20 U.S. cities — including Boston, Chicago, Washington, and Seattle. Then, they gathered the corresponding satellite images from a Google Maps dataset.</p> <p>In training, RoadTagger learns weights — which assign varying degrees of importance to features and node connections —&nbsp;of the CNN and GNN. The CNN extracts features from pixel patterns of tiles and the GNN propagates the learned features along the graph. From randomly selected subgraphs of the road, the system learns to predict the road features at each tile. In doing so, it automatically learns which image features are useful and how to propagate those features along the graph. For instance, if a target tile has unclear lane markings, but its neighbor tile has four lanes with clear lane markings and shares the same road width, then the target tile is likely to also have four lanes. In this case, the model automatically learns that the road width is a useful image feature, so if two adjacent tiles share the same road width, they’re likely to have the same lane count.</p> <p>Given a road not seen in training from OpenStreetMap, the model breaks the road into tiles and uses its learned weights to make predictions. Tasked with predicting a number of lanes in an occluded tile, the model notes that neighboring tiles have matching pixel patterns and, therefore, a high likelihood to share information. So, if those tiles have four lanes, the occluded tile must also have four.</p> <p>In another result, RoadTagger accurately predicted lane numbers in a dataset of synthesized, highly challenging road disruptions. As one example, an overpass with two lanes covered a few tiles of a target road with four lanes. The model detected mismatched pixel patterns of the overpass, so it ignored the two lanes over the covered tiles, accurately predicting four lanes were underneath.</p> <p>The researchers hope to use RoadTagger to help humans rapidly validate and approve continuous modifications to infrastructure in datasets such as OpenStreetMap, where many maps don’t contain lane counts or other details. A specific area of interest is Thailand, Bastani says, where roads are constantly changing, but there are few if any updates in the dataset.</p> <p>“Roads that were once labeled as dirt roads have been paved over so are better to drive on, and some intersections have been completely built over. There are changes every year, but digital maps are out of date,” he says. “We want to constantly update such road attributes based on the most recent imagery.”</p> An AI model developed at MIT and Qatar Computing Research Institute that uses only satellite imagery to automatically tag road features in digital maps could improve GPS navigation, especially in countries with limited map data.Image: Google Maps/MIT NewsResearch, Computer science and technology, Algorithms, Transportation, Cities, Automobiles, Machine learning, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering, Artificial intelligence How to verify that quantum chips are computing correctly A new method determines whether circuits are accurately executing complex operations that classical computers can’t tackle. Mon, 13 Jan 2020 10:59:03 -0500 Rob Matheson | MIT News Office <p>In a step toward practical quantum computing, researchers from MIT, Google, and elsewhere have designed a system that can verify when quantum chips have accurately performed complex computations that classical computers can’t.</p> <p>Quantum chips perform computations using quantum bits, called “qubits,” that can represent the two states corresponding to classic binary bits — a 0 or 1 — or a “quantum superposition” of both states simultaneously. The unique superposition state can enable quantum computers to solve problems that are practically impossible for classical computers, potentially spurring breakthroughs in material design, drug discovery, and machine learning, among other applications.</p> <p>Full-scale quantum computers will require millions of qubits, which isn’t yet feasible. In the past few years, researchers have started developing “Noisy Intermediate Scale Quantum” (NISQ) chips, which contain around 50 to 100 qubits. That’s just enough to demonstrate “quantum advantage,” meaning the NISQ chip can solve certain algorithms that are intractable for classical computers. Verifying that the chips performed operations as expected, however, can be very inefficient. The chip’s outputs can look entirely random, so it takes a long time to simulate steps to determine if everything went according to plan.</p> <p>In a paper published today in <em>Nature Physics</em>, the researchers describe a novel protocol to efficiently verify that an NISQ chip has performed all the right quantum operations. They validated their protocol on a notoriously difficult quantum problem running on custom quantum photonic chip.</p> <p>“As rapid advances in industry and academia bring us to the cusp of quantum machines that can outperform classical machines, the task of quantum verification becomes time critical,” says first author Jacques Carolan, a postdoc in the Department of Electrical Engineering and Computer Science (EECS) and the Research Laboratory of Electronics (RLE). “Our technique provides an important tool for verifying a broad class of quantum systems. Because if I invest billions of dollars to build a quantum chip, it sure better do something interesting.”</p> <p>Joining Carolan on the paper are researchers from EECS and RLE at MIT, as well from the Google Quantum AI Laboratory, Elenion Technologies, Lightmatter, and Zapata Computing. &nbsp;</p> <p><strong>Divide and conquer</strong></p> <p>The researchers’ work essentially traces an output quantum state generated by the quantum circuit back to a known input state. Doing so reveals which circuit operations were performed on the input to produce the output. Those operations should always match what researchers programmed. If not, the researchers can use the information to pinpoint where things went wrong on the chip.</p> <p>At the core of the new protocol, called “Variational Quantum Unsampling,” lies a “divide and conquer” approach, Carolan says, that breaks the output quantum state into chunks. “Instead of doing the whole thing in one shot, which takes a very long time, we do this unscrambling layer by layer. This allows us to break the problem up to tackle it in a more efficient way,” Carolan says.</p> <p>For this, the researchers took inspiration from neural networks — which solve problems through many layers of computation —&nbsp;to build a novel “quantum neural network” (QNN), where each layer represents a set of quantum operations.</p> <p>To run the QNN, they used traditional silicon fabrication techniques to build a 2-by-5-millimeter NISQ chip with more than 170 control parameters — tunable circuit components that make manipulating the photon path easier. Pairs of photons are generated at specific wavelengths from an external component and injected into the chip. The photons travel through the chip’s phase shifters — which change the path of the photons — interfering with each other. This produces a random quantum output state —&nbsp;which represents what would happen during computation. The output is measured by an array of external photodetector sensors.</p> <p>That output is sent to the QNN. The first layer uses complex optimization techniques to dig through the noisy output to pinpoint the signature of a single photon among all those scrambled together. Then, it “unscrambles” that single photon from the group to identify what circuit operations return it to its known input state. Those operations should match exactly the circuit’s specific design for the task. All subsequent layers do the same computation — removing from the equation any previously unscrambled photons — until all photons are unscrambled.</p> <p>As an example, say the input state of qubits fed into the processor was all zeroes. The NISQ chip executes a bunch of operations on the qubits to generate a massive, seemingly randomly changing number as output. (An output number will constantly be changing as it’s in a quantum superposition.) The QNN selects chunks of that massive number. Then, layer by layer, it determines which operations revert each qubit back down to its input state of zero. If any operations are different from the original planned operations, then something has gone awry. Researchers can inspect any mismatches between the expected output to input states, and use that information to tweak the circuit design.</p> <p><strong>Boson “unsampling”</strong></p> <p>In experiments, the team successfully ran a popular computational task used to demonstrate quantum advantage, called “boson sampling,” which is usually performed on photonic chips. In this exercise, phase shifters and other optical components will manipulate and convert a set of input photons into a different quantum superposition of output photons. Ultimately, the task is to calculate the probability that a certain input state will match a certain output state. That will essentially be a sample from some probability distribution.</p> <p>But it’s nearly impossible for classical computers to compute those samples, due to the unpredictable behavior of photons. It’s been theorized that NISQ chips can compute them fairly quickly. Until now, however, there’s been no way to verify that quickly and easily, because of the complexity involved with the NISQ operations and the task itself.</p> <p>“The very same properties which give these chips quantum computational power makes them nearly impossible to verify,” Carolan says.</p> <p>In experiments, the researchers were able to “unsample” two photons that had run through the boson sampling problem on their custom NISQ chip — and in a fraction of time it would take traditional verification approaches.</p> <p>“This is an excellent paper that employs a nonlinear quantum neural network to learn the unknown unitary operation performed by a black box,” says Stefano Pirandola, a professor of computer science who specializes in quantum technologies at the University of York. “It is clear that this scheme could be very useful to verify the actual gates that are performed by a quantum circuit — [for example] by a NISQ processor. From this point of view, the scheme serves as an important benchmarking tool for future quantum engineers. The idea was remarkably implemented on a photonic quantum chip.”</p> <p>While the method was designed for quantum verification purposes, it could also help capture useful physical properties, Carolan says. For instance, certain molecules when excited will vibrate, then emit photons based on these vibrations. By injecting these photons into a photonic chip, Carolan says, the unscrambling technique could be used to discover information about the quantum dynamics of those molecules to aid in bioengineering molecular design. It could also be used to unscramble photons carrying quantum information that have accumulated noise by passing through turbulent spaces or materials. &nbsp;</p> <p>“The dream is to apply this to interesting problems in the physical world,” Carolan says.</p> Researchers from MIT, Google, and elsewhere have designed a novel method for verifying when quantum processors have accurately performed complex computations that classical computers can’t. They validate their method on a custom system (pictured) that’s able to capture how accurately a photonic chip (“PNP”) computed a notoriously difficult quantum problem.Image: Mihika PrabhuResearch, Computer science and technology, Algorithms, Light, Photonics, Nanoscience and nanotechnology, Quantum computing, electronics, Machine learning, Artificial intelligence, Research Laboratory of Electronics, Electrical Engineering & Computer Science (eecs), School of Engineering Sending clearer signals Associate Professor Yury Polyanskiy is working to keep data flowing as the “internet of things” becomes a reality. Sat, 11 Jan 2020 23:59:59 -0500 Rob Matheson | MIT News Office <p>In the secluded Russian city where Yury Polyanskiy grew up, all information about computer science came from the outside world. Visitors from distant Moscow would occasionally bring back the latest computer science magazines and software CDs to Polyanskiy’s high school for everyone to share.</p> <p>One day while reading a borrowed <em>PC World</em> magazine in the mid-1990s, Polyanskiy learned about a futuristic concept: the World Wide Web.</p> <p>Believing his city would never see such wonders of the internet, he and his friends built their own. Connecting an ethernet cable between two computers in separate high-rises, they could communicate back and forth. Soon, a handful of other kids asked to be connected to the makeshift network.</p> <p>“It was a pretty challenging engineering problem,” recalls Polyanskiy, an associate professor of electrical engineering and computer science at MIT, who recently earned tenure. “I don’t remember exactly how we did it, but it took us a whole day. You got a sense of just how contagious the internet could be.”</p> <p>Thanks to the then-recent fall of the Iron Curtain, Polyanskiy’s family did eventually connect to the internet. Soon after, he became interested in computer science and then information theory, the mathematical study of storing and transmitting data. Now at MIT, his most exciting work centers on preventing major data-transmission issues with the rise of the “internet of things” (IoT). Polyanskiy is a member of the of the Laboratory for Information and Decision Systems, the Institute for Data, Systems, and Society, and the&nbsp;Statistics and Data Science Center.</p> <p>Today, people carry around a smartphone and maybe a couple smart devices. Whenever you watch a video on your smartphone, for example, a nearby cell tower assigns you an exclusive chunk of the wireless spectrum for a certain time. It does so for everyone, making sure the data never collide.</p> <p>The number IoT devices is expected to explode, however. People may carry dozens of smart devices; all delivered packages may have tracking sensors; and smart cities may implement thousands of connected sensors in their infrastructure. Current systems can’t divvy up the spectrum effectively to stop data from colliding. That will slow down transmission speeds and make our devices consume much more energy in sending and resending data.</p> <p>“There may soon be a hundredfold explosion of devices connected to the internet, which is going to clog the spectrum, and there will be no way to ensure interference-free transmission. Entirely new access approaches will be needed,” Polyanskiy says. “It’s the most exciting thing I’m working on, and it’s surprising that no one is talking much about it.”</p> <p><strong>From Russia, with love of computer science</strong></p> <p>Polyanskiy grew up in a place that translates in English to “Rainbow City,” so named because it was founded as a site to develop military lasers. Surrounded by woods, the city had a population of about 15,000 people, many of them engineers.</p> <p>In part, that environment got Polyanskiy into computer science. At the age of 12, he started coding —&nbsp;“and for profit,” he says. His father was working for an engineering firm, on a team that was programming controllers for oil pumps. When the lead programmer took another position, they were left understaffed. “My father was discussing who can help. I was sitting next to him, and I said, ‘I can help,’” Polyanskiy says. “He first said no, but I tried it and it worked out.”</p> <p>Soon after, his father opened his own company for designing oil pump controllers and brought Polyanskiy on board while he was still in high school. The business gained customers worldwide. He says some of the controllers he helped program are still being used today.</p> <p>Polyanskiy earned his bachelor’s in physics from the Moscow Institute of Physics and Technology, a top university worldwide for physics research. But then, interested in pursuing electrical engineering for graduate school, he applied to programs in the U.S. and was accepted to Princeton University.</p> <p>In 2005, he moved to the U.S. to attend Princeton, which came with cultural shocks “that I still haven’t recovered from,” Polyanskiy jokes. For starters, he says, the U.S. education system encourages interaction with professors. Also, the televisions, gaming consoles, and furniture in residential buildings and around campus were not placed under lock and key.</p> <p>“In Russia, everything is chained down,” Polyanskiy says. “I still can’t believe U.S. universities just keep those things out in the open.”</p> <p>At Princeton, Polyanskiy wasn’t sure which field to enter. But when it came time to select, he asked one rather discourteous student about studying under a giant in information theory, Sergio Verdú. The student told Polyanskiy he wasn’t smart enough for Verdú — so Polyanskiy got defiant. “At that moment, I knew for certain that Sergio would be my number one pick,” Polyanskiy says, laughing. “When people say I can’t do something, that’s usually the best way to motivate me.”<br /> <br /> At Princeton, working under Verdú, Polyanskiy focused on a component of information theory that deals with how much redundancy to send with data. Each time data transmit, they are perturbed by some noise. Adding duplicate data means less data get lost in that noise. Researchers thus study the optimal amounts of redundancy to reduce signal loss but keep transmissions fast.</p> <p>In his graduate work, Polyanskiy pinpointed sweet spots for redundancy when transmitting hundreds or thousands of data bits in packets, which is mostly how data are transmitted online today.</p> <p><strong>Getting hooked</strong></p> <p>After earning his PhD in electrical engineering from Princeton, Polyanskiy finally did come to MIT, his “dream school,” in 2011, but as a professor. MIT had helped pioneer some information theory research and introduced the first college courses in the field.</p> <p>Some call information theory “a green island,” he says, “because it’s hard to get into but once you’re there, you’re very happy. And information theorists can be seen as snobby.” &nbsp;When he came to MIT, Polyanskiy says, he was narrowly focused on his work. But he experienced yet another cultural shock — this time in a collaborative and bountiful research culture.</p> <p>MIT researchers are constantly presenting at conferences, holding seminars, collaborating, and “working on about 20 projects in parallel,” Polyanskiy says. “I was hesitant that I could do quality research like that, but then I got hooked. I became more broad-minded, thanks to MIT’s culture of drinking from a fire hose. There’s so much going on that eventually you get addicted to learning fields that are far away from you own interests.”</p> <p>In collaboration with other MIT researchers, Polyanskiy’s group now focuses on finding ways to split up the spectrum in the coming IoT age. So far, his group has mathematically proven that the systems in use today do not have the capabilities and energy to do so. They’ve also shown what types of alternative transmission systems will and won’t work.</p> <p>Inspired by his own experiences, Polyanskiy likes to give his students “little hooks,” tidbits of information about the history of scientific thought surrounding their work and about possible future applications. One example is explaining philosophies behind randomness to mathematics students who may be strictly deterministic thinkers. “I want to give them a little taste of something more advanced and outside scope of what they’re studying,” he says.</p> <p>After spending 14 years in the U.S., the culture has shaped the Russian native in certain ways. For instance, he’s accepted a more relaxed and interactive Western teaching style, he says. But it extends beyond the classroom, as well. Just last year, while visiting Moscow, Polyanskiy found himself holding a subway rail with both hands. Why is this strange? Because he was raised to keep one hand on the subway rail, and one hand over his wallet to prevent thievery. “With horror, I realized what I was doing,” Polyanskiy says, laughing. “I said, ‘Yury, you’re becoming a real Westerner.’”</p> Yury Polyanskiy Image: M. Scott BrauerResearch, Computer science and technology, Profile, Faculty, Wireless, internet of things, Data, Mobile devices, Laboratory for Information and Decision Systems (LIDS), IDSS, Electrical Engineering & Computer Science (eecs), School of Engineering How well can computers connect symptoms to diseases? Models that map these relationships based on patient data require fine-tuning for certain conditions, study shows. Thu, 09 Jan 2020 17:10:38 -0500 Rob Matheson | MIT News Office <p>A new MIT study finds “health knowledge graphs,” which show relationships between symptoms and diseases and are intended to help with clinical diagnosis, can fall short for certain conditions and patient populations. The results also suggest ways to boost their performance.</p> <p>Health knowledge graphs have typically been compiled manually by expert clinicians, but that can be a laborious process. Recently, researchers have experimented with automatically generating these knowledge graphs from patient data. The MIT team has been studying how well such graphs hold up across different diseases and patient populations.</p> <p>In a paper presented at the Pacific Symposium on Biocomputing 2020, the researchers evaluated automatically generated health knowledge graphs based on real datasets comprising more than 270,000 patients with nearly 200 diseases and more than 770 symptoms.