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    Media Advisory — MIT researchers: AI policy needed to manage impacts, build more equitable systems

    On Thursday, May 6 and Friday, May 7, the AI Policy Forum — a global effort convened by researchers from MIT — will present their initial policy recommendations aimed at managing the effects of artificial intelligence and building AI systems that better reflect society’s values. Recognizing that there is unlikely to be any singular national AI policy, but rather public policies for the distinct ways in which we encounter AI in our lives, forum leaders will preview their preliminary findings and policy recommendations in three key areas: finance, mobility, and health care.

    The inaugural AI Policy Forum Symposium, a virtual event hosted by the MIT Schwarzman College of Computing, will bring together AI and public policy leaders, government officials from around the world, regulators, and advocates to investigate some of the pressing questions posed by AI in our economies and societies. The symposium’s program will feature remarks from public policymakers helping shape governments’ approaches to AI; state and federal regulators on the front lines of these issues; designers of self-driving cars and cancer-diagnosing algorithms; faculty examining the systems used in emerging finance companies and associated concerns; and researchers pushing the boundaries of AI.

    WHAT: AI Policy Forum (AIPF) Symposium

    WHO:MIT speakers: 

    Martin A. Schmidt, MIT provost
    Daniel Huttenlocher, AIPF chair and dean of the MIT Schwarzman College of Computing
    Regina Barzilay, MIT School of Engineering Distinguished Professor of AI and Health; AI faculty lead of the Jameel Clinic at MIT
    Daniel Weitzner, founding director of the MIT Internet Policy Research Initiative; former U.S. deputy chief technology officer in the Office of Science and Technology Policy
    Luis Videgaray, senior lecturer in the MIT Sloan School of Management; former foreign minister and minister of finance of Mexico
    Aleksander Madry, professor of computer science in the MIT Department of Electrical Engineering and Computer Science
    R. David Edelman, director of public policy for the MIT Internet Policy Research Initiative; former special assistant to U.S. President Barack Obama for economic and technology policy
    Julie Shah, MIT associate professor of aeronautics and astronautics; associate dean of social and ethical responsibilities of computing in the MIT Schwarzman College of Computing
    Andrew Lo, professor of finance in the MIT Sloan School of Management

    Guest speakers and participants: 

    Julie Bishop, chancellor of the Australian National University; former minister of foreign affairs and member of the Parliament of Australia
    Andrew Wyckoff, director for science, technology and innovation at the Organization for Economic Cooperation and Development (OECD)
    Martha Minow, 300th Anniversary University Professor at Harvard Law School; former dean of the Harvard Law School
    Alejandro Poiré, dean of the School of Public Policy at Monterrey Tec; former secretary of the interior of Mexico
    Ngaire Woods, dean of the Blavatnik School of Government at the University of Oxford
    Darran Anderson, director of strategy and innovation at the Texas Department of Transportation
    Nat Beuse, vice president of security at Aurora; former head safety regulator for autonomous vehicles at the U.S. Department of Transportation
    Laura Major, chief technology officer of Motional
    Manuela Veloso, head of AI research at JP Morgan Chase
    Stephanie Lee, managing director of BlackRock Systematic Active Equities Emerging Markets

    WHEN: Thursday and Friday, May 6 and 7

    Media RSVP:Reporters interested in attending can register here. More information on the AI Policy Forum can be found here.  More

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    Nano flashlight enables new applications of light

    In work that could someday turn cell phones into sensors capable of detecting viruses and other minuscule objects, MIT researchers have built a powerful nanoscale flashlight on a chip.

    Their approach to designing the tiny light beam on a chip could also be used to create a variety of other nano flashlights with different beam characteristics for different applications. Think of a wide spotlight versus a beam of light focused on a single point.

    For many decades, scientists have used light to identify a material by observing how that light interacts with the material. They do so by essentially shining a beam of light on the material, then analyzing that light after it passes through the material. Because all materials interact with light differently, an analysis of the light that passes through the material provides a kind of “fingerprint” for that material. Imagine doing this for several colors — i.e., several wavelengths of light — and capturing the interaction of light with the material for each color. That would lead to a fingerprint that is even more detailed.