</p> <p>The team analyzed how various models used electronic health record (EHR) data, containing medical and treatment histories of patients, to automatically “learn” patterns of disease-symptom correlations. They found that the models performed particularly poorly for diseases that have high percentages of very old or young patients, or high percentages of male or female patients — but that choosing the right data for the right model, and making other modifications, can improve performance.</p> <p>The idea is to provide guidance to researchers about the relationship between dataset size, model specification, and performance when using electronic health records to build health knowledge graphs. That could lead to better tools to aid physicians and patients with medical decision-making or to search for new relationships between diseases and symptoms.</p> <p>“In the last 10 years, EHR use has skyrocketed in hospitals, so there’s an enormous amount of data that we hope to mine to learn these graphs of disease-symptom relationships,” says first author Irene Y. Chen, a graduate student in the Department of Electrical Engineering and Computer Science (EECS). “It is essential that we closely examine these graphs, so that they can be used as the first steps of a diagnostic tool.”</p> <p>Joining Chen on the paper are Monica Agrawal, a graduate student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL); Steven Horng of Beth Israel Deaconess Medical Center (BIDMC); and EECS Professor David Sontag, who is a member of CSAIL and the Institute for Medical Engineering and Science, and head of the Clinical Machine Learning Group.</p> <p><strong>Patients and diseases</strong></p> <p>In health knowledge graphs, there are hundreds of nodes, each representing a different disease and symptom. Edges (lines) connect disease nodes, such as “diabetes,” with correlated symptom nodes, such as “excessive thirst.” Google famously launched its own version in 2015, which was manually curated by several clinicians over hundreds of hours and is considered the gold standard. When you Google a disease now, the system displays associated symptoms.</p> <p>In a 2017 <em>Nature Scientific Reports</em> paper, Sontag, Horng, and other researchers leveraged data from the same 270,00 patients in their current study — which came from the emergency department at BIDMC between 2008 and 2013 — to build health knowledge graphs. They used three model structures to generate the graphs, called logistic regression, naive Bayes, and noisy OR. Using data provided by Google, the researchers compared their automatically generated health knowledge graph with the Google Health Knowledge Graph (GHKG). The researchers’ graph performed very well.</p> <p>In their new work, the researchers did a rigorous error analysis to determine which specific patients and diseases the models performed poorly for. Additionally, they experimented with augmenting the models with more data, from beyond the emergency room.</p> <p>In one test, they broke the data down into subpopulations of diseases and symptoms. For each model, they looked at connecting lines between diseases and all possible symptoms, and compared that with the GHKG. In the paper, they sort the findings into the 50 bottom- and 50 top-performing diseases. Examples of low performers are polycystic ovary syndrome (which affects women), allergic asthma (very rare), and prostate cancer (which predominantly affects older men). High performers are the more&nbsp;common diseases and conditions, such as heart arrhythmia and plantar fasciitis, which is tissue swelling along the feet.</p> <p>They found the noisy OR model was the most robust against error overall for nearly all of the diseases and patients. But accuracy decreased among all models for patients that have many co-occurring diseases and co-occurring symptoms, as well as patients that are very young or above the age of 85. Performance also suffered for patient populations with very high or low percentages of any sex.</p> <p><a name="_gjdgxs"></a>Essentially, the researchers hypothesize, poor performance is caused by patients and diseases that have outlier predictive performance, as well as potential unmeasured confounders. Elderly patients, for instance, tend to enter hospitals with more diseases and related symptoms than younger patients. That means it’s difficult for the models to correlate specific diseases with specific symptoms, Chen says. “Similarly,” she adds, “young patients don’t have many diseases or as many symptoms, and if they have a rare disease or symptom, it doesn’t present in a normal way the models understand.”</p> <p><strong>Splitting data</strong></p> <p>The researchers also collected much more patient data and created three distinct datasets of different granularity to see if that could improve performance. For the 270,000 visits used in the original analysis, the researchers extracted the full EHR history of the 140,804 unique patients, tracking back a decade, with around 7.4 million annotations total from various sources, such as physician notes.</p> <p>Choices in the dataset-creation process impacted the model performance as well. One of the datasets aggregates each of the 140,400 patient histories as one data point each. Another dataset treats each of the 7.4 million annotations as a separate data point. A final one creates “episodes” for each patient, defined as a continuous series of visits without a break of more than 30 days, yielding a total of around 1.4 million episodes.</p> <p>Intuitively, a dataset where the full patient history is aggregated into one data point should lead to greater accuracy since the entire patient history is considered. Counterintuitively, however, it also caused the naive Bayes model&nbsp;to perform more poorly for some diseases. “You assume the more intrapatient information, the better, with machine-learning models. But these models are dependent on the granularity of the data you feed them,” Chen says. “The type of model you use could get overwhelmed.”</p> <p>As expected, feeding the model demographic information can also be effective. For instance, models can use that information to exclude all male patients for, say, predicting cervical cancer. And certain diseases far more common for elderly patients can be eliminated in younger patients.</p> <p>But, in another surprise, the demographic information didn’t boost performance for the most successful model, so collecting that data may be unnecessary. That’s important, Chen says, because compiling data and training models on the data can be expensive and time-consuming. Yet, depending on the model, using scores of data may not actually improve performance.</p> <p>Next, the researchers hope to use their findings to build a robust model to deploy in clinical settings. Currently, the health knowledge graph learns relations between diseases and symptoms but does not give a direct prediction of disease from symptoms. “We hope that any predictive model and any medical knowledge graph would be put under a stress test so that clinicians and machine-learning researchers can confidently say, ‘We trust this as a useful diagnostic tool,’” Chen says.</p> A variety of new diagnostic models can analyze patient data and real-time symptoms to predict if a given patient has a particular disease. Research, Computer science and technology, Algorithms, Machine learning, Health, Health care, Disease, Medicine, Institute for Medical Engineering and Science (IMES), Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering Finding the true potential of algorithms Using mathematical theory, Virginia Williams coaxes algorithms to run faster or proves they’ve hit their maximum speed. Tue, 07 Jan 2020 00:00:00 -0500 Rob Matheson | MIT News Office <p>Each semester, Associate Professor Virginia Vassilevska Williams tries to impart one fundamental lesson to her computer-science undergraduates: Math is the foundation of everything.</p> <p>Often, students come into Williams’ class, 6.006 (Introduction to Algorithms), wanting to dive into advanced programming that power the latest, greatest computing techniques. Her lessons instead focus on how algorithms are designed around core mathematical models and concepts. &nbsp;</p> <p>“When taking an algorithms class, many students expect to program a lot and perhaps use deep learning, but it’s very mathematical and has very little programming,” says Williams, the Steven G. (1968) and Renee Finn Career Development Professor who recently earned tenure in the Department of Electrical Engineering and Computer Science. “We don’t have much time together in class (only two hours a week), but I hope in that time they get to see a little of the beauty of math — because math allows you to see how and why everything works together. It really is a beautiful thing.”</p> <p>Williams’ life is very much shaped by math. As a child of two mathematician parents, she fell in love with the subject early on. But even though she excelled in the subject, her high school classes focused on German, writing, and biology. Returning to her first love in college and beyond, she applied her math skills to make waves in computer science.</p> <p>In highly influential work, Williams in 2012 improved an algorithm for “<a href="">matrix multiplication</a>” —&nbsp;a fundamental operation across computer science — that was thought to be the fastest iteration for 24 years. Years later, she co-founded an emerging field called “fine-grained complexity,” which seeks to explain, in part, how fast certain algorithms can solve various problems.</p> <p>In matrix multiplication, her work has now shifted slightly to showing that existing techniques “cannot do better,” she says. “We couldn’t improve the performance of our own algorithms anymore, so we came up with ways to explain why we couldn’t and why other methods can’t improve the performance either.”</p> <p><strong>Winding path to math</strong></p> <p>Growing up in Sofia, Bulgaria, Williams loved math and was a gifted student. But her parents often reminded her the mathematician’s life wasn’t exactly glamorous —especially when trying to find faculty gigs in the same area for two people. They sometimes traveled where work took them.</p> <p>That included a brief odyssey around the U.S. as a child. The first stop was Laramie, Wyoming. Her parents were visiting professors at the University of Wyoming, while Williams initially struggled through fourth grade because of the language barrier. “I didn’t really speak English, and was thrown into this school. My brother and I learned English watching the Disney channel, which was pretty fun,” says Williams, who today speaks Bulgarian, English, German, and some Russian.</p> <p>The next stop was Los Angeles — right around the time of the Rodney King riots. “The house on the other side of our street was set on fire,” Williams recalls. “Those were some very strange memories of L.A.”</p> <p>Returning to Bulgaria after two years, Williams decided to “explore her options” outside math by enrolling in the German Language High School in Sofia, the country’s top high school at the time, where she studied the German language, literature, history, and other humanities subjects. But, when it came to applying to colleges, she could never shake her first love. “I really tried to like the humanities, and what I learned is very helpful to me nowadays. But those subjects were very hard for me. My brain just doesn’t work that way,” she says. “I went back to what I like.”</p> <p><strong>Transfixed by algorithms</strong></p> <p>In 1999, Williams enrolled in Caltech. In her sophomore year, she became smitten by an exciting new field: computer science. “I took my first programming course, and I loved it,” she says.</p> <p>She became transfixed by matrix multiplication algorithms, which have some heavy-duty math at their core. These algorithms compute multiple arrays of numbers corresponding to some data and output a single combined matrix of some target values. Applications are wide-ranging, including computer graphics, product design, artificial intelligence, and biotechnology.</p> <p>As a PhD student at Carnegie Mellon, and beyond, she published <a href="">numerous papers</a>, on topics such as developing fast matrix multiplication algorithms in special algebraic structures, with applications including flight scheduling and network routing. After earning her PhD, she took on a series of postdoc and researcher positions at the Institute for Advanced Study, the University of California at Berkeley, and Stanford University, where she landed a faculty position in 2013 teaching courses on algorithms.</p> <p>In 2012, she developed a new algorithm that was faster than the Coppersmith–Winograd algorithm, which had reigned supreme in matrix multiplication since the 1980s. Williams’ method reduced the number of steps required to multiply matrices. Her algorithm is only slightly slower than the current record-holder.</p> <p><strong>Dealing with complexity</strong></p> <p>Between 2010 and 2015, Williams and her husband, Ryan Williams, who is also an MIT professor, became main founders of “fine-grained complexity.” The older field of “computational complexity” finds provably efficient algorithms and algorithms that are probably inefficient, based on some threshold of computational steps they take to solve a problem.</p> <p>Fine-grained complexity groups problems together by computational equivalence to better prove if algorithms are truly optimal or not. For instance, two problems may appear very different in what they solve and how many steps algorithms take to solve them. But fine-grained complexity shows such problems are secretly the same. Therefore, if an algorithm exists for one problem that uses fewer steps, then there must exist an algorithm for the other problem that uses fewer steps, and vice versa. On the flip side, if there exists a provably optimal algorithm for one problem, then all equivalent problems must have optimal algorithms. If someone ever finds a much faster algorithm for one problem, all the equivalent problems can be solved faster.</p> <p>Since co-launching the field, “it’s ballooned,” Williams says. “For most theoretical computer science conferences, you can now submit your paper under the heading ‘fine-grained complexity.’”</p> <p>In 2017, Williams came to MIT, where she says she has found impassioned, likeminded researchers. Many graduate students and colleagues, for instance, are working in topics related to fine-grained complexity. In turn, her students have introduced her to other subjects, such as cryptography, where she’s now introducing ideas from fine-grained complexity.</p> <p>She also sometimes studies “computational social choice,” a field that caught her eye during graduate school. Her work focuses on examining the computational complexity needed to rig sports games, voting schemes, and other systems where competitors are placed in paired brackets. If someone knows, for instance, which player will win in paired match-ups, a tournament organizer can place all players in specific positions in the initial seeding to ensure a certain player wins it all.</p> <p>Simulating all the possible combinations to rig these schemes can be very computationally complex. But Williams, an avid tennis player, authored a 2010 <a href="">paper</a> that found it’s fairly simple to rig a single-elimination tournament so a certain player wins, depending on accurate predictions for match-up winners and other factors.</p> <p>This year she co-wrote a <a href="">paper</a> that showed a tournament organizer could arrange an initial seeding and bribe certain top players — within a specific budget —&nbsp;to ensure a favorite player wins the tournament. “When I need a break from my usual work, I work in this field,” Williams says. “It’s a fun change of pace.”</p> <p>Thanks to the ubiquity of computing today, Williams’ graduate students often enter her classroom far more experienced in computer science than she was at their age. But to help steer them down a distinct path, she draws inspiration from her own college experiences, getting hooked on specific topics she still pursues today.</p> <p>“In order to do good research, you have to obsess over a problem,” Williams says. “I want them to find something in my course they can obsess over.”</p> Virginia WilliamsImage: Jared CharneyResearch, Computer science and technology, Algorithms, Profile, Faculty, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering, Mathematics Tool predicts how fast code will run on a chip Machine-learning system should enable developers to improve computing efficiency in a range of applications. Mon, 06 Jan 2020 00:00:00 -0500 Rob Matheson | MIT News Office <p>MIT researchers have invented a machine-learning tool that predicts how fast computer chips will execute code from various applications.&nbsp;&nbsp;</p> <p>To get code to run as fast as possible, developers and compilers — programs that translate programming language into machine-readable code — typically use performance models that run the code through a simulation of given chip architectures.&nbsp;</p> <p>Compilers use that information to automatically optimize code, and developers use it to tackle performance bottlenecks on the microprocessors that will run it. But performance models for machine code are handwritten by a relatively small group of experts and are not properly validated. As a consequence, the simulated performance measurements often deviate from real-life results.&nbsp;</p> <p>In series of conference papers, the researchers describe a novel machine-learning pipeline that automates this process, making it easier, faster, and more accurate. In a&nbsp;<a href="">paper</a>&nbsp;presented at the International Conference on Machine Learning in June, the researchers presented Ithemal, a neural-network model that trains on labeled data in the form of “basic blocks” — fundamental snippets of computing instructions — to automatically predict how long it takes a given chip to execute previously unseen basic blocks. Results suggest Ithemal performs far more accurately than traditional hand-tuned models.&nbsp;</p> <p>Then, at the November IEEE International Symposium on Workload Characterization, the researchers&nbsp;<a href="">presented</a>&nbsp;a benchmark suite of basic blocks from a variety of domains, including machine learning, compilers, cryptography, and graphics that can be used to validate performance models. They pooled more than 300,000 of the profiled blocks into an open-source dataset called BHive.&nbsp;During their evaluations, Ithemal predicted how fast Intel chips would run code even better than a performance model built by Intel itself.&nbsp;</p> <p>Ultimately, developers and compilers can use the tool to generate code that runs faster and more efficiently on an ever-growing number of diverse and “black box” chip designs.&nbsp;“Modern computer processors are opaque, horrendously complicated, and difficult to understand. It is also incredibly challenging to write computer code that executes as fast as possible for these processors,” says co-author on all three papers Michael Carbin, an assistant professor in the Department of Electrical Engineering and Computer Science (EECS) and a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “This tool is a big step forward toward fully modeling the performance of these chips for improved efficiency.”</p> <p>Most recently, in a&nbsp;<a href="">paper</a>&nbsp;presented at the NeurIPS conference in December, the team proposed a new technique to automatically generate compiler optimizations.&nbsp;&nbsp;Specifically, they automatically generate an algorithm, called Vemal, that converts certain code into vectors, which can be used for parallel computing. Vemal outperforms hand-crafted vectorization algorithms used in the LLVM compiler — a popular compiler used in the industry.</p> <p><strong>Learning from data</strong></p> <p>Designing performance models by hand can be “a black art,” Carbin says. Intel provides extensive documentation of more than 3,000 pages describing its chips’ architectures. But there currently exists only a small group of experts who will build performance models that simulate the execution of code on those architectures.&nbsp;</p> <p>“Intel’s documents are neither error-free nor complete, and Intel will omit certain things, because it’s proprietary,” says co-author on all three papers Charith Mendis, a graduate student in EECS and CSAIL. “However, when you use data, you don’t need to know the documentation. If there’s something hidden you can learn it directly from the data.”</p> <p>To do so, the researchers clocked the average number of cycles a given microprocessor takes to compute basic block instructions — basically, the sequence of boot-up, execute, and shut down — without human intervention. Automating the process enables rapid profiling of hundreds of thousands or millions of blocks.&nbsp;</p> <p><strong>Domain-specific architectures</strong></p> <p>In training, the Ithemal model analyzes millions of automatically profiled basic blocks to learn exactly how different chip architectures will execute computation. Importantly, Ithemal takes raw text as input and does not require manually adding features to the input data. In testing, Ithemal can be fed previously unseen basic blocks and a given chip, and will generate a single number indicating how fast the chip will execute that code.&nbsp;</p> <p>The researchers found Ithemal cut error rates in accuracy —&nbsp;meaning the difference between the predicted speed versus real-world speed —&nbsp;by 50 percent over traditional hand-crafted models. Further,&nbsp;in their next&nbsp;paper, they showed that&nbsp;Ithemal’s error rate was 10 percent, while the Intel performance-prediction model’s error rate was 20 percent on a variety of basic blocks across multiple different domains.</p> <p>The tool now makes it easier to quickly learn performance speeds for any new chip architectures, Mendis says. For instance, domain-specific architectures, such as Google’s new Tensor Processing Unit used specifically for neural networks, are now being built but aren’t widely understood. “If you want to train a model on some new architecture, you just collect more data from that architecture, run it through our profiler, use that information to train Ithemal, and now you have a model that predicts performance,” Mendis says.</p> <p>Next, the researchers are studying methods to make models interpretable. Much of machine learning is a black box, so it’s not really clear why a particular model made its predictions. “Our model is saying it takes a processor, say, 10 cycles to execute a basic block. Now, we’re trying to figure out why,” Carbin says. “That’s a fine level of granularity that would be amazing for these types of tools.”</p> <p>They also hope to use Ithemal to enhance the performance of Vemal even further and achieve better performance automatically.</p> MIT researchers have built a new benchmark tool that can accurately predict how long it takes given code to execute on a computer chip, which can help programmers tweak the code for better performance.Research, Computer science and technology, Algorithms, Machine learning, Data, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering Finding a good read among billions of choices As natural language processing techniques improve, suggestions are getting speedier and more relevant. Fri, 20 Dec 2019 12:55:01 -0500 Kim Martineau | MIT Quest for Intelligence <p>With billions of books, news stories, and documents online, there’s never been a better time to be reading — if you have time to sift through all the options. “There’s a ton of text on the internet,” says&nbsp;<a href="">Justin Solomon</a>, an assistant professor at MIT. “Anything to help cut through all that material is extremely useful.”</p> <p>With the&nbsp;<a href="">MIT-IBM Watson AI Lab</a>&nbsp;and his&nbsp;<a href="">Geometric Data Processing Group</a>&nbsp;at MIT, Solomon recently presented a new technique for cutting through massive amounts of text at the&nbsp;<a href="">Conference on Neural Information Processing Systems</a> (NeurIPS). Their method combines three popular text-analysis tools — topic modeling, word embeddings, and optimal transport — to deliver better, faster results than competing methods on a popular benchmark for classifying documents.</p> <p>If an algorithm knows what you liked in the past, it can scan the millions of possibilities for something similar. As natural language processing techniques improve, those “you might also like” suggestions are getting speedier and more relevant.&nbsp;</p> <p>In the method presented at NeurIPS, an algorithm summarizes a collection of, say, books, into topics based on commonly-used words in the collection. It then divides each book into its five to 15 most important topics, with an estimate of how much each topic contributes to the book overall.&nbsp;</p> <p>To compare books, the researchers use two other tools: word embeddings, a technique that turns words into lists of numbers to reflect their similarity in popular usage, and optimal transport, a framework for calculating the most efficient way of moving objects — or data points — among multiple destinations.&nbsp;</p> <p>Word embeddings make it possible to leverage optimal transport twice: first to compare topics within the collection as a whole, and then, within any pair of books, to measure how closely common themes overlap.&nbsp;</p> <p>The technique works especially well when scanning large collections of books and lengthy documents. In the study, the researchers offer the example of Frank Stockton’s “The Great War Syndicate,” a 19th&nbsp;century American novel that anticipated the rise of nuclear weapons. If you’re looking for a similar book, a topic model would help to identify the dominant themes shared with other books — in this case, nautical, elemental, and martial.&nbsp;</p> <p>But a topic model alone wouldn’t identify Thomas Huxley’s 1863 lecture,&nbsp;“<a href="">The Past Condition of Organic Nature</a>,” as a good match. The writer was a champion of Charles Darwin’s theory of evolution, and his lecture, peppered with mentions of fossils and sedimentation, reflected emerging ideas about geology. When the themes in Huxley’s lecture are matched with Stockton’s novel via optimal transport, some cross-cutting motifs emerge: Huxley’s geography, flora/fauna, and knowledge themes map closely to Stockton’s nautical, elemental, and martial themes, respectively.</p> <p>Modeling books by their representative topics, rather than individual words, makes high-level comparisons possible. “If you ask someone to compare two books, they break each one into easy-to-understand concepts, and then compare the concepts,” says the study’s lead author&nbsp;<a href="">Mikhail Yurochkin</a>, a researcher at IBM.&nbsp;</p> <p>The result is faster, more accurate comparisons, the study shows. The researchers compared 1,720 pairs of books in the Gutenberg Project dataset in one second — more than 800 times faster than the next-best method.</p> <p>The technique also does a better job of accurately sorting documents than rival methods — for example, grouping books in the Gutenberg dataset by author, product reviews on Amazon by department, and BBC sports stories by sport. In a series of visualizations, the authors show that their method neatly clusters documents by type.</p> <p>In addition to categorizing documents quickly and more accurately, the method offers a window into the model’s decision-making process. Through the list of topics that appear, users can see why the model is recommending a document.</p> <p>The study’s other authors are&nbsp;<a href="">Sebastian Claici</a>&nbsp;and&nbsp;<a href="">Edward Chien</a>, a graduate student and a postdoc, respectively, at MIT’s Department of Electrical Engineering and Computer Science and Computer Science and Artificial Intelligence Laboratory, and&nbsp;<a href="">Farzaneh Mirzazadeh</a>, a researcher at IBM.</p> In a new study, researchers at MIT and IBM combine three popular text-analysis tools — topic modeling, word embeddings, and optimal transport — to compare thousands of documents per second. Here, they show that their method (left) clusters newsgroup posts by category more tightly than a competing method. Image courtesy of the researchers.Quest for Intelligence, MIT-IBM Watson AI Lab, Electrical engineering and computer science (EECS), Computer Science and Artificial Intelligence Laboratory (CSAIL), School of Engineering, Algorithms, Artificial intelligence, Computer science and technology, Data, Machine learning, Natural language processing Model beats Wall Street analysts in forecasting business financials Using limited data, this automated system predicts a company’s quarterly sales. Thu, 19 Dec 2019 09:31:03 -0500 Rob Matheson | MIT News Office <p>Knowing a company’s true sales can help determine its value. Investors, for instance, often employ financial analysts to predict a company’s upcoming earnings using various public data, computational tools, and their own intuition. Now MIT researchers have developed an automated model that significantly outperforms humans in predicting business sales using very limited, “noisy” data.</p> <p>In finance, there’s growing interest in using imprecise but frequently generated consumer data — called “alternative data” —&nbsp;to help predict a company’s earnings for trading and investment purposes. Alternative data can comprise credit card purchases, location data from smartphones, or even satellite images showing how many cars are parked in a retailer’s lot. Combining alternative data with more traditional but infrequent ground-truth financial data — such as quarterly earnings, press releases, and stock prices — can paint a clearer picture of a company’s financial health on even a daily or weekly basis.</p> <p>But, so far, it’s been very difficult to get accurate, frequent estimates using alternative data. In a paper published this week in the <em>Proceedings of ACM Sigmetrics Conference</em>, the researchers describe a model for forecasting financials that uses only anonymized weekly credit card transactions and three-month earning reports.</p> <p>Tasked with predicting quarterly earnings of more than 30 companies, the model outperformed the combined estimates of expert Wall Street analysts on 57 percent of predictions. Notably, the analysts had access to any available private or public data and other machine-learning models, while the researchers’ model used a very small dataset of the two data types.</p> <p>“Alternative data are these weird, proxy signals to help track the underlying financials of a company,” says first author Michael Fleder, a postdoc in the Laboratory for Information and Decision Systems (LIDS). “We asked, ‘Can you combine these noisy signals with quarterly numbers to estimate the true financials of a company at high frequencies?’ Turns out the answer is yes.”</p> <p>The model could give an edge to investors, traders, or companies looking to frequently compare their sales with competitors. Beyond finance, the model could help social and political scientists, for example, to study aggregated, anonymous data on public behavior. “It’ll be useful for anyone who wants to figure out what people are doing,” Fleder says.</p> <p>Joining Fleder on the paper is EECS Professor Devavrat Shah, who is the director of MIT’s Statistics and Data Science Center, a member of the Laboratory for Information and Decision Systems, a principal investigator for the MIT Institute for Foundations of Data Science, and an adjunct professor at the Tata Institute of Fundamental Research. &nbsp;</p> <p><strong>Tackling the “small data” problem</strong></p> <p>For better or worse, a lot of consumer data is up for sale. Retailers, for instance, can buy credit card transactions or location data to see how many people are shopping at a competitor. Advertisers can use the data to see how their advertisements are impacting sales. But getting those answers still primarily relies on humans. No machine-learning model has been able to adequately crunch the numbers.</p> <p>Counterintuitively, the problem is actually lack of data. Each financial input, such as a quarterly report or weekly credit card total, is only one number. Quarterly reports over two years total only eight data points. Credit card data for, say, every week over the same period is only roughly another 100 “noisy” data points, meaning they contain potentially uninterpretable information.</p> <p>“We have a ‘small data’ problem,” Fleder says. “You only get a tiny slice of what people are spending and you have to extrapolate and infer what’s really going on from that fraction of data.”</p> <p>For their work, the researchers obtained consumer credit card transactions —&nbsp;at typically weekly and biweekly intervals — and quarterly reports for 34 retailers from 2015 to 2018 from a hedge fund. Across all companies, they gathered 306 quarters-worth of data in total.</p> <p>Computing daily sales is fairly simple in concept. The model assumes a company’s daily sales remain similar, only slightly decreasing or increasing from one day to the next. Mathematically, that means sales values for consecutive days are multiplied by some constant value plus some statistical noise value — which captures some of the inherent randomness in a company’s sales. Tomorrow’s sales, for instance, equal today’s sales multiplied by, say, 0.998 or 1.01, plus the estimated number for noise.</p> <p>If given accurate model parameters for the daily constant&nbsp;and noise level, a standard inference algorithm can calculate that equation to output an accurate forecast of daily sales. But the trick is calculating those parameters.</p> <p><strong>Untangling the numbers</strong></p> <p>That’s where quarterly reports and probability techniques come in handy. In a simple world, a quarterly report could be divided by, say, 90 days to calculate the daily sales (implying sales are roughly constant day-to-day). In reality, sales vary from day to day. Also, including alternative data to help understand how sales vary over a quarter complicates matters: Apart from being noisy, purchased credit card data always consist of some indeterminate fraction of the total sales. All that makes it very difficult to know how exactly the credit card totals factor into the overall sales estimate.</p> <p>“That requires a bit of untangling the numbers,” Fleder says. “If we observe 1 percent of a company’s weekly sales through credit card transactions, how do we know it’s 1 percent? And, if the credit card data is noisy, how do you know how noisy it is? We don’t have access to the ground truth for daily or weekly sales totals. But the quarterly aggregates help us reason about those totals.”</p> <p>To do so, the researchers use a variation of the standard inference algorithm, called Kalman filtering or Belief Propagation, which has been used in various technologies from space shuttles to smartphone GPS. Kalman filtering uses data measurements observed over time, containing noise inaccuracies, to generate a probability distribution for unknown variables over a designated timeframe. In the researchers’ work, that means estimating the possible sales of a single day.</p> <p>To train the model, the technique first breaks down quarterly sales into a set number of measured days, say 90 — allowing sales to vary day-to-day. Then, it matches the observed, noisy credit card data to unknown daily sales. Using the quarterly numbers and some extrapolation, it estimates the fraction of total sales the credit card data likely represents. Then, it calculates each day’s fraction of observed sales, noise level, and an error estimate for how well it made its predictions.</p> <p>The inference algorithm plugs all those values into the formula to predict daily sales totals. Then, it can sum those totals to get weekly, monthly, or quarterly numbers. Across all 34 companies, the model beat a consensus benchmark — which combines estimates of Wall Street analysts —&nbsp;on 57.2 percent of 306 quarterly predictions.</p> <p>Next, the researchers are designing the model to analyze a combination of credit card transactions and other alternative data, such as location information. “This isn’t all we can do. This is just a natural starting point,” Fleder says.</p> An automated machine-learning model developed by MIT researchers significantly outperforms human Wall Street analysts in predicting quarterly business sales.Research, Computer science and technology, Algorithms, Laboratory for Information and Decision Systems (LIDS), IDSS, Data, Machine learning, Finance, Industry, Electrical Engineering & Computer Science (eecs), School of Engineering Boston-area girls discover a passion for coding Weekend robotics workshops help middle and high school girls dispel “computing phobia.” Fri, 13 Dec 2019 12:20:01 -0500 Dorothy Ryan | Lincoln Laboratory <p>“My goal is to make computing 'normal' for girls,” says Sabina Chen from Lincoln Laboratory's Advanced Capabilities and Systems Group, who led a workshop that taught middle school girls how to program a robotic car to autonomously follow colored cones. The girls attended this enrichment class for eight consecutive Saturdays from September to November. “The class is about exposure [to computing] and interest-building,” she explains.</p> <p>While Chen was introducing the 20 middle school girls to coding and computer visualization software at the Beaver Works facility in MIT's Building 31, Eyassu Shimelis of the laboratory's Advanced Concepts and Technologies Group was conducting a similar series of classes for 21 high school girls in MIT's Building 33.</p> <p>The motivation behind holding the girls-only workshops is to foster a curiosity and familiarity in computing that may lead to a future increase in the number of women engaged in computer science. According to, in 2018 only 18 percent of bachelor's degrees in computer science were awarded to women; in electrical engineering, a major that often leads to professions involving computing, the percentage is even lower, at 13.7. The Bureau of Labor Statistics reports that women make up only about 21 percent of computer programmers, 19 percent of software developers, and 32 percent of website developers.</p> <p>While multiple theories exist as to why women are underrepresented in computer-related majors and jobs, one consensus is that young women do not have confidence in their ability to master computers. “The girls came in thinking they can't do it,” Chen says, adding that she finds the course worthwhile when “their eyes sort of sparkle when they realize they can do it.”</p> <p>Both workshops are based on a rigorous four-week course offered to high school seniors through the <a href="">Beaver Works Summer Institute</a> (BWSI). The summer course lets students explore the various technologies that can be used to enable an MIT-designed RACECAR (Rapid Autonomous Complex Environment Competing Ackermann steeRing) robotic vehicle to compete in fast, autonomous navigation around a mini “Grand Prix” racetrack. Although the Saturday sessions could not delve as deeply as the summer course into the software and various systems needed for the cars to tackle the obstacle-ridden Grand Prix, these weekend “crash courses” did cover the coding and computer-vison technology that allowed the girls to race their cars around a circular course set up in Building 31's high-bay space.</p> <p>Chen developed the curriculum for the middle school program that was offered to both boys and girls this past summer in conjunction with the BWSI. “It is designed so students learn a specific concept, apply it, and see an immediate result,” she says. The gratification of witnessing a hands-on application of a lesson is what keeps the students interested. Her curriculum will soon be online so that schools, robotics clubs, or even interested individuals can adapt it for themselves.&nbsp;</p> <p>Shimelis taught a similar version of a <a href="">RACECAR preliminary course</a> for Boston-area high schoolers. That course was developed in 2018 by Andrew Fishberg, who passed along his program when he moved on to tackle graduate studies at MIT. Shimelis is tweaking the course to address feedback from BWSI RACECAR students and teaching assistants, and to adapt it to his teaching style.</p> <p>Both Chen and Shimelis say they did not modify their courses for the girls-only sessions that were new this fall. They agree that the girls were eager to learn and capable of handling the classwork. “Many of the girls were faster at grasping the concepts than students in my summer course,” Shimelis notes. This is high praise, because to be accepted for the BWSI program, students must complete a prerequisite RACECAR online tutorial and submit teacher recommendations and stellar school transcripts.</p> <p>Chen says she was pleased by the change she saw in the girls from the beginning to the end of her workshop. “At the end, they were a lot more sure of themselves and more willing to explore their own ideas without fear.”</p> <p>According to Chen and Shimelis, the success of the two workshops can, in large part, be attributed to the dedicated help of a number of people. Sertac Karaman, an MIT engineering professor who developed the original RACECAR course for undergraduate students, provided guidance to both the instructors and students. A cadre of volunteers served as teaching assistants: Kourosh Arasteh, Olivia Brown, Juliana Furgala, Saivamsi Hanumanthu, Elisa Kircheim, Tzofi Klinghoffer, Marko Kocic, Aryk Ledet, Harrison Packer, Joyce Tam, and Jing Wang from Lincoln Laboratory's staff; and Andrew Schoer, a Boston University grad student who is a participant in the Laboratory's Lincoln Scholars tuition assistance program.</p> <p>The success of the workshops is captured in one student's answer to a course-evaluation question about what she gained: “I see myself coding in the future!”</p> Middle school and high school girls meet in MIT's Building 31 to showcase the capabilities of the autonomous cars their teams programmed to navigate a circuitous course. Photo: Joel GrimmLincoln Laboratory, Beaver works, STEM education, K-12 education, Education, teaching, academics, Robotics, Programming, Women in STEM, Classes and programs, Computer science and technology This object-recognition dataset stumped the world’s best computer vision models Objects are posed in varied positions and shot at odd angles to spur new AI techniques. Tue, 10 Dec 2019 11:00:01 -0500 Kim Martineau | MIT Quest for Intelligence <p>Computer vision models have learned to identify objects in photos so accurately that some can outperform humans on some datasets. But when those same object detectors are turned loose in the real world, their performance noticeably drops, creating reliability concerns for self-driving cars and other safety-critical systems that use machine vision.</p> <p>In an effort to close this performance gap, a team of MIT and IBM researchers set out to create a very different kind of object-recognition dataset. It’s called <a href="">ObjectNet,</a> a play on ImageNet, the crowdsourced database of photos responsible for launching much of the modern boom in artificial intelligence.&nbsp;</p> <p>Unlike ImageNet, which features photos taken from Flickr and other social media sites, ObjectNet features photos taken by paid freelancers. Objects are shown tipped on their side, shot at odd angles, and displayed in clutter-strewn rooms. When leading object-detection models were tested on ObjectNet<strong>,</strong> their accuracy rates fell from a high of 97 percent on ImageNet to just 50-55 percent.