    Most instruments for doing this, known as spectrometers, are relatively large. Making them much smaller would have a number of advantages. For example, they could be portable and have additional applications (imagine a futuristic cell phone loaded with a self-contained sensor for a specific gas). However, while researchers have made great strides toward miniaturizing the sensor for detecting and analyzing the light that has passed through a given material, a miniaturized and appropriately shaped light beam—or flashlight—remains a challenge. Today that light beam is most often provided by macroscale equipment like a laser system that is not built into the chip itself as the sensors are.

    Complete sensor

    Enter the MIT work. In two recent papers in Nature Scientific Reports, researchers describe not only their approach for designing on-chip flashlights with a variety of beam characteristics, they also report building and successfully testing a prototype. Importantly, they created the device using existing fabrication technologies familiar to the microelectronics industry, so they are confident that the approach could be deployable at a mass scale with the lower cost that implies.

    Overall, this could enable industry to create a complete sensor on a chip with both light source and detector. As a result, the work represents a significant advance in the use of silicon photonics for the manipulation of light waves on microchips for sensor applications.

    “Silicon photonics has so much potential to improve and miniaturize the existing bench-scale biosensing schemes. We just need smarter design strategies to tap its full potential. This work shows one such approach,” says PhD candidate Robin Singh SM ’18, who is lead author of both papers.

    “This work is significant, and represents a new paradigm of photonic device design, enabling enhancements in the manipulation of optical beams,” says Dawn Tan, an associate professor at the Singapore University of Technology and Design who was not involved in the research.

    The senior coauthors on the first paper are Anuradha “Anu” Murthy Agarwal, a principal research scientist in MIT’s Materials Research Laboratory, Microphotonics Center, and Initiative for Knowledge and Innovation in Manufacturing; and Brian W. Anthony, a principal research scientist in MIT’s Department of Mechanical Engineering. Singh’s coauthors on the second paper are Agarwal; Anthony; Yuqi Nie, now at Princeton University; and Mingye Gao, a graduate student in MIT’s Department of Electrical Engineering and Computer Science.

    How they did it

    Singh and colleagues created their overall design using multiple computer modeling tools. These included conventional approaches based on the physics involved in the propagation and manipulation of light, and more cutting-edge machine-learning techniques in which the computer is taught to predict potential solutions using huge amounts of data. “If we show the computer many examples of nano flashlights, it can learn how to make better flashlights,” says Anthony. Ultimately, “we can then tell the computer the pattern of light that we want, and it will tell us what the design of the flashlight needs to be.”

    All of these modeling tools have advantages and disadvantages; together they resulted in a final, optimal design that can be adapted to create flashlights with different kinds of light beams.

    The researchers went on to use that design to create a specific flashlight with a collimated beam, or one in which the rays of light are perfectly parallel to each other. Collimated beams are key to some types of sensors. The overall flashlight that the researchers made involved some 500 rectangular nanoscale structures of different dimensions that the team’s modeling predicted would enable a collimated beam. Nanostructures of different dimensions would lead to different kinds of beams that in turn are key to other applications.

    The tiny flashlight with a collimated beam worked. Not only that, it provided a beam that was five times more powerful than is possible with conventional structures. That’s partly because “being able to control the light better means that less is scattered and lost,” says Agarwal.

    Singh describes the excitement he felt upon creating that first flashlight. “It was great to see through a microscope what I had designed on a computer. Then we tested it, and it worked!”

    This research was supported, in part, by the MIT Skoltech Initiative.

    Additional MIT facilities and departments that made this work possible are the Department of Materials Science and Engineering, the Materials Research Laboratory, the Institute for Medical Engineering and Science, and MIT.nano. More

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    Climate solutions depend on technology, policy, and businesses working together

    “The challenge for humanity now is how to decarbonize the global economy by 2050. To do that, we need a supercharged decade of energy innovation,” said Ernest J. Moniz, the Cecil and Ida Green Professor of Physics and Engineering Systems Emeritus, founding director of the MIT Energy Initiative, and a former U.S. secretary of energy, as he opened the MIT Forefront virtual event on April 21. “But we also need practical visionaries, in every economic sector, to develop new business models that allow them to remain profitable while achieving the zero-carbon emissions.”