</p> <p>“We created this dataset to tell people the object-recognition problem continues to be a hard problem,” says <a href="">Boris Katz</a>, a research scientist at MIT’s <a href="">Computer Science and Artificial Intelligence Laboratory</a> (CSAIL) and <a href="">Center for Brains, Minds and Machines</a> (CBMM).&nbsp; “We need better, smarter algorithms.” Katz and his colleagues will present ObjectNet and their results at the <a href="">Conference on Neural Information Processing Systems (NeurIPS)</a>.</p> <p>Deep learning, the technique driving much of the recent progress in AI, uses layers of artificial "neurons" to find patterns in vast amounts of raw data. It learns to pick out, say, the chair in a photo after training on hundreds to thousands of examples. But even datasets with millions of images can’t show each object in all of its possible orientations and settings, creating problems when the models encounter these objects in real life.</p> <p>ObjectNet is different from conventional image datasets in another important way: it contains no training images. Most datasets are divided into data for training the models and testing their performance. But the training set often shares subtle similarities with the test set, in effect giving the models a sneak peak at the test.&nbsp;</p> <p>At first glance, <a href="">ImageNet</a>, at 14 million images, seems enormous. But when its training set is excluded, it’s comparable in size to ObjectNet, at 50,000 photos.&nbsp;</p> <p>“If we want to know how well algorithms will perform in the real world, we should test them on images that are unbiased and that they’ve never seen before,” says study co-author <a href="">Andrei Barbu</a>, a research scientist at CSAIL and CBMM.<em>&nbsp;</em></p> <p><strong>A dataset that tries to capture the complexity of real-world objects&nbsp;</strong></p> <p>Few people would think to share the photos from ObjectNet with their friends, and that’s the point. The researchers hired freelancers from Amazon Mechanical Turk to take photographs of hundreds of randomly posed household objects. Workers received photo assignments on an app, with animated instructions telling them how to orient the assigned object, what angle to shoot from, and whether to pose the object in the kitchen, bathroom, bedroom, or living room.&nbsp;</p> <p>They wanted to eliminate three common biases: objects shown head-on, in iconic positions, and in highly correlated settings — for example, plates stacked in the kitchen.&nbsp;</p> <p>It took three years to conceive of the dataset and design an app that would standardize the data-gathering process. “Discovering how to gather data in a way that controls for various biases was incredibly tricky,” says study co-author <a href="">David Mayo</a>, a graduate student at MIT’s <a href="">Department of Electrical Engineering and Computer Science.</a> “We also had to run experiments to make sure our instructions were clear and that the workers knew exactly what was being asked of them.”&nbsp;</p> <p>It took another year to gather the actual data, and in the end, half of all the photos freelancers submitted had to be discarded for failing to meet the researchers’ specifications. In an attempt to be helpful, some workers added labels to their objects, staged them on white backgrounds, or otherwise tried to improve on the aesthetics of the photos they were assigned to shoot.</p> <p>Many of the photos were taken outside of the United States, and thus, some objects may look unfamiliar. Ripe oranges are green, bananas come in different sizes, and clothing appears in a variety of shapes and textures.</p> <p><strong>Object Net vs. ImageNet: how leading object-recognition models compare</strong></p> <p>When the researchers tested state-of-the-art computer vision models on ObjectNet, they found a performance drop of 40-45 percentage points from ImageNet. The results show that object detectors still struggle to understand that objects are three-dimensional and can be rotated and moved into new contexts, the researchers say. “These notions are not built into the architecture of modern object detectors,” says study co-author <a href="">Dan Gutfreund</a>, a researcher at IBM.</p> <p>To show that ObjectNet is difficult precisely because of how objects are viewed and positioned, the researchers allowed the models to train on half of the ObjectNet data before testing them on the remaining half. Training and testing on the same dataset typically improves performance, but here the models improved only slightly, suggesting that object detectors have yet to fully comprehend how objects exist in the real world.</p> <p>Computer vision models have progressively improved since 2012, when an object detector called AlexNet crushed the competition at the annual ImageNet contest. As datasets have gotten bigger, performance has also improved.</p> <p>But designing bigger versions of ObjectNet, with its added viewing angles and orientations, won’t necessarily lead to better results, the researchers warn. The goal of ObjectNet is to motivate researchers to come up with the next wave of revolutionary techniques, much as the initial launch of the ImageNet challenge did.</p> <p>“People feed these detectors huge amounts of data, but there are diminishing returns,” says Katz. “You can’t view an object from every angle and in every context. Our hope is that this new dataset will result in robust computer vision without surprising failures in the real world.”</p> <p>The study’s other authors are Julian Alvero, William Luo, Chris Wang, and Joshua Tenenbaum of MIT. The research was funded by the National Science Foundation, MIT’s Center for Brains, Minds, and Machines, the MIT-IBM Watson AI Lab, Toyota Research Institute, and the SystemsThatLearn@CSAIL initiative.</p> ObjectNet, a dataset of photos created by MIT and IBM researchers, shows objects from odd angles, in multiple orientations, and against varied backgrounds to better represent the complexity of 3D objects. The researchers hope the dataset will lead to new computer vision techniques that perform better in real life. Photo collage courtesy of the researchers.Quest for Intelligence, Center for Brains Minds and Machines, Electrical engineering and computer science (EECS), Computer Science and Artificial Intelligence Laboratory (CSAIL), School of Engineering, Algorithms, Artifical intelligence, Computer science and technology, Computer vision, Data, Machine learning, Software Using computers to view the unseen A new computational imaging method could change how we view hidden information in scenes. Fri, 06 Dec 2019 11:05:01 -0500 Rachel Gordon | CSAIL <p>Cameras and computers together can conquer some seriously stunning feats. Giving computers vision has helped us <a href="" target="_blank">fight wildfires in California</a>, understand complex and treacherous roads — and even see around corners.&nbsp;</p> <p>Specifically, seven years ago a group of MIT researchers created a <a href="" target="_self">new imaging system</a> that used floors, doors, and walls as “mirrors” to understand information about scenes outside a normal line of sight. Using special lasers to produce recognizable 3D images, the work opened up a realm of possibilities in letting us better understand what we can’t see.&nbsp;</p> <p>Recently, a different group of scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has built off of this work, but this time with no special equipment needed: They developed a method that can reconstruct hidden video from just the subtle shadows and reflections on an observed pile of clutter. This means that, with a video camera turned on in a room, they can reconstruct a video of an unseen corner of the room, even if it falls outside the camera's field of view.&nbsp;</p> <div class="cms-placeholder-content-video"></div> <p>By observing the interplay of shadow and geometry in video, the team’s algorithm predicts the way that light travels in a scene, which is known as “light transport.” The system then uses that to estimate the hidden video from the observed shadows — and it can even construct the silhouette of a live-action performance.&nbsp;</p> <p>This type of image reconstruction could one day benefit many facets of society: Self-driving cars could better understand what’s emerging from behind corners, elder-care centers could enhance safety for their residents, and search-and-rescue teams could even improve their ability to navigate dangerous or obstructed areas.&nbsp;</p> <p>The technique, which is “passive,” meaning there are no lasers or other interventions to the scene, still currently takes about two hours to process, but the researchers say it could eventually be helpful in reconstructing scenes not in the traditional line of sight for the aforementioned applications.&nbsp;</p> <p>“You can achieve quite a bit with non-line-of-sight imaging equipment like lasers, but in our approach you only have access to the light that's naturally reaching the camera, and you try to make the most out of the scarce information in it,” says Miika Aittala, former CSAIL postdoc and current research scientist at NVIDIA<strong>, </strong>and the lead researcher on the new technique. “Given the recent advances in neural networks, this seemed like a great time to visit some challenges that, in this space, were considered largely unapproachable before.”&nbsp;</p> <p>To capture this unseen information, the team uses subtle, indirect lighting cues, such as shadows and highlights from the clutter in the observed area.</p> <p>In a way, a pile of clutter behaves somewhat like a pinhole camera, similar to something you might build in an elementary school science class: It blocks some light rays, but allows others to pass through, and these paint an image of the surroundings wherever they hit. But where a pinhole camera is designed to let through just the amount of right rays to form a readable picture, a general pile of clutter produces an image that is scrambled (by the light transport) beyond recognition, into a complex play of shadows and shading.&nbsp;</p> <p>You can think of the clutter, then, as a mirror that gives you a scrambled view into the surroundings around it — for example, behind a corner where you can’t see directly.&nbsp;</p> <p>The challenge addressed by the team's algorithm was to unscramble and make sense of these lighting cues. Specifically, the goal was to recover a human-readable video of the activity in the hidden scene, which is a multiplication of the light transport and the hidden video.&nbsp;</p> <p>However, unscrambling proved to be a classic “chicken-or-egg” problem. To figure out the scrambling pattern, a user would need to know the hidden video already, and vice versa.&nbsp;</p> <p>“Mathematically, it’s like if I told you that I’m thinking of two secret numbers, and their product is 80. Can you guess what they are? Maybe 40 and 2? Or perhaps 371.8 and 0.2152? In our problem, we face a similar situation at every pixel,” says Aittala. “Almost any hidden video can be explained by a corresponding scramble, and vice versa. If we let the computer choose, it’ll just do the easy thing and give us a big pile of essentially random images that don’t look like anything.”&nbsp;</p> <p>With that in mind, the team focused on breaking the ambiguity by specifying algorithmically that they wanted a “scrambling” pattern that corresponds to plausible real-world shadowing and shading, to uncover the hidden video that looks like it has edges and objects that move coherently.&nbsp;</p> <p>The team also used the surprising fact that neural networks naturally prefer to express “image-like” content, even when they’ve never been trained to do so, which helped break the ambiguity. The algorithm trains two neural networks simultaneously, where they’re specialized for the one target video only, using ideas from a machine learning concept called <a href="">Deep Image Prior</a>. One network produces the scrambling pattern, and the other estimates the hidden video. The networks are rewarded when the combination of these two factors reproduce the video recorded from the clutter, driving them to explain the observations with plausible hidden data.</p> <p>To test the system, the team first piled up objects on one wall, and either projected a video or physically moved themselves on the opposite wall. From this, they were able to reconstruct videos where you could get a general sense of what motion was taking place in the hidden area of the room.</p> <p>In the future, the team hopes to improve the overall resolution of the system, and eventually test the technique in an uncontrolled environment.&nbsp;</p> <p>Aittala wrote a new paper on the technique alongside CSAIL PhD students Prafull Sharma, Lukas Murmann, and Adam Yedidia, with MIT professors Fredo Durand, Bill Freeman, and Gregory Wornell. They will present it next week at the Conference on Neural Information Processing Systems in Vancouver, British Columbia.&nbsp;</p> Based on shadows that an out-of-view video casts on nearby objects, MIT researchers can estimate the contents of the unseen video. In the top row, researchers used this method to recreate visual elements in an out-of-view video; the original elements are shown in the bottom row. Images courtesy of the researchers.Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical engineering and computer science (EECS), School of Engineering, Research, Algorithms, Computer science and technology, Computer vision, Video games, Video, Disaster response, Film and Television Helping machines perceive some laws of physics Model registers “surprise” when objects in a scene do something unexpected, which could be used to build smarter AI. Mon, 02 Dec 2019 00:00:00 -0500 Rob Matheson | MIT News Office <p>Humans have an early understanding of the laws of physical reality. Infants, for instance, hold expectations for how objects should move and interact with each other, and will show surprise when they do something unexpected, such as disappearing in a sleight-of-hand magic trick.</p> <p>Now MIT researchers have designed a model that demonstrates an understanding of some basic “intuitive physics” about how objects should behave. The model could be used to help build smarter artificial intelligence and, in turn, provide information to help scientists understand infant cognition.</p> <p>The model, called ADEPT, observes objects moving around a scene and makes predictions about how the objects should behave, based on their underlying physics. While tracking the objects, the model outputs a signal at each video frame that correlates to a level of “surprise” — the bigger the signal, the greater the surprise. If an object ever dramatically mismatches the model’s predictions — by, say, vanishing or teleporting across a scene — its surprise levels will spike.</p> <p>In response to videos showing objects moving in physically plausible and implausible ways, the model registered levels of surprise that matched levels reported by humans who had watched the same videos. &nbsp;</p> <p>“By the time infants are 3 months old, they have some notion that objects don’t wink in and out of existence, and can’t move through each other or teleport,” says first author Kevin A. Smith, a research scientist in the Department of Brain and Cognitive Sciences (BCS) and a member of the Center for Brains, Minds, and Machines (CBMM). “We wanted to capture and formalize that knowledge to build infant cognition into artificial-intelligence agents. We’re now getting near human-like in the way models can pick apart basic implausible or plausible scenes.”</p> <p>Joining Smith on the paper are co-first authors Lingjie Mei, an undergraduate in the Department of Electrical Engineering and Computer Science, and BCS research scientist Shunyu Yao; Jiajun Wu PhD ’19; CBMM investigator Elizabeth Spelke; Joshua B. Tenenbaum, a professor of computational cognitive science, and researcher in CBMM, BCS, and the Computer Science and Artificial Intelligence Laboratory (CSAIL); and CBMM investigator Tomer D. Ullman PhD ’15.</p> <p><strong>Mismatched realities</strong></p> <p>ADEPT relies on two modules: an “inverse graphics” module that captures object representations from raw images, and a “physics engine” that predicts the objects’ future representations from a distribution of possibilities.</p> <p>Inverse graphics basically extracts information of objects —&nbsp;such as shape, pose, and velocity — from pixel inputs. This module captures frames of video as images and uses inverse graphics to extract this information from objects in the scene. But it doesn’t get bogged down in the details. ADEPT requires only some approximate geometry of each shape to function. In part, this helps the model generalize predictions to new objects, not just those it’s trained on.</p> <p>“It doesn’t matter if an object is rectangle or circle, or if it’s a truck or a duck. ADEPT just sees there’s an object with some position, moving in a certain way, to make predictions,” Smith says. “Similarly, young infants also don’t seem to care much about some properties like shape when making physical predictions.”</p> <p>These coarse object descriptions are fed into a physics engine — software that simulates behavior of physical systems, such as rigid or fluidic bodies, and is commonly used for films, video games, and computer graphics. The researchers’ physics engine “pushes the objects forward in time,” Ullman says. This creates a range of predictions, or a “belief distribution,” for what will happen to those objects in the next frame.</p> <p>Next, the model observes the actual next frame. Once again, it captures the object representations, which it then aligns to one of the predicted object representations from its belief distribution. If the object obeyed the laws of physics, there won’t be much mismatch between the two representations. On the other hand, if the object did something implausible — say, it vanished from behind a wall — there will be a major mismatch.</p> <p>ADEPT then resamples from its belief distribution and notes a very low probability that the object had simply vanished. If there’s a low enough probability, the model registers great “surprise” as a signal spike. Basically, surprise is inversely proportional to the probability of an event occurring. If the probability is very low, the signal spike is very high. &nbsp;</p> <p>“If an object goes behind a wall, your physics engine maintains a belief that the object is still behind the wall. If the wall goes down, and nothing is there, there’s a mismatch,” Ullman says. “Then, the model says, ‘There’s an object in my prediction, but I see nothing. The only explanation is that it disappeared, so that’s surprising.’”</p> <p><strong>Violation of expectations</strong></p> <p>In development psychology, researchers run “violation of expectations” tests in which infants are shown pairs of videos. One video shows a plausible event, with objects adhering to their expected notions of how the world works. The other video is the same in every way, except objects behave in a way that violates expectations in some way. Researchers will often use these tests to measure how long the infant looks at a scene after an implausible action has occurred. The longer they stare, researchers hypothesize, the more they may be surprised or interested in what just happened.</p> <p>For their experiments, the researchers created several scenarios based on classical developmental research to examine the model’s core object knowledge. They employed 60 adults to watch 64 videos of known physically plausible and physically implausible scenarios. Objects, for instance, will move behind a wall and, when the wall drops, they’ll still be there or they’ll be gone. The participants rated their surprise at various moments on an increasing scale of 0 to 100. Then, the researchers showed the same videos to the model. Specifically, the scenarios examined the model’s ability to capture notions of permanence (objects do not appear or disappear for no reason), continuity (objects move along connected trajectories), and solidity (objects cannot move through one another).</p> <p>ADEPT matched humans particularly well on videos where objects moved behind walls and disappeared when the wall was removed. Interestingly, the model also matched surprise levels on videos that humans weren’t surprised by but maybe should have been. For example, in a video where an object moving at a certain speed disappears behind a wall and immediately comes out the other side, the object might have sped up dramatically when it went behind the wall or it might have teleported to the other side. In general, humans and ADEPT were both less certain about whether that event was or wasn’t surprising. The researchers also found traditional neural networks that learn physics from observations — but don’t explicitly represent objects — are far less accurate at differentiating surprising from unsurprising scenes, and their picks for surprising scenes don’t often align with humans.</p> <p>Next, the researchers plan to delve further into how infants observe and learn about the world, with aims of incorporating any new findings into their model. Studies, for example, show that infants up until a certain age actually aren’t very surprised when objects completely change in some ways — such as if a truck disappears behind a wall, but reemerges as a duck.</p> <p>“We want to see what else needs to be built in to understand the world more like infants, and formalize what we know about psychology to build better AI agents,” Smith says.</p> An MIT-invented model demonstrates an understanding of some basic “intuitive physics” by registering “surprise” when objects in simulations move in unexpected ways, such as rolling behind a wall and not reappearing on the other side.Image: Christine Daniloff, MITResearch, Computer science and technology, Algorithms, Artificial intelligence, Machine learning, Computer vision, Computer Science and Artificial Intelligence Laboratory (CSAIL), Brain and cognitive sciences, Electrical Engineering & Computer Science (eecs), School of Engineering, Center for Brains Minds and Machines Toward more efficient computing, with magnetic waves Circuit design offers a path to “spintronic” devices that use little electricity and generate practically no heat. Thu, 28 Nov 2019 13:59:59 -0500 Rob Matheson | MIT News Office <p>MIT researchers have devised a novel circuit design that enables precise control of computing with magnetic waves — with no electricity needed. The advance takes a step toward practical magnetic-based devices, which have the potential to compute far more efficiently than electronics.</p> <p>Classical computers rely on massive amounts of electricity for computing and data storage, and generate a lot of wasted heat. In search of more efficient alternatives, researchers have started designing magnetic-based “spintronic” devices, which use relatively little electricity and generate practically no heat.</p> <p>Spintronic devices leverage the “spin wave” — a quantum property of electrons — in magnetic materials with a lattice structure. This approach involves modulating the spin wave properties to produce some measurable output that can be correlated to computation. Until now, modulating spin waves has required injected electrical currents using bulky components that can cause signal noise and effectively negate any inherent performance gains.</p> <p>The MIT researchers developed a circuit architecture that uses only a nanometer-wide domain wall in layered nanofilms of magnetic material to modulate a passing spin wave, without any extra components or electrical current. In turn, the spin wave can be tuned to control the location of the wall, as needed. This provides precise control of two changing spin wave states, which correspond to the 1s and 0s used in classical computing. A paper describing the circuit design was published today in <em>Science</em>.</p> <p>In the future, pairs of spin waves could be fed into the circuit through dual channels, modulated for different properties, and combined to generate some measurable quantum interference — similar to how photon wave interference is used for quantum computing. Researchers hypothesize that such interference-based spintronic devices, like quantum computers, could execute highly complex tasks that conventional computers struggle with.</p> <p>“People are beginning to look for computing beyond silicon. Wave computing is a promising alternative,” says Luqiao Liu, a professor in the Department of Electrical Engineering and Computer Science (EECS) and principal investigator of the Spintronic Material and Device Group in the Research Laboratory of Electronics. “By using this narrow domain wall, we can modulate the spin wave and create these two separate states, without any real energy costs. We just rely on spin waves and intrinsic magnetic material.”</p> <p>Joining Liu on the paper are Jiahao Han, Pengxiang Zhang, and Justin T. Hou, three graduate students in the Spintronic Material and Device Group; and EECS postdoc Saima A. Siddiqui.</p> <p><strong>Flipping magnons</strong></p> <p>Spin waves are ripples of energy with small wavelengths. Chunks of the spin wave, which are essentially the collective spin of many electrons, are called magnons. While magnons are not true particles, like individual electrons, they can be measured similarly for computing applications.</p> <p>In their work, the researchers utilized a customized “magnetic domain wall,” a nanometer-sized barrier between two neighboring magnetic structures. They layered a pattern of cobalt/nickel nanofilms — each a few atoms thick — with certain desirable magnetic properties that can handle a high volume of spin waves. Then they placed the wall in the middle of a magnetic material with a special lattice structure, and incorporated the system into a circuit.</p> <p>On one side of the circuit, the researchers excited constant spin waves in the material. As the wave passes through the wall, its magnons immediately spin in the opposite direction: Magnons in the first region spin north, while those in the second region — past the wall —&nbsp;spin south. This causes the dramatic shift in the wave’s phase (angle) and slight decrease in magnitude (power).</p> <p>In experiments, the researchers placed a separate antenna on the opposite side of the circuit, that detects and transmits an output signal. Results indicated that, at its output state, the phase of the input wave flipped 180 degrees. The wave’s magnitude — measured from highest to lowest peak —&nbsp;had also decreased by a significant amount.</p> <p><strong>Adding some torque</strong></p> <p>Then, the researchers discovered a mutual interaction between spin wave and domain wall that enabled them to efficiently toggle between two states. Without the domain wall, the circuit would be uniformly magnetized; with the domain wall, the circuit has a split, modulated wave.</p> <p>By controlling the spin wave, they found they could control the position of the domain wall. This relies on a phenomenon called, “spin-transfer torque,” which is when spinning electrons essentially jolt a magnetic material to flip its magnetic orientation.</p> <p>In the researchers’ work, they boosted the power of injected spin waves to induce a certain spin of the magnons. This actually draws the wall toward the boosted wave source. In doing so, the wall gets jammed under the antenna — effectively making it unable to modulate waves and ensuring uniform magnetization in this state.</p> <p>Using a special magnetic microscope, they showed that this method causes a micrometer-size shift in the wall, which is enough to position it anywhere along the material block. Notably, the mechanism of magnon spin-transfer torque was proposed, but not demonstrated, a few years ago. “There was good reason to think this would happen,” Liu says. “But our experiments prove what will actually occur under these conditions.”</p> <p>The whole circuit is like a water pipe, Liu says. The valve (domain wall) controls how the water (spin wave) flows through the pipe (material). “But you can also imagine making water pressure so high, it breaks the valve off and pushes it downstream,” Liu says. “If we apply a strong enough spin wave, we can move the position of domain wall — except it moves slightly upstream, not downstream.”</p> <p>Such innovations could enable practical wave-based computing for specific tasks, such as the signal-processing technique, called “fast Fourier transform.” Next, the researchers hope to build a working wave circuit that can execute basic computations. Among other things, they have to optimize materials, reduce potential signal noise, and further study how fast they can switch between states by moving around the domain wall. “That’s next on our to-do list,” Liu says.</p> An MIT-invented circuit uses only a nanometer-wide “magnetic domain wall” to modulate the phase and magnitude of a spin wave, which could enable practical magnetic-based computing — using little to no electricity.Image courtesy of the researchers, edited by MIT NewsResearch, Computer science and technology, Nanoscience and nanotechnology, Spintronics, electronics, Energy, Quantum computing, Materials Science and Engineering, Design, Research Laboratory of Electronics, Electrical Engineering & Computer Science (eecs), School of Engineering 3 Questions: Dan Huttenlocher on the formation of the MIT Schwarzman College of Computing The inaugural dean shares an update on the process of building a college. Tue, 26 Nov 2019 14:15:01 -0500 Terri Park | MIT Schwarzman College of Computing <p><em>Since beginning his position in August, Dean Dan Huttenlocher has been working on developing the organizational structure of the new MIT Stephen A. Schwarzman College of Computing. He shares an update on the process of building the college and offers a glimpse into the plans for the new college headquarters.&nbsp; </em></p> <p><strong>Q: </strong>Can you give us a status update on the college?</p> <p><strong>A:</strong> We have been concentrating our efforts on developing an organizational plan for the college, drawing on last spring’s <a href="" target="_blank">College of Computing Task Force Working Group reports</a>, and discussions with the leadership of all of the schools and departments, the Faculty Policy Committee, and a number of other groups. The process has been ongoing and iterative, with the development of an approximately 20-page plan that has undergone substantial changes in response to feedback on previous versions.</p> <p>The latest draft of the plan was presented at the Institute Faculty meeting last Wednesday. It was sent to the entire faculty about three weeks ago and shared with student leadership as well. We expect to share it with the entire MIT community as soon as additional input from the faculty is reflected in the draft, and then to have the initial structure of the college in place by January.</p> <p>There will undoubtedly continue to be revisions to the organizational plan as we learn more, but I’m really excited to be moving forward with the implementation, some of which has already begun, such as academic implementation work led by Asu Ozdaglar and the initial startup of Social and Ethical Responsibilities of Computing led by David Kaiser and Julie Shah. Our work is just beginning, and in particular, new curricula, classes, and programs will be developed over time by academic units in the college, in partnership with others across MIT.</p> <p>I’m thankful to the MIT community for the tremendous amount of time and effort they have put into the initial planning of the MIT Schwarzman College of Computing.</p> <p><strong>Q: </strong>Last year MIT <a href="" target="_self">announced the location</a> for construction of the college’s new headquarters, near the intersection of Vassar and Main streets. What are the plans for the new building, and when is construction expected to be complete?<strong> </strong></p> <p><strong>A:</strong> The building’s central location will serve as an interdisciplinary hub. The new building will enable the growth of the faculty and bring together those from numerous departments, centers, and labs at MIT that integrate computing into their work, and it will provide convening spaces for classes, seminars, conferences, and interdisciplinary computing projects, in addition to much needed open areas for students across disciplines to meet, mingle, work, and collaborate.</p> <p>After an in-depth search and selection process, we have chosen Skidmore, Owings &amp; Merrill (SOM) to design the new building. SOM is a firm whose practice spans the fields of architecture, engineering, interior design, and urban planning. They have worked on thousands of projects around the world and have designed some of the most technically and environmentally advanced buildings, among them The New School in New York.&nbsp;</p> <p>We are currently early in the design with SOM, a process that began in October. Completion of the new college headquarters is slated for 2023.</p> <p><strong>Q: </strong>As the college begins to take shape, what has the reaction been so far?&nbsp;<strong> </strong></p> <p><strong>A: </strong>There has been widespread recognition of the importance of the MIT Schwarzman College of Computing and the changes that we are undertaking. Our colleagues at other top institutions are interested in what we are doing and how we are doing it, and some are already beginning to consider how they might make relevant changes at their university. No other academic institution is taking on the scale and scope of change that we are pursuing at MIT; reorganizing academic programs that involve many of the faculty and most of the students to position them for the computing age; changing how we develop what we teach in computing, changing how many of our research activities are organized to bring other fields together with computing and artificial intelligence, notably the social sciences, humanities, design, and the arts; and attending to the social and ethical responsibilities in both teaching and research.</p> Dean Dan Huttenlocher has been working on developing the organizational structure of the new MIT Stephen A. Schwarzman College of Computing. He answers three questions about building the college and offers a glimpse into the new college headquarters. MIT Schwarzman College of Computing, Electrical engineering and computer science (EECS), Artificial intelligence, Computer science and technology, Technology and society, Alumni/ae, Computer Science and Artificial Intelligence Laboratory (CSAIL), School of Engineering, School of Humanities Arts and Social Sciences Producing better guides for medical-image analysis Model quickly generates brain scan templates that represent a given patient population. Tue, 26 Nov 2019 13:56:05 -0500 Rob Matheson | MIT News Office <p>MIT researchers have devised a method that accelerates the process for creating and customizing templates used in medical-image analysis, to guide disease diagnosis. &nbsp;</p> <p>One use of medical image analysis is to crunch datasets of patients’ medical images and capture structural relationships that may indicate the progression of diseases. In many cases, analysis requires use of a common image template, called an “atlas,” that’s an average representation of a given patient population. Atlases serve as a reference for comparison, for example to identify clinically significant changes in brain structures over time.</p> <p>Building a template is a time-consuming, laborious process, often taking days or weeks to generate, especially when using 3D brain scans. To save time, researchers often download publicly available atlases previously generated by research groups. But those don’t fully capture the diversity of individual datasets or specific subpopulations, such as those with new diseases or from young children. Ultimately, the atlas can’t be smoothly mapped onto outlier images, producing poor results.</p> <p>In a paper being presented at the Conference on Neural Information Processing Systems in December, the researchers describe an automated machine-learning model that generates “conditional” atlases based on specific patient attributes, such as age, sex, and disease. By leveraging shared information from across an entire dataset, the model can also synthesize atlases from patient subpopulations that may be completely missing in the dataset.</p> <p>“The world needs more atlases,” says first author Adrian Dalca, a former postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and now a faculty member in radiology at Harvard Medical School and Massachusetts General Hospital. “Atlases are central to many medical image analyses. This method can build a lot more of them and build conditional ones as well.”</p> <p>Joining Dalca on the paper are Marianne Rakic, a visiting researcher in CSAIL; John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering and head of CSAIL’s Data Driven Inference Group; and Mert R. Sabuncu of Cornell University.</p> <p><strong>Simultaneous alignment and atlases</strong></p> <p>Traditional atlas-building methods run lengthy, iterative optimization processes on all images in a dataset. They align, say, all 3D brain scans to an initial (often blurry) atlas, and compute a new average image from the aligned scans. They repeat this iterative process for all images. This computes a final atlas that minimizes the extent to which all scans in the dataset must deform to match the atlas. Doing this process for patient subpopulations can be complex and imprecise if there isn’t enough data available.</p> <p>Mapping an atlas to a new scan generates a “deformation field,” which characterizes the differences between the two images. This captures structural variations, which can then be further analyzed. In brain scans, for instance, structural variations can be due to tissue degeneration at different stages of a disease.</p> <p>In previous work, Dalca and other researchers developed a neural network to rapidly align these images. In part, that helped speed up the traditional atlas-building process. “We said, ‘Why can’t we build conditional atlases while learning to align images at the same time?’” Dalca says.</p> <p>To do so, the researchers combined two neural networks: One network automatically learns an atlas at each iteration, and another — adapted from the previous research — simultaneously aligns that atlas to images in a dataset.</p> <p>In training, the joint network is fed a random image from a dataset encoded with desired patient attributes. From that, it estimates an attribute-conditional atlas. The second network aligns the estimated atlas with the input image, and generates a deformation field.</p> <p>The deformation field generated for each image pair is used to train a “loss function,” a component of machine-learning models that helps minimize deviations from a given value. In this case, the function specifically learns to minimize distances between the learned atlas and each image. The network continuously refines the atlas to smoothly align to any given image across the dataset.</p> <div class="cms-placeholder-content-video"></div> <p><strong>On-demand atlases</strong></p> <p>The end result is a function that’s learned how specific attributes, such as age, correlate to structural variations across all images in a dataset. By plugging new patient attributes into the function, it leverages all learned information across the dataset to synthesize an on-demand atlas — even if that attribute data is missing or scarce in the dataset.</p> <p>Say someone wants a brain scan atlas for a 45-year-old female patient from a dataset with information from patients aged 30 to 90, but with little data for women aged 40 to 50. The function will analyze patterns of how the brain changes between the ages of 30 to 90 and incorporate what little data exists for that age and sex. Then, it will produce the most representative atlas for females of the desired age. In their paper, the researchers verified the function by generating conditional templates for various age groups from 15 to 90.</p> <p>The researchers hope clinicians can use the model to build their own atlases quickly from their own, potentially small datasets. Dalca is now collaborating with researchers at Massachusetts General Hospital, for instance, to harness a dataset of pediatric brain scans to generate conditional atlases for younger children, which are hard to come by.</p> <p>A big dream is to build one function that can generate conditional atlases for any subpopulation, spanning birth to 90 years old. Researchers could log into a webpage, input an age, sex, diseases, and other parameters, and get an on-demand conditional atlas. “That would be wonderful, because everyone can refer to this one function as a single universal atlas reference,” Dalca says.</p> <p>Another potential application beyond medical imaging is athletic training. Someone could train the function to generate an atlas for, say, a tennis player’s serve motion. The player could then compare new serves against the atlas to see exactly where they kept proper form or where things went wrong.</p> <p>“If you watch sports, it’s usually commenters saying they noticed if someone’s form was off from one time compared to another,” Dalca says. “But you can imagine that it could be much more quantitative than that.”</p> With their model, researchers were able to generate on-demand brain scan templates of various ages (pictured) that can be used in medical-image analysis to guide disease diagnosis. Image courtesy of the researchersResearch, Computer science and technology, Algorithms, Imaging, Machine learning, Health care, Medicine, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering Zach Lieberman joins MIT Media Lab New adjunct associate professor combines fine arts and coding. Mon, 25 Nov 2019 13:10:01 -0500 Janine Liberty | MIT Media Lab <p>Artist and educator Zach Lieberman has been appointed as an adjunct associate professor of media arts and sciences at the Media Lab. As of the fall 2019 semester, he is teaching courses and working on projects at the lab under the aegis of his newly founded research group, <a href="" target="_blank">Future Sketches</a>.</p> <p>A new-media artist with a background in fine arts, Lieberman creates animations, public art, and installations that explore the relationship between computation, art, and movement. He holds degrees from Hunter College and Parsons School of Design, has been artist-in-residence at Ars Electronica Futurelab, Eyebeam, Dance Theater Workshop, and the Hangar Center for the Arts in Barcelona, and his work has been exhibited around the world. He is one of the co-founders of openFrameworks, a C++ library for creative coding.</p> <p>Lieberman is particularly drawn to coding as a mode of expression, comparing it to poetry in its dichotomy between precision and infinite variation. “What I like about poetry is that it’s an art form where you’re using really precise words in a certain order to describe what it means to be human, what it means to be alive. It’s an art form that’s about precision with language,” says Lieberman. “And coding is really about precision, too, with an artificial language. You’re using language in a very specific order to make something emerge.”</p> <p>His interest in code as a creative medium led Lieberman to found the School for Poetic Computation in 2013, an alternative school for art and technology in New York, where he continues to teach and advise. Lieberman also has a longstanding affinity for, and affiliation with, the Media Lab, citing John Maeda’s book “Design By Numbers” as a crucial influence. He worked with Golan Levin, a Media Lab alum from Maeda’s Aesthetics and Computation group, on a series of audiovisual projects under the moniker Tmema.