    The event, “Addressing Climate and Sustainability through Technology, Policy, and Business Models,” was the third in the MIT Forefront series, which invites top minds from the worlds of science, industry, and policy to propose bold new answers to urgent global problems. Moniz moderated the event, and more than 12,000 people tuned in online.

    MIT and other universities play an important role in preparing the world’s best minds to take on big climate challenges and develop the technology needed to advance sustainability efforts, a point illustrated in the main session with a video about Via Separations, a company supported by MIT’s The Engine. Co-founded by Shreya Dave ’09, SM ’12, PhD ’16, Via Separations customizes filtration technology to reduce waste and save money across multiple industries. “By next year, we are going to be eliminating carbon dioxide emissions from our customers’ facilities,” Dave said.

    Via Separations is one of many innovative companies born of MIT’s energy and climate initiatives — the work of which, as the panel went on to discuss, is critical to achieving net-zero emissions and deploying successful environmental sustainability efforts. As Moniz put it, the company embodies “the spirit of science and technology in action for the good of humankind” and exemplifies how universities and businesses, as well as technology and policy, must work together to make the best environmental choices.

    How businesses confront climate change

    Innovation in sustainable practices can be met with substantial challenges when proposed or applied to business models, particularly on the policy side, the panelists noted. But they shared some key ways that their respective organizations have employed current technologies and the challenges they face in reaching their sustainability goals. Despite each business’s different products and services, a common thread of needing new technologies to achieve their sustainability goals emerged. 

    Although 2050 is the long-term goal for net-zero emissions put forth by the Paris Agreement, the businesses represented by the panelists are thinking about the shorter term. “IBM has committed to net-zero emissions by 2030 ― without carbon offsets,” said Arvind Krishna, chairman and chief executive officer of IBM. “We believe that some carbon taxes would be a good policy tool. But policy alone is insufficient. We need advanced technological tools to reach our goal.” 

    Jeff Wilke SM ’93, who retired as Amazon’s chief executive officer of Worldwide Consumer in February 2021, outlined a number of initiatives that the online retail giant is undertaking to curb emissions. Transportation is one of their biggest hurdles to reaching zero emissions, leading to a significant investment in Class 8 electric trucks. “Another objective is to remove the need for plane shipments by getting inventory closer to urban areas, and that has been happening steadily over the years,” he said.

    Jim Fitterling, chair and chief executive officer of Dow, explained that Dow has reduced its carbon emissions by 15 percent in the past decade and is poised to reduce it further in the next. Future goals include working toward electrifying ethylene production. “If we can electrify that, it will allow us to make major strides toward carbon-dioxide reduction,” he said. “But we need more reliable and stable power to get to that point.” 

    Collaboration is key to advancing climate solutions

    Maria T. Zuber, MIT’s vice president for research, who was recently appointed by U.S. President Joe Biden as co-chair of the President’s Council of Advisors on Science and Technology, stressed that MIT innovators and industry leaders must work together to implement climate solutions. 

    “Innovation is a team sport,” said Zuber, who is also the E. A. Griswold Professor of Geophysics. “Even if MIT researchers make a huge discovery, deploying it requires cooperation on a policy level and often industry support. Policymakers need to solve problems and seize opportunities in ways that are popular. It’s not just solving technical problems ― there is a human behavior component.”

    But businesses, Zuber said, can play a huge role in advancing innovation. “If a company becomes convinced of the potential of a new technology, they can be the best advocates with policymakers,” she said.

    The question of “sustainability vs. shareholders” 

    During the Q&A session, an audience member pointed out that environmentalists are often distrustful of companies’ sustainability policies when their focus is on shareholders and profit.

    “Companies have to show that they’re part of the solution,” Fitterling said. “Investors will be afraid of high costs up front, so, say, completely electrifying a plant overnight is off the table. You have to make a plan to get there, and then incentivize that plan through policy. Carbon taxes are one way, but miss the market leverage.”

    Krishna also pushed back on the idea that companies have to choose between sustainability and profit. “It’s a false dichotomy,” he said. “If companies were only interested in short-term profits, they wouldn’t last for long.”

    “A belief I’ve heard from some environmental groups is that ‘anything a company does is greenwashing,’ and that they’ll abandon those efforts if the economy tanks,” Zuber said, referring to a practice wherein organizations spend more time marketing themselves as environmentally sustainable than on maximizing their sustainability efforts. “The economy tanked in 2020, though, and we saw companies double down on their sustainability plans. They see that it’s good for business.”

    The role of universities and businesses in sustainability innovation

    “Amazon and all corporations are adapting to the effects of climate change, like extreme weather patterns, and will need to adapt more — but I’m not ready to throw in the towel for decarbonization,” Wilke said. “Either way, companies will have to invest in decarbonization. There is no way we are going to make the progress we have to make without it.” 

    Another component is the implications of artificial intelligence (AI) and quantum computing. Krishna noted multiple ways that AI and quantum computing will play a role at IBM, including finding the most environmentally sustainable and cost-efficient ways to advance carbon separation in exhaust gases and lithium battery life in electric cars. 

    AI, quantum computing, and alternate energy sources such as fusion energy that have the potential to achieve net-zero energy, are key areas that students, researchers, and faculty members are pursuing at MIT.

    “Universities like MIT need to go as fast as we can as far as we can with the science and technology we have now,” Zuber said. “In parallel, we need to invest in and deploy a suite of new tools in science and technology breakthroughs that we need to reach the 2050 goal of decarbonizing. Finally, we need to continue to train the next generation of students and researchers who are solving these issues and deploy them to these companies to figure it out.” More

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    With a zap of light, system switches objects’ colors and patterns

    When was the last time you repainted your car? Redesigned your coffee mug collection? Gave your shoes a colorful facelift?

    You likely answered: never, never, and never. You might consider these arduous tasks not worth the effort. But a new color-shifting “programmable matter” system could change that with a zap of light.

    MIT researchers have developed a way to rapidly update imagery on object surfaces. The system, dubbed “ChromoUpdate” pairs an ultraviolet (UV) light projector with items coated in light-activated dye. The projected light alters the reflective properties of the dye, creating colorful new images in just a few minutes. The advance could accelerate product development, enabling product designers to churn through prototypes without getting bogged down with painting or printing.

    An ultraviolet (UV) light projector is used on a cell-phone case coated in light-activated dye. The projected light alters the reflective properties of the dye, creating images in just a few minutes.

    ChromoUpdate “takes advantage of fast programming cycles — things that wouldn’t have been possible before,” says Michael Wessley, the study’s lead author and a postdoc in MIT’s Computer Science and Artificial Intelligence Laboratory.

    The research will be presented at the ACM Conference on Human Factors in Computing Systems this month. Wessely’s co-authors include his advisor, Professor Stefanie Mueller, as well as postdoc Yuhua Jin, recent graduate Cattalyya Nuengsigkapian ’19, MNG ’20, visiting master’s student Aleksei Kashapov, postdoc Isabel Qamar, and Professor Dzmitry Tsetserukou of the Skolkovo Institute of Science and Technology.

    ChromoUpdate builds on the researchers’ previous programmable matter system, called PhotoChromeleon. That method was “the first to show that we can have high-resolution, multicolor textures that we can just reprogram over and over again,” says Wessely. PhotoChromeleon used a lacquer-like ink comprising cyan, magenta, and yellow dyes. The user covered an object with a layer of the ink, which could then be reprogrammed using light. First, UV light from an LED was shone on the ink, fully saturating the dyes. Next, the dyes were selectively desaturated with a visible light projector, bringing each pixel to its desired color and leaving behind the final image. PhotoChromeleon was innovative, but it was sluggish. It took about 20 minutes to update an image. “We can accelerate the process,” says Wessely.

    They achieved that with ChromoUpdate, by fine-tuning the UV saturation process. Rather than using an LED, which uniformly blasts the entire surface, ChromoUpdate uses a UV projector that can vary light levels across the surface. So, the operator has pixel-level control over saturation levels. “We can saturate the material locally in the exact pattern we want,” says Wessely. That saves time — someone designing a car’s exterior might simply want to add racing stripes to an otherwise completed design. ChromoUpdate lets them do just that, without erasing and reprojecting the entire exterior.