</p> <p>Lieberman also points to Media Lab founding faculty member Muriel Cooper as an inspiration and exemplar; his research group’s name, Future Sketches, is an homage to her. “The name comes from Muriel Cooper, whose work means a lot to me. She has this letter that she wrote for <em>Plan Magazine</em> in 1980, with a <a href="">12-page spread</a> of all the work being done in her Visual Language Workshop. She finished that letter with, ‘This stands as a sketch for the future.’ My work is dedicated to exploring this tradition.”</p> <p>“We’re really thrilled to have Zach join us at the lab,” says Tod Machover, Muriel R. Cooper Professor of Music and Media, who directs the Opera of the Future research group and is academic head of the Program in Media Arts and Sciences. “In addition to carrying on the legacy of Muriel Cooper that’s so intrinsic to the lab in a playful and thoughtful way, Zach is also committed to mentorship and fostering creativity. He has already become a kind of artistic Pied Piper to many of our students, in the loveliest, most productive way. I believe that Zach’s work and pedagogy will have a profound impact on the future fabric of the Media Lab.”</p> Artwork by Zach LiebermanImage: Zach LiebermanMedia Lab, School of Architecture and Planning, Technology and society, Design, Computer science and technology, Faculty Smart systems for semiconductor manufacturing Lam Research Tech Symposium, co-hosted by MIT.nano and Microsystems Technology Lab, explores challenges, opportunities for the future of the industry. Mon, 25 Nov 2019 12:55:01 -0500 Amanda Stoll | MIT.nano <p>Integrating smart systems into manufacturing offers the potential to transform many industries.&nbsp;Lam Research, a founding member of the MIT.nano Consortium and a longtime member of the Microsystems Technology Lab (MTL) Microsystems Industrial Group, explored the challenges and opportunities smart systems bring to the semiconductor industry at its annual technical symposium, held at MIT in October.</p> <p>Co-hosted by MIT.nano and the MTL, the two-day event brought together Lam’s global technical staff, academic collaborators, and industry leaders with MIT faculty, students, and researchers to focus on software and hardware needed for smart manufacturing and process controls.</p> <p>Tim Archer, president and CEO of Lam Research, kicked off the first day, noting that “the semiconductor industry is more impactful to people's lives than ever before."&nbsp;</p> <p>“We stand at an innovation inflection point where smart systems will transform the way we work and live,” says Rick Gottscho, executive vice president and chief technology officer of Lam Research. “The event inspires us to make the impossible possible, through learning about exciting research opportunities that drive innovation, fostering collaboration between industry and academia to discover best-in-class solutions together, and engaging researchers and students in our industry. For all of us to realize the opportunities of smart systems, we have to embrace challenges, disrupt conventions, and collaborate.”</p> <p>The symposium featured speakers from MIT and Lam Research, as well as the University of California at Berkeley, Tsinghua University in Beijing, Stanford University, Winbond Electronics Corporation, Harting Technology Group, and GlobalFoundries, among others. Professors, corporate leaders, and MIT students came together over discussions of machine learning, micro- and nanofabrication, big data — and how it all relates to the semiconductor industry.</p> <p>“The most effective way to deliver innovative and&nbsp;lasting&nbsp;solutions is to combine our skills with others, working here on the MIT campus and beyond,” says Vladimir Bulović, faculty director of MIT.nano and the&nbsp;Fariborz Maseeh Chair in&nbsp;Emerging Technology. “The strength of this event was not only the fantastic mix&nbsp;of expertise and&nbsp;perspectives convened by Lam and MIT, but also the variety of&nbsp;opportunities it created for networking and connection.”</p> <p>Tung-Yi Chan, president of Winbond Electronics, a specialty memory integrated circuit company, set the stage on day one with his opening keynote, “Be a ‘Hidden Champion’ in the Fast-Changing Semiconductor Industry.” The second day’s keynote, given by&nbsp;Ron Sampson, senior vice president and general manager of US Fab Operations at GlobalFoundries, continued the momentum, addressing the concept that smart manufacturing is key to the future for semiconductors.</p> <p>“We all marvel at the seemingly superhuman capabilities that AI systems have recently demonstrated in areas of image classification, natural language processing, and autonomous navigation,” says Jesús del Alamo, professor of electrical engineering and computer science and former faculty director of MTL. “The symposium discussed the potential for smart tools to transform semiconductor manufacturing. This is a terrific topic for exploration in collaboration between semiconductor equipment makers and universities.”</p> <p>A series of plenary talks took place over the course of the symposium:</p> <ul> <li>“Equipment Intelligence: Fact or Fiction” – Rick Gottscho, executive vice president and chief technology officer at Lam Research</li> <li>“Machine Learning for Manufacturing: Opportunities and Challenges”&nbsp;– Duane Boning, the Clarence J. LeBel Professor in Electrical Engineering at MIT</li> <li>“Learning-based Diagnosis and Control for Nonequilibrium Plasmas”&nbsp;– Ali Mesbah, assistant professor of chemical and biomolecular engineering at the University of California at Berkeley</li> <li>“Reconfigurable Computing and AI Chips”<em>&nbsp;</em>– Shouyi Yin, professor and vice director of the Institute of Microelectronics at Tsinghua University</li> <li>“Moore’s Law Meets Industry 4.0”&nbsp;– Costas Spanos, professor at UC Berkeley</li> <li>“Monitoring Microfabrication Equipment and Processes Enabled by Machine Learning and Non-contacting Utility Voltage and Current Measurements”&nbsp;– Jeffrey H. Lang, the Vitesse Professor of Electrical Engineering at MIT, and Vivek R. Dave, director of technology at Harting, Inc. of North America</li> <li>“Big and Streaming Data in the Smart Factory”&nbsp;– Brian Anthony, associate director of MIT.nano and principal research scientist in the Institute of Medical Engineering and Sciences (IMES) and the Department of Mechanical Engineering at MIT</li> </ul> <p>Both days also included panel discussions. The first featured leaders in global development of smarter semiconductors: Tim Archer of Lam Research; Anantha Chandrakasan of MIT; Tung-Yi Chan of Winbond; Ron Sampson of GlobalFoundries; and Shaojun Wei of Tsinghua University. The second panel brought together faculty to talk about “graduating to smart systems”: Anette “Peko” Hosoi of MIT; Krishna Saraswat of Stanford University; Huaqiang Wu of Tsinghua University; and Costas Spanos of UC Berkeley.</p> <p>Opportunities specifically for startups and students to interact with industry and academic leaders capped off each day of the symposium. Eleven companies competed in a startup pitch session at the end of the first day, nine of which are associated with the MIT Startup Exchange — a program that promotes collaboration&nbsp;between MIT-connected startups and industry.&nbsp;Secure AI Labs, whose work focuses on easier data sharing while preserving data privacy, was deemed the winner by a panel of six venture capitalists. The startup received a convertible note investment provided by Lam Capital.&nbsp;HyperLight, a silicon photonics startup, and&nbsp;Southie Autonomy, a robotics startup, received honorable mentions, coming in second and third place, respectively.</p> <p>Day two concluded with a student poster session. Graduate students from MIT and Tsinghua University delivered 90-second pitches about their cutting-edge research in the areas of materials and devices, manufacturing and processing, and machine learning and modeling. The winner of the lightning pitch session was MIT’s Christian Lau for his work on a modern&nbsp;microprocessor built from complementary carbon nanotube transistors.</p> <p>The Lam Research Technical Symposium takes place annually and rotates locations between academic collaborators, MIT, Stanford University, Tsinghua University, UC Berkeley, and Lam’s headquarters in Fremont, California. The 2020 symposium will be held at UC Berkeley next fall.</p> The 2019 Lam Research Tech Symposium brought together Lam’s global technical staff, academic collaborators, and industry leaders with MIT faculty, students, and researchers for a two-day event on smart systems for semiconductor manufacturing.Photo: Lam ResearchMIT.nano, Manufacturing, Nanoscience and nanotechnology, Industry, Data, Computer science and technology, Electrical engineering and computer science (EECS), electronics, School of Engineering, Special events and guest speakers MIT art installation aims to empower a more discerning public With “In Event of Moon Disaster,” the MIT Center for Advanced Virtuality aims to educate the public on deepfakes with an alternative history of the moon landing. Mon, 25 Nov 2019 11:30:01 -0500 Suzanne Day | MIT Open Learning <p>Videos doctored by artificial intelligence, culturally known as “deepfakes,” are being created and shared by the public at an alarming rate. Using advanced computer graphics and audio processing to realistically emulate speech and mannerisms, deepfakes have the power to distort reality, erode truth, and spread misinformation. In a troubling example, researchers around the world have sounded the alarm that they carry significant potential to influence American voters in the 2020 elections.&nbsp;</p> <p>While technology companies race to develop ways to detect and control deepfakes on social media platforms, and lawmakers search for ways to regulate them, a team of artists and computer scientists led by the MIT Center for Advanced Virtuality have designed an art installation to empower and educate the public on how to discern reality from deepfakes on their own.</p> <p>“Computer-based misinformation is a global challenge,” says Fox Harrell, professor of digital media and of artificial intelligence at MIT and director of the MIT Center for Advanced Virtuality. “We are galvanized to make a broad impact on the literacy of the public, and we are committed to using AI not for misinformation, but for truth. We are pleased to bring onboard people such as our new XR Creative Director Francesca Panetta to help further this mission.”</p> <p>Panetta is the director of “In Event of Moon Disaster,” along with co-director Halsey Burgund, a fellow in the MIT Open Documentary Lab. She says, “We hope that our work will spark critical awareness among the public. We want them to be alert to what is possible with today’s technology, to explore their own susceptibility, and to be ready to question what they see and hear as we enter a future fraught with challenges over the question of truth.”</p> <p>With “In Event of Moon Disaster,” which opened Friday at the International Documentary Festival Amsterdam, the team has reimagined the story of the moon landing. Installed in a 1960s-era living room, audiences are invited to sit on vintage furniture surrounded by three screens, including a vintage television set. The screens play an edited array of vintage footage from NASA, taking the audience on a journey from takeoff into space and to the moon. Then, on the center television, Richard Nixon reads a contingency speech written for him by his speech writer, Bill Safire, “in event of moon disaster” which he was to read if the Apollo 11 astronauts had not been able to return to Earth. In this installation, Richard Nixon reads this speech from the Oval Office.</p> <div class="cms-placeholder-content-video"></div> <p>To recreate this moving elegy that never happened, the team used deep learning techniques and the contributions of a voice actor to build the voice of Richard Nixon, producing a synthetic speech working with the Ukranian-based company Respeecher. They also worked with Israeli company Canny AI to use video dialogue replacement techniques to study and replicate the movement of Nixon’s mouth and lips, making it look as though he is reading this very speech from the Oval Office. The resulting video is highly believable, highlighting the possibilities of deepfake technology today.</p> <p>The researchers chose to create a deepfake of this historical moment for a number of reasons: Space is a widely loved topic, so potentially engaging to a wide audience; the piece is apolitical and less likely to alienate, unlike a lot of misinformation; and, as the 1969 moon landing is an event widely accepted by the general public to have taken place, the deepfake elements will be starkly obvious.&nbsp;</p> <p>Rounding out the educational experience, “In Event of Moon Disaster” transparently provides information regarding what is possible with today’s technology, and the goal of increasing public awareness and ability to identify misinformation in the form of deepfakes. This will be in the form of newspapers written especially for the exhibit which detail the making of the installation, how to spot a deepfake, and the most current work being done in algorithmic detection. Audience participants will be encouraged to take this away.</p> <p>"Our goal was to use the most advanced artificial intelligence techniques available today to create the most believable result possible — and then point to it and say, ‘This is fake; here’s how we did it; and here’s why we did it,’” says Burgund.</p> <p>While the physical installation opens in November 2019 in Amsterdam, the team is building a web-based version that is expected to go live in spring 2020.</p> "In Event of Moon Disaster" reimagines the story of the first moon landing as if the Apollo 11 astronauts had not been able to return to Earth. It was created to highlight the concern about computer-based misinformation, or "deepfakes."Photo: Chris BoebelOffice of Open Learning, Augmented and virtual reality, Machine learning, Artificial intelligence, History, Space exploration, Film and Television, Arts, Computer Science and Artificial Intelligence Laboratory (CSAIL), Comparative Media Studies/Writing, NASA, Computer science and technology, Technology and society, History of science, School of Engineering, School of Humanities Arts and Social Sciences MIT conference focuses on preparing workers for the era of artificial intelligence As automation rises in the workplace, speakers explore ways to train students and reskill workers. Fri, 22 Nov 2019 16:35:55 -0500 Rob Matheson | MIT News Office <p>In opening yesterday’s AI and the Work of the Future Congress, MIT Professor Daniela Rus presented diverging views of how artificial intelligence will impact jobs worldwide.</p> <p>By automating certain menial tasks, experts think AI is poised to improve human quality of life, boost profits, and create jobs, said Rus, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science.</p> <p>Rus then quoted a World Economic Forum study estimating AI could help create 133 million new jobs worldwide over the next five years. Juxtaposing this optimistic view, however, she noted a recent survey that found about two-thirds of Americans believe machines will soon rob humans of their careers. “So, who is right? The economists, who predict greater productivity and new jobs? The technologists, who dream of creating better lives? Or the factory line workers who worry about unemployment?” Rus asked. “The answer is, probably all of them.”</p> <p>Her remarks kicked off an all-day conference in Kresge Auditorium that convened experts from industry and academia for panel discussions and informal talks about preparing humans of all ages and backgrounds for a future of AI automation in the workplace. The event was co-sponsored by CSAIL, the MIT Initiative on the Digital Economy (IDE), and the MIT Work of the Future Task Force, an Institute-wide effort launched in 2018 that aims to understand and shape the evolution of jobs during an age of innovation.</p> <p>Presenters were billed as “leaders and visionaries” rigorously measuring technological impact on enterprise, government, and society, and generating solutions. Apart from Rus, who also moderated a panel on dispelling AI myths, speakers included Chief Technology Officer of the United States Michael Kratsios; executives from Amazon, Nissan, Liberty Mutual, IBM, Ford, and Adobe; venture capitalists and tech entrepreneurs; representatives of nonprofits and colleges; journalists who cover AI issues; and several MIT professors and researchers.</p> <p>Rus, a self-described “technology optimist,” drove home a point that echoed throughout all discussions of the day: AI doesn’t automate jobs<em>,&nbsp;</em>it automates tasks. Rus quoted a recent McKinsey Global Institute study that estimated 45 percent of tasks that humans are paid to do can now be automated. But, she said, humans can adapt to work in concert with AI —&nbsp;meaning job tasks may change dramatically, but jobs may not disappear entirely. “If we make the right choices and the right investments, we can ensure that those benefits get distributed widely across our workforce and our planet,” Rus said.</p> <p><strong>Avoiding the “job-pocalypse”</strong></p> <p>Common topics throughout the day included reskilling veteran employees to use AI technologies; investing heavily in training young students in AI through tech apprenticeships, vocational programs, and other education initiatives; ensuring workers can make livable incomes; and promoting greater inclusivity in tech-based careers. The hope is to avoid, as one speaker put it, a “job-pocalypse,” where most humans will lose their jobs to machines.</p> <p>A panel moderated by David Mindell, the Dibner Professor of the History of Engineering and Manufacturing and a professor of aeronautics and astronautics, focused on how AI technologies are changing workflow and skills, especially within sectors resistant to change. Mindell asked panelists for specific examples of implementing AI technologies into their companies.</p> <p>In response, David Johnson, vice president of production and engineering at Nissan, shared an anecdote about pairing an MIT student with a 20-year employee in developing AI methods to autonomously predict car-part quality. In the end, the veteran employee became immersed in the technology and is now using his seasoned expertise to deploy it in other areas, while the student learned more about the technology’s real-world applications. “Only through this synergy, when you purposely pair these people with a common goal, can you really drive the skills forward … for mass new technology adoption and deployment,” Johnson said.</p> <p>In a panel about shaping public policies to ensure technology benefits society — which included U.S. CTO Kratsios — moderator Erik Brynjolfsson, director of IDE and a professor in the MIT Sloan School of Management, got straight to the point: “People have been dancing around this question: Will AI destroy jobs?”</p> <p>“Yes, it will — but not to the extent that people presume,” replied MIT Institute Professor Daron Acemoglu. AI, he said, will mostly automate mundane operations in white-collar jobs, which will free up humans to refine their creative, interpersonal, and other high-level skills for new roles. Humans, he noted, also won’t be stuck doing low-paying jobs, such as labeling data for machine-learning algorithms.</p> <p>“That’s not the future of work,” he said. “The hope is we use our amazing creativity and all these wonderful and technological platforms to create meaningful jobs in which humans can use their flexibility, creativity, and all the things … machines won’t be able to do — at least in the next 100 years.”</p> <p>Kratsios emphasized a need for public and private sectors to collaborate to reskill workers. Specifically, he pointed to the Pledge to the America’s Worker, the federal initiative that now has 370 U.S. companies committed to retraining roughly 4 million American workers for tech-based jobs over the next five years.</p> <p>Responding to an audience question about potential public policy changes, Kratsios echoed sentiments of many panelists, saying education policy should focus on all levels of education, not just college degrees. “A vast majority of our policies, and most of our departments and agencies, are targeted toward coaxing people toward a four-year degree,” Kratsios said. “There are incredible opportunities for Americans to live and work and do fantastic jobs that don’t require four-year degrees. So, [a change is] thinking about using the same pool of resources to reskill, or retrain, or [help students] go to vocational schools.”</p> <p><strong>Inclusivity and underserved populations</strong></p> <p>Entrepreneurs at the event explained how AI can help create diverse workforces. For instance, a panel about creating economically and geographically diverse workforces, moderated by Devin Cook, executive producer of IDE’s Inclusive Innovation Challenge, included Radha Basu, who founded Hewlett Packard’s operations in India in the 1970s. In 2012, Basu founded iMerit, which hires employees — half are young women and more than 80 percent come from underserved populations —&nbsp;to provide AI services for computer vision, machine learning, and other applications.</p> <p>A panel hosted by Paul Osterman, co-director of the MIT Sloan Institute for Work and Employment Research and an MIT Sloan professor, explored how labor markets are changing in the face of technological innovations. Panelist Jacob Hsu is CEO of Catalyte, which uses an AI-powered assessment test to predict a candidate’s ability to succeed as a software engineer, and hires and trains those who are most successful. Many of their employees don’t have four-year degrees, and their ages range from anywhere from 17 to 72.</p> <p>A “media spotlight” session, in which journalists discussed their reporting on the impact of AI on the workplace and the world, included David Fanning, founder and producer of the investigative documentary series FRONTLINE, which recently ran a documentary titled “In the Era of AI.” Fanning briefly discussed how, during his investigations, he learned about the profound effect AI is having on workplaces in the developing world, which rely heavily on manual labor, such as manufacturing lines.</p> <p>“What happens as automation expands, the manufacturing ladder that was opened to people in developing countries to work their way out of rural poverty — all that manufacturing gets replaced by machines,” Fanning said. “Will we end up across the world with people who have nowhere to go? Will they become the new economic migrants we have to deal with in the age of AI?”</p> <p><strong>Education: The great counterbalance</strong></p> <p>Elisabeth Reynolds, executive director for the MIT Task Force on the Work of the Future and of the MIT Industrial Performance Center, and Andrew McAfee, co-director of IDE and a principal research scientist at the MIT Sloan School of Management, closed out the conference and discussed next steps.</p> <p>Reynolds said the MIT Task Force on the Work of the Future, over the next year, will further study how AI is being adopted, diffused, and implemented across the U.S., as well as issues of race and gender bias in AI. In closing, she charged the audience with helping tackle the issues: “I would challenge everybody here to say, ‘What on Monday morning is [our] organization doing in respect to this agenda?’”&nbsp;</p> <p>In paraphrasing economist Robert Gordon, McAfee reemphasized the shifting nature of jobs in the era of AI: “We don’t have a job quantity problem, we have a job quality problem.”</p> <p>AI may generate more jobs and company profits, but it may also have numerous negative effects on employees. Proper education and training are keys to ensuring the future workforce is paid well and enjoys a high quality of life, he said: “Tech progress, we’ve known for a long time, is an engine of inequality. The great counterbalancing force is education.”</p> Daniela Rus (far right), director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), moderated a panel on dispelling the myths of AI technologies in the workplace. The AI and the Work of the Future Congress was co-organized by CSAIL, the MIT Initiative on the Digital Economy, and the MIT Work of the Future Task Force.Image: Andrew KubicaResearch, Computer science and technology, Algorithms, Computer Science and Artificial Intelligence Laboratory (CSAIL), Sloan School of Management, Technology and society, Jobs, Economics, Policy, Artificial intelligence, Machine learning, Innovation and Entrepreneurship (I&E), Business and management, Manufacturing, Careers, Special events and guest speakers How to design and control robots with stretchy, flexible bodies Optimizing soft robots to perform specific tasks is a huge computational problem, but a new model can help. Fri, 22 Nov 2019 00:00:00 -0500 Rob Matheson | MIT News Office <p>MIT researchers have invented a way to efficiently optimize the control and design of soft robots for target tasks, which has traditionally been a monumental undertaking in computation.</p> <p>Soft robots have springy, flexible, stretchy bodies that can essentially move an infinite number of ways at any given moment. Computationally, this represents a highly complex “state representation,” which describes how each part of the robot is moving. State representations for soft robots can have potentially millions of dimensions, making it difficult to calculate the optimal way to make a robot complete complex tasks.</p> <p>At the Conference on Neural Information Processing Systems next month, the MIT researchers will present a model that learns a compact, or “low-dimensional,” yet detailed state representation, based on the underlying physics of the robot and its environment, among other factors. This helps the model iteratively co-optimize movement control and material design parameters catered to specific tasks.</p> <p>“Soft robots are infinite-dimensional creatures that bend in a billion different ways at any given moment,” says first author Andrew Spielberg, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “But, in truth, there are natural ways soft objects are likely to bend. We find the natural states of soft robots can be described very compactly in a low-dimensional description. We optimize control and design of soft robots by learning a good description of the likely states.”</p> <p>In simulations, the model enabled 2D and 3D soft robots to complete tasks — such as moving certain distances or reaching a target spot —more quickly and accurately than current state-of-the-art methods. The researchers next plan to implement the model in real soft robots.</p> <p>Joining Spielberg on the paper are CSAIL graduate students Allan Zhao, Tao Du, and Yuanming Hu; Daniela Rus, director of CSAIL and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science; and Wojciech Matusik, an MIT associate professor in electrical engineering and computer science and head of the Computational Fabrication Group.</p> <div class="cms-placeholder-content-video"></div> <p><strong>“Learning-in-the-loop”</strong></p> <p>Soft robotics is a relatively new field of research, but it holds promise for advanced robotics. For instance, flexible bodies could offer safer interaction with humans, better object manipulation, and more maneuverability, among other benefits.</p> <p>Control of robots in simulations relies on an “observer,” a program that computes variables that see how the soft robot is moving to complete a task. In previous work, the researchers decomposed the soft robot into hand-designed clusters of simulated particles. Particles contain important information that help narrow down the robot’s possible movements. If a robot attempts to bend a certain way, for instance, actuators may resist that movement enough that it can be ignored. But, for such complex robots, manually choosing which clusters to track during simulations can be tricky.</p> <p>Building off that work, the researchers designed a “learning-in-the-loop optimization” method, where all optimized parameters are learned during a single feedback loop over many simulations. And, at the same time as learning optimization —&nbsp;or “in the loop” — the method also learns the state representation.</p> <p>The model employs a technique called a material point method (MPM), which simulates the behavior of particles of continuum materials, such as foams and liquids, surrounded by a background grid. In doing so, it captures the particles of the robot and its observable environment into pixels or 3D pixels, known as voxels, without the need of any additional computation. &nbsp;&nbsp;&nbsp;&nbsp;</p> <p>In a learning phase, this raw particle grid information is fed into a machine-learning component that learns to input an image, compress it to a low-dimensional representation, and decompress the representation back into the input image. If this “autoencoder” retains enough detail while compressing the input image, it can accurately recreate the input image from the compression.</p> <p>In the researchers’ work, the autoencoder’s learned compressed representations serve as the robot’s low-dimensional state representation. In an optimization phase, that compressed representation loops back into the controller, which outputs a calculated actuation for how each particle of the robot should move in the next MPM-simulated step.</p> <p>Simultaneously, the controller uses that information to adjust the optimal stiffness for each particle to achieve its desired movement. In the future, that material information can be useful for 3D-printing soft robots, where each particle spot may be printed with slightly different stiffness. “This allows for creating robot designs catered to the robot motions that will be relevant to specific tasks,” Spielberg says. “By learning these parameters together, you keep everything as synchronized as much as possible to make that design process easier.”</p> <p><strong>Faster optimization</strong></p> <p>All optimization information is, in turn, fed back into the start of the loop to train the autoencoder. Over many simulations, the controller learns the optimal movement and material design, while the autoencoder learns the increasingly more detailed state representation. “The key is we want that low-dimensional state to be very descriptive,” Spielberg says.</p> <p>After the robot gets to its simulated final state over a set period of time —&nbsp;say, as close as possible to the target destination —&nbsp;it updates a “loss function.” That’s a critical component of machine learning, which tries to minimize some error. In this case, it minimizes, say, how far away the robot stopped from the target. That loss function flows back to the controller, which uses the error signal to tune all the optimized parameters to best complete the task.</p> <p>If the researchers tried to directly feed all the raw particles of the simulation into the controller, without the compression step, “running and optimization time would explode,” Spielberg says. Using the compressed representation, the researchers were able to decrease the running time for each optimization iteration from several minutes down to about 10 seconds.</p> <p>The researchers validated their model on simulations of various 2D and 3D biped and quadruped robots. They researchers also found that, while robots using traditional methods can take up to 30,000 simulations to optimize these parameters, robots trained on their model took only about 400 simulations.</p> <p>"Our goal is to enable quantum leaps in the way engineers go from specification to design, prototyping, and programming of soft robots. In this paper, we explore the potential of co-optimizing the body and control system of a soft robot can lead the rapid creation of soft bodied robots customized to the tasks they have to do," Rus says.</p> <p>Deploying the model into real soft robots means tackling issues with real-world noise and uncertainty that may decrease the model’s efficiency and accuracy. But, in the future, the researchers hope to design a full pipeline, from simulation to fabrication, for soft robots.</p> An MIT-invented model efficiently and simultaneously optimizes control and design of soft robots for target tasks, which has traditionally been a monumental undertaking in computation. The model, for instance, was significantly faster and more accurate than state-of-the-art methods at simulating how quadrupedal robots (pictured) should move to reach target destinations.Image courtesy of the researchersResearch, Computer science and technology, Algorithms, Robots, Robotics, Soft robotics, Design, 3-D printing, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering Bot can beat humans in multiplayer hidden-role games Using deductive reasoning, the bot identifies friend or foe to ensure victory over humans in certain online games. Tue, 19 Nov 2019 23:59:59 -0500 Rob Matheson | MIT News Office <p>MIT researchers have developed a bot equipped with artificial intelligence that can beat human players in tricky online multiplayer games where player roles and motives are kept secret.</p> <p>Many gaming bots have been built to keep up with human players. Earlier this year, a team from Carnegie Mellon University developed the world’s first bot that can beat professionals in multiplayer poker. DeepMind’s AlphaGo made headlines in 2016 for besting a professional Go player. Several bots have also been built to beat professional chess players or join forces in cooperative games such as online capture the flag. In these games, however, the bot knows its opponents and teammates from the start.</p> <p>At the Conference on Neural Information Processing Systems next month, the researchers will present DeepRole, the first gaming bot that can win online multiplayer games in which the participants’ team allegiances are initially unclear. The bot is designed with novel “deductive reasoning” added into an AI algorithm commonly used for playing poker. This helps it reason about partially observable actions, to determine the probability that a given player is a teammate or opponent. In doing so, it quickly learns whom to ally with and which actions to take to ensure its team’s victory.</p> <p>The researchers pitted DeepRole against human players in more than 4,000 rounds of the online game “The Resistance: Avalon.” In this game, players try to deduce their peers’ secret roles as the game progresses, while simultaneously hiding their own roles. As both a teammate and an opponent, DeepRole consistently outperformed human players.</p> <p>“If you replace a human teammate with a bot, you can expect a higher win rate for your team. Bots are better partners,” says first author Jack Serrino ’18, who majored in electrical engineering and computer science at MIT and is an avid online “Avalon” player.</p> <p>The work is part of a broader project to better model how humans make socially informed decisions. Doing so could help build robots that better understand, learn from, and work with humans.</p> <p>“Humans learn from and cooperate with others, and that enables us to achieve together things that none of us can achieve alone,” says co-author Max Kleiman-Weiner, a postdoc in the Center for Brains, Minds and Machines and the Department of Brain and Cognitive Sciences at MIT, and at Harvard University. “Games like ‘Avalon’ better mimic the dynamic social settings humans experience in everyday life. You have to figure out who’s on your team and will work with you, whether it’s your first day of kindergarten or another day in your office.”</p> <p>Joining Serrino and Kleiman-Weiner on the paper are David C. Parkes of Harvard and Joshua B. Tenenbaum, a professor of computational cognitive science and a member of MIT’s Computer Science and Artificial Intelligence Laboratory and the Center for Brains, Minds and Machines.</p> <p><strong>Deductive bot</strong></p> <p>In “Avalon,” three players are randomly and secretly assigned to a “resistance” team and two players to a “spy” team. Both spy players know all players’ roles. During each round, one player proposes a subset of two or three players to execute a mission. All players simultaneously and publicly vote to approve or disapprove the subset. If a majority approve, the subset secretly determines whether the mission will succeed or fail. If two “succeeds” are chosen, the mission succeeds; if one “fail” is selected, the mission fails. Resistance players must always choose to succeed, but spy players may choose either outcome. The resistance team wins after three successful missions; the spy team wins after three failed missions.</p> <p>Winning the game basically comes down to deducing who is resistance or spy, and voting for your collaborators. But that’s actually more computationally complex than playing chess and poker. “It’s a game of imperfect information,” Kleiman-Weiner says. “You’re not even sure who you’re against when you start, so there’s an additional discovery phase of finding whom to cooperate with.”</p> <p>DeepRole uses a game-planning algorithm called “counterfactual regret minimization” (CFR) — which learns to play a game by repeatedly playing against itself — augmented with deductive reasoning. At each point in a game, CFR looks ahead to create a decision “game tree” of lines and nodes describing the potential future actions of each player. Game trees represent all possible actions (lines) each player can take at each future decision point. In playing out potentially billions of game simulations, CFR notes which actions had increased or decreased its chances of winning, and iteratively revises its strategy to include more good decisions. Eventually, it plans an optimal strategy that, at worst, ties against any opponent.</p> <p>CFR works well for games like poker, with public actions — such as betting money and folding a hand — but it struggles when actions are secret. The researchers’ CFR combines public actions and consequences of private actions to determine if players are resistance or spy.</p> <p>The bot is trained by playing against itself as both resistance and spy. When playing an online game, it uses its game tree to estimate what each player is going to do. The game tree represents a strategy that gives each player the highest likelihood to win as an assigned role. The tree’s nodes contain “counterfactual values,” which are basically estimates for a payoff that player receives if they play that given strategy.</p> <p>At each mission, the bot looks at how each person played in comparison to the game tree. If, throughout the game, a player makes enough decisions that are inconsistent with the bot’s expectations, then the player is probably playing as the other role. Eventually, the bot assigns a high probability for each player’s role. These probabilities are used to update the bot’s strategy to increase its chances of victory.</p> <p>Simultaneously, it uses this same technique to estimate how a third-person observer might interpret its own actions. This helps it estimate how other players may react, helping it make more intelligent decisions. “If it’s on a two-player mission that fails, the other players know one player is a spy. The bot probably won’t propose the same team on future missions, since it knows the other players think it’s bad,” Serrino says.</p> <p><strong>Language: The next frontier</strong></p> <p>Interestingly, the bot did not need to communicate with other players, which is usually a key component of the game. “Avalon” enables players to chat on a text module during the game. “But it turns out our bot was able to work well with a team of other humans while only observing player actions,” Kleiman-Weiner says. “This is interesting, because one might think games like this require complicated communication strategies.”</p> <p>“I was thrilled to see this paper when it came out,” says Michael Bowling, a professor at the University of Alberta whose research focuses, in part, on training computers to play games. “It is really exciting seeing the ideas in DeepStack see broader application outside of poker. [DeepStack has] been so central to AI in chess and Go to situations of imperfect information. But I still wasn't expecting to see it extended so quickly into the situation of a hidden role game like Avalon. Being able to navigate a social deduction scenario, which feels so quintessentially human, is a really important step. There is still much work to be done, especially when the social interaction is more open ended, but we keep seeing that many of the fundamental AI algorithms with self-play learning can go a long way.”</p> <p>Next, the researchers may enable the bot to communicate during games with simple text, such as saying a player is good or bad. That would involve assigning text to the correlated probability that a player is resistance or spy, which the bot already uses to make its decisions. Beyond that, a future bot might be equipped with more complex communication capabilities, enabling it to play language-heavy social-deduction games — such as a popular game “Werewolf” —which involve several minutes of arguing and persuading other players about who’s on the good and bad teams.</p> <p>“Language is definitely the next frontier,” Serrino says. “But there are many challenges to attack in those games, where communication is so key.”</p> DeepRole, an MIT-invented gaming bot equipped with “deductive reasoning,” can beat human players in tricky online multiplayer games where player roles and motives are kept secret.Research, Computer science and technology, Algorithms, Video games, Artificial intelligence, Machine learning, Language, Computer Science and Artificial Intelligence Laboratory (CSAIL), Brain and cognitive sciences, Electrical Engineering & Computer Science (eecs), School of Engineering Students push to speed up artificial intelligence adoption in Latin America To help the region catch up, students organize summit to bring Latin policymakers and researchers to MIT. Tue, 19 Nov 2019 16:30:01 -0500 Kim Martineau | MIT Quest for Intelligence <p>Omar Costilla Reyes reels off all the ways that artificial intelligence might benefit his native Mexico. It could raise living standards, he says, lower health care costs, improve literacy and promote greater transparency and accountability in government.</p> <p>But Mexico, like many of its Latin American neighbors, has failed to invest as heavily in AI as other developing countries. That worries <a href="">Costilla Reyes</a>, a postdoc at MIT’s Department of Brain and Cognitive Sciences.</p> <p>To give the region a nudge, Costilla Reyes and three other MIT graduate students — <a href="" target="_blank">Guillermo Bernal</a>, <a href="">Emilia Simison</a> and <a href="">Pedro Colon-Hernandez</a> — have spent the last six months putting together a three-day event that will &nbsp;bring together policymakers and AI researchers in Latin America with AI researchers in the United States. The <a href="">AI Latin American sumMIT</a> will take place in January at the <a href="">MIT Media Lab</a>.</p> <p>“Africa is getting lots of support — Africa will eventually catch up,” Costilla Reyes says. “You don’t see anything like that in Latin America, despite the potential for AI to move the region forward socially and economically.”</p> <p><strong>Four paths to MIT and research inspired by AI</strong></p> <p>Each of the four students took a different route to MIT, where AI plays a central role in their work — on the brain, voice assistants, augmented creativity and politics. Costilla Reyes got his first computer in high school, and though it had only dial-up internet access, it exposed him to a world far beyond his home city of Toluca. He studied for a PhD &nbsp;at the University of Manchester, where he developed an <a href="">AI system</a> with applications in security and health to identify individuals by their gait. At MIT, Costilla Reyes is building computational models of how firing neurons in the brain produce memory and cognition, information he hopes can also advance AI.</p> <p>After graduating from a vocational high school in El Salvador, Bernal moved in with relatives in New Jersey and studied English at a nearby community college. He continued on to Pratt Institute, where he learned to incorporate Python into his design work. Now at the MIT Media Lab, he’s developing interactive storytelling tools like <a href="">PaperDreams</a> that uses AI to help people unlock their creativity. His work recently won a <a href="">Schnitzer Prize</a>.&nbsp;</p> <p>Simison came to MIT to study for a PhD in political science after her professors at Argentina’s University Torcuato Di Tella encouraged her to continue her studies in the United States. She is currently using text analysis tools to mine archival records in Brazil and Argentina to understand the role that political parties and unions played under the last dictatorships in both countries.</p> <p>Colon-Hernandez grew up in Puerto Rico fascinated with video games. A robotics class in high school inspired him to build a computer to play video games of his own, which led to a degree in computer engineering at the University of Puerto Rico at Mayagüez.&nbsp;After helping a friend with a project at MIT Lincoln Laboratory, Colon-Hernandez applied to a summer research program at MIT, and later, the MIT Media Lab’s graduate program. He’s currently working on intelligent voice assistants.</p> <p>It’s hard to generalize about a region as culturally diverse and geographically vast as Latin America, stretching from Mexico and the Caribbean to the tip of South America. But protests, violence and reports of entrenched corruption have dominated the news for years, and the average income per person has been <a href="">falling</a> with respect to the United States since the 1950s. All four students see AI as a means to bring stability and greater opportunity to their home countries.</p> <p><strong>AI with a humanitarian agenda</strong></p> <p>The idea to bring Latin American policymakers to MIT was hatched last December, at the world’s premier conference for AI research, <a href="">NeurIPS</a>. The organizers of NeurIPS had launched several new workshops to promote diversity in response to growing criticism of the exclusion of women and minorities in tech. At <a href="">Latinx,</a> a workshop for Latin American students, Costilla Reyes met Colon-Hernandez, who was giving a talk on voice-activated wearables. A few hours later they began drafting a plan to bring a Latinx-style event to MIT.</p> <p>Back in Cambridge, they found support from <a href="">Armando Solar-Lezama</a>, a <a href="">native of Mexico</a> and a professor at MIT’s <a href="">Department of Electrical Engineering and Computer Science</a>. They also began knocking on doors for funding, securing an initial $25,000 grant from MIT’s <a href="">Institute Community and Equity Office</a>. Other graduate students joined the cause, including, and together they set out to recruit speakers, reserve space at the MIT Media Lab and design a website. RIMAC, the MIT-IBM Watson AI Lab, X Development, and Facebook have all since offered support for the event.</p> <p>Unlike other AI conferences, this one has a practical bent, with themes that echo many of the UN Sustainable Development Goals: to end extreme poverty, develop quality education, create fair and transparent institutions, address climate change and provide good health.</p> <p>The students have set similarly concrete goals for the conference, from mapping the current state of AI-adoption across Latin America to outlining steps policymakers can take to coordinate efforts. U.S. researchers will offer tutorials on open-source AI platforms like TensorFlow and scikit-learn for Python, and the students are continuing to raise money to fly 10 of their counterparts from Latin America to attend the poster session.</p> <p>“We reinvent the wheel so much of the time,” says Simison. “If we can motivate countries to integrate their efforts, progress could move much faster.”</p> <p>The potential rewards are high. A <a href="">2017 report</a> by Accenture estimated that if AI were integrated into South America’s top five economies — Argentina, Brazil, Chile, Colombia and Peru — which generate about 85 percent of the continent’s economic output, they could each add up to 1 percent to their annual growth rate.</p> <p>In developed countries like the U.S. and in Europe, AI is sometimes viewed apprehensively for its potential to eliminate jobs, spread misinformation and perpetuate bias and inequality. But the risk of not embracing AI, especially in countries that are already lagging behind economically, is potentially far greater, says Solar-Lezama. “There’s an urgency to make sure these countries have a seat at the table and can benefit from what will be one of the big engines for economic development in the future,” he says.</p> <p>Post-conference deliverables include a set of recommendations for policymakers to move forward. “People are protesting across the entire continent due to the marginal living conditions that most face,” says Costilla Reyes. “We believe that AI plays a key role now, and in the future development of the region, if it’s used in the right way.”</p> “We believe that AI plays a key role now, and in the future development of the region, if it’s used in the right way,” says Omar Costilla Reyes, one of four MIT graduate students working to help Latin America adopt artificial intelligence technologies. Pictured here (left to right) are Costilla Reyes, Emilia Simison, Pedro Antonio Colon-Hernandez, and Guillermo Bernal.Photo: Kim MartineauQuest for Intelligence, Electrical engineering and computer science (EECS), Media Lab, Brain and cognitive sciences, Lincoln Laboratory, MIT-IBM Watson AI Lab, School of Engineering, School of Science, School of Humanities Arts and Social Sciences, Artificial intelligence, Computer science and technology, Technology and society, Machine learning, Software, Algorithms, Political science, Latin America Algorithm may improve brain-controlled prostheses and exoskeletons An improved method for magnet tracking enables high-speed wireless tracking through various materials. Mon, 18 Nov 2019 15:55:01 -0500 Stephanie Strom | MIT Media Lab <p>A team of researchers at the MIT Media Lab has devised an <a href="">algorithm</a> that promises to vastly improve the simultaneous tracking of any number of magnets. This has significant implications for prostheses, augmented reality, robotics, and other fields.&nbsp;</p> <p>Graduate student Cameron Taylor, lead researcher on the approach in the Media Lab’s Biomechatronics group, says the algorithm dramatically reduces the time it takes for sensors to determine the positions and orientations of magnets embedded in the body, wood, ceramics, and other materials.</p> <p>“I’ve been dreaming for years about a minimally invasive approach to controlling prostheses, and magnets offer that potential,” says Hugh Herr, professor of media arts and sciences at MIT and head of the Biomechatronics group. “But previous techniques were too slow to track tissue movement in real time at high bandwidth.”</p> <p>The <a href="" target="_blank">work</a>, "Low-Latency Tracking of Multiple Permanent Magnets," has been published by <em>IEEE Sensors Journal.</em> MIT undergraduate Haley Abramson is also a co-author.</p> <div class="cms-placeholder-content-video"></div> <p><strong>Real-time tracking</strong></p> <p>For years, prostheses have relied on electromyography to interpret messages from a user’s peripheral nervous system. Electrodes attached to the skin adjacent to muscles measure impulses delivered by the brain to activate them.</p> <p>It’s a less-than-perfect system. The ability of electrodes to sense signals that change over time, as well as to estimate the length and speed&nbsp; of muscle movement, is limited, and wearing the devices can be uncomfortable.&nbsp;</p> <p>Scientists have long attempted to figure out a way of using magnets, which can be embedded in the body indefinitely, to control high-speed robotics. But they kept running into a big hurdle: It took computers too long to determine precisely where the magnets were and initiate a reaction.&nbsp;</p> <p>“The software needs to guess at where the magnets are, and in what orientation,” Taylor said. “It checks how good its guess is given the magnetic field it sees, and when it’s wrong, it guesses again and again until it homes in on the location.”</p> <p>That process, which Taylor <a href="" target="_blank">compares to a game of Hot and Cold</a>, takes a lot of calculation, which delays movement. “Robotic control systems require very high speeds in terms of reactiveness,” Herr says. “If the time between sensing and actuation by an engineered platform is too long, device instability can occur.”</p> <p>To decrease the time delay in magnet tracking, a computer would need to quickly identify which direction was “warmest” before making a guess about a magnet’s location. Taylor was lying on the floor at home one day pondering this problem when it struck him that the “warmest” direction could be calculated very quickly using simple computer coding techniques.&nbsp;</p> <p>“I knew immediately that it was possible, which was extremely exciting. But I still had to validate it,” he says.</p> <p>Once validated, Taylor and members of his research team had to solve another problem that complicates magnet tracking: disturbance from the Earth’s magnetic field. Traditional methods of eliminating that interference weren’t practical for the type of compact, mobile system needed for prostheses and exoskeletons.&nbsp;</p> <p>The team landed on an elegant solution by programming their computer software to search for the Earth’s magnetic field as if it is simply another magnetic signal.&nbsp;</p> <p>They then tested their algorithm using a system with an array of magnetometers tracking as many as four tiny, pearl-like magnets. The test demonstrated that, in comparison to state-of-the-art magnet tracking systems, the new algorithm increased maximum bandwidths by 336 percent, 525 percent, 635 percent, and 773 percent when used to simultaneously track one, two, three, and four magnets respectively.&nbsp;</p> <p>Taylor stressed that a handful of other researchers have used the same derivative approach for tracking, but did not demonstrate the tracking of multiple moving magnets in real time. “This is the first time a team has demonstrated this technique for real-time tracking of several permanent magnets at once,” he says.</p> <p>And such tracking has never been deployed in the past as a means of speeding up magnetic tracking. “All implementations in the past have used high-level computer languages without the techniques we use to enhance speed,” Taylor says.&nbsp;</p> <p>The new algorithm means, according to Taylor and Herr, that magnetic target tracking can be extended to high-speed, real-time applications that require tracking of one or more targets, eliminating the need for a fixed magnetometer array. Software enabled with the new algorithm could greatly enhance reflexive control of protheses and exoskeletons, simplify magnetic levitation, and improve interaction with augmented and virtual reality devices.&nbsp;</p> <p>“All kinds of technology exists to implant into the nervous system or muscles for controlling mechatronics, but typically there is a wire across the skin boundary or electronics embedded inside the body to do transmission,” Herr says. “The beauty of this approach is that you’re injecting small passive magnetic beads into the body, and all the technology stays outside the body.”</p> <p><strong>Numerous applications</strong></p> <p>The Biomechatronics group is primarily interested in using its new findings to improve control of prostheses, but Hisham Bedri, a graduate of the Media Lab who works in augmented reality, says potential applications of the advances are huge in the consumer market. “If you wanted to step into the virtual reality world and, say, kick a ball, this is super useful for something like that,” Bedri says. “This brings that future closer to a reality.”&nbsp;</p> <p>People are already injecting themselves with tiny magnets in hopes of using them to enhance the body’s natural performance, and this raises an interesting question about public policy, Herr says. “When ‘normal’ people want to be implanted with magnets to improve bodily function, how do we think about that?” he says. “It’s not a medical device or application, so under what regulatory body will we allow Joe and Suzy to do that? We need a vigorous policy discussion around this question.”&nbsp;</p> <p>The group has applied for a patent on its algorithm and its method for using magnets to track muscle movement. It is also working with the U.S. Food and Drug Administration on guidance for the transition of high-speed, broad bandwidth magnetic tracking into the clinical realm.</p> <p>Now the researchers are preparing to do preclinical work to validate that this technique will work for tracking human tissues and controlling prostheses and exoskeletons. “I think it’s possible we would begin human testing as soon as next year,” Herr says. “This isn’t something that’s 10 years out at all.”&nbsp;</p> <p>Beyond that? “Our long-term vision for the future is that we inject these magnets into you and me and use them to operate a non-militant Iron Man suit — everyone would be walking around with superhero strength,” Taylor says, only half in jest. “Seriously, though, I do think this is the missing piece to let us finally take magnet tracking and move it to a place where it can be used far more widely.”&nbsp;</p> <p>Full image and video credits are <a href="" target="_blank">available via the Media Lab</a>.</p> Two permanent magnets are tracked with magnetic field sensors. MIT engineers have devised an algorithm for high-speed tracking of any number of magnets, with significant implications for augmented reality and prosthesis control. Image: Jimmy Day/MIT Media Lab and IEEE Sensors Journal/IEEEMedia Lab, Bionics, Magnets, Algorithms, Prosthetics, Research, Computer science and technology, School of Architecture and Planning PhD student Marc Aidinoff explores how technology impacts public policy Historian&#039;s research focuses on understanding how visions for social and economic policy are tied to changing ideas about technology. Mon, 18 Nov 2019 14:50:01 -0500 School of Humanities, Arts, and Social Sciences <p>“Computers have encapsulated so many collective hopes and fears for the future,” says Marc Aidinoff, a PhD candidate in History/Anthropology/Science, Technology, and Society (HASTS), a doctoral program that draws on the expertise of three fields in MIT's School of Humanities, Arts, and Social Sciences (SHASS).</p> <p>“In the 1990s, you have Vice President Gore, President Clinton, and the Rev. Jesse Jackson saying that closing the digital divide was a fundamental civil rights issue of our times. What does it mean when civil rights become about access to computers and the internet? When lack of internet access is considered a form of poverty? These are really big questions and I haven’t been able to get them out of my system.”</p> <p><strong>How is social policy tied to ideas about technology?</strong></p> <p>Aidinoff has become dedicated to understanding how policymakers have thought about technology. It makes sense. After graduating from Harvard University, Aidinoff worked for Barack Obama's presidential campaign and subsequently joined the administration working as a policy advisor for three years — including a two-year stint as the assistant policy director for Vice President Joe Biden.</p> <p>“But these questions were getting under my skin,” Aidinoff explains. “I wanted to know how visions for social and economic policy were tied to changing ideas about technology. So I became a card-carrying historian who pokes around archives from Mississippi to D.C., trying to get answers.”</p> <p><strong>Restructuring the citizen’s relationship to the state</strong></p> <p>The story in Aidinoff’s dissertation project begins in 1984, with the breakup of the Bell System and the launch of the Macintosh computer. That was also the year the U.S. federal government began measuring citizens’ access to computers. The dissertation traces policies designed to democratize information and the implementation of massive systems built to digitize the U.S. government.</p> <p>“Networked computing,” Aidinoff argues, “has been part of a larger restructuring of the citizen’s relationship to the state in U.S. history. For example, when you see a welfare caseworker, and there is a computer on their desk — does it matter who wrote that software?”</p> <p>The Horowitz Foundation for Social Policy presented Aidinoff with its John Stanley Award for History and Ethics earlier this year to support his efforts and fund his research trips.</p> <p>Aidinoff’s research has sent him searching for some of the same types of information he reviewed and generated as a policy advisor. He lights up when talking about a visit to the George H. W. Bush Presidential Library and Museum in College Station, Texas, to examine a hodgepodge of materials from policy memos to computer manuals. These archives help him understand how information moved around the executive branch and how policymakers would have understood technological systems.</p> <p><strong>The archive you need</strong></p> <p>Reading through the documents he locates can be difficult, however; Aidinoff credits the HASTS program for sharpening his research skills so he can home in on what is essential.</p> <p>“The HASTS faculty are really good at teaching you how to be unsatisfied until you’ve figured out how to construct the archive that you think is right for the question you’re asking. For me, that has meant a lot of newspapers and computer manuals. There’s a real belief among historians of science and technology that you need to go out and construct the archive you need. Archives aren’t just things that are waiting for you to discover. You’re going to need to go out and be creative.”</p> <p>“HASTS pushed me harder than I expected. I knew MIT would be challenging, but my colleagues encouraged me to spend time in places where I was less comfortable, including rural Mississippi.”</p> <p><strong>The humanistic/technical synergy at MIT</strong></p> <p>In fact, Aidinoff spent a semester at the University of Mississippi and the most recent summers teaching college-bridge courses to high school students in the Mississippi Delta with the Freedom Summer Collegiate program — an organization that continues the work of the 1964 Freedom Summer.</p> <p>For Aidinoff, there is no question that SHASS is the best place to continue his studies. The combination of rich humanities research programs and surrounding science and technology expertise was exactly what he wanted.</p> <p>“You’ve got such amazing people, world-class historians and historians of science and technology. The people I get to work with in a small, loving, interdisciplinary department is pretty extraordinary. My friends are technical, and being technical is really valued. I hang out with computer scientists all the time, which is great. I couldn’t do what I do if I didn’t have people pushing back on me from a social science perspective and from a technical engineering perspective.”</p> <p>Aidinoff’s position with the MIT Computer Science and Artificial Intelligence Laboratory’s Internet Policy Research Initiative has complemented the perspective of his home department in SHASS.</p> <p><strong>Knowledge is social</strong><br /> <br /> “A key lesson from the history of science and technology is that knowledge is social. Some knowledge comes from sitting and thinking, and that’s important. But over and over again we learn it’s sitting and thinking and then going and having lunch or a coffee with people in your discipline and across disciplines.</p> <p>“I don’t think I’ll ever again be in a community with this many historians of science per square mile. It’s just an incredibly exciting community. And it’s social. We think these questions really matter, so it’s worth looking up from the book, too, and having the discussion where you fight about them because these are real live questions with political consequences.”</p> <p><br /> <span style="font-size:12px;"><em>Story prepared by SHASS Communications<br /> Writer, photographer: Maria Iacobo </em></span></p> "What does it mean," Aidinoff asks "when civil rights become about access to computers and the internet? When lack of internet access is considered a form of poverty? These questions were getting under my skin," he says. "I wanted to know how social and economic policy were tied to changing ideas about technology."Photo: Maria IacoboSchool of Humanities Arts and Social Sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Students, Anthropology, History, History of science, Policy, Civil rights, Program in STS, Computer science and technology, graduate, Graduate, postdoctoral