    This selective saturation procedure allows designers to create a black-and-white preview of a design in seconds, or a full-color prototype in minutes. That means they could try out dozens of designs in a single work session, a previously unattainable feat. “You can actually have a physical prototype to see if your design really works,” says Wessely. “You can see how it looks when sunlight shines on it or when shadows are cast. It’s not enough just to do this on a computer.”

    Play video

    That speed also means ChromoUpdate could be used for providing real-time notifications without relying on screens. “One example is your coffee mug,” says Wessely. “You put your mug in our projector system and program it to show your daily schedule. And it updates itself directly when a new meeting comes in for that day, or it shows you the weather forecast.”

    Wessely hopes to keep improving the technology. At present, the light-activated ink is specialized for smooth, rigid surfaces like mugs, phone cases, or cars. But the researchers are working toward flexible, programmable textiles. “We’re looking at methods to dye fabrics and potentially use light-emitting fibers,” says Wessely. “So, we could have clothing — t-shirts and shoes and all that stuff — that can reprogram itself.”

    The researchers have partnered with a group of textile makers in Paris to see how ChomoUpdate can be incorporated into the design process.

    This research was funded, in part, by Ford. More

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    Undergraduates explore practical applications of artificial intelligence

    Deep neural networks excel at finding patterns in datasets too vast for the human brain to pick apart. That ability has made deep learning indispensable to just about anyone who deals with data. This year, the MIT Quest for Intelligence and the MIT-IBM Watson AI Lab sponsored 17 undergraduates to work with faculty on yearlong research projects through MIT’s Advanced Undergraduate Research Opportunities Program (SuperUROP).

    Students got to explore AI applications in climate science, finance, cybersecurity, and natural language processing, among other fields. And faculty got to work with students from outside their departments, an experience they describe in glowing terms. “Adeline is a shining testament of the value of the UROP program,” says Raffaele Ferrari, a professor in MIT’s Department of Earth and Planetary Sciences, of his advisee. “Without UROP, an oceanography professor might have never had the opportunity to collaborate with a student in computer science.”

    Highlighted below are four SuperUROP projects from this past year.

    A faster algorithm to manage cloud-computing jobs

    The shift from desktop computing to far-flung data centers in the “cloud” has created bottlenecks for companies selling computing services. Faced with a constant flux of orders and cancellations, their profits depend heavily on efficiently pairing machines with customers.

    Approximation algorithms are used to carry out this feat of optimization. Among all the possible ways of assigning machines to customers by price and other criteria, they find a schedule that achieves near-optimal profit.​ For the last year, junior Spencer Compton worked on a virtual whiteboard with MIT Professor Ronitt Rubinfeld and postdoc Slobodan Mitrović to find a faster scheduling method.

    “We didn’t write any code,” he says. “We wrote proofs and used mathematical ideas to find a more efficient way to solve this optimization problem. The same ideas that improve cloud-computing scheduling can be used to assign flight crews to planes, among other tasks.”

    In a pre-print paper on arXiv, Compton and his co-authors show how to speed up an approximation algorithm under dynamic conditions. They also show how to locate machines assigned to individual customers without computing the entire schedule.

    A big challenge was finding the crux of the project, he says. “There’s a lot of literature out there, and a lot of people who have thought about related problems. It was fun to look at everything that’s been done and brainstorm to see where we could make an impact.”​

    How much heat and carbon can the oceans absorb?

    Earth’s oceans regulate climate by drawing down excess heat and carbon dioxide from the air. But as the oceans warm, it’s unclear if they will soak up as much carbon as they do now. A slowed uptake could bring about more warming than what today’s climate models predict. It’s one of the big questions facing climate modelers as they try to refine their predictions for the future.

    The biggest obstacle in their way is the complexity of the problem: today’s global climate models lack the computing power to get a high-resolution view of the dynamics influencing key variables like sea-surface temperatures. To compensate for the lost accuracy, researchers are building surrogate models to approximate the missing dynamics without explicitly solving for them.

    In a project with MIT Professor Raffaele Ferrari and research scientist Andre Souza, MIT junior Adeline Hillier is exploring how deep learning solutions can be used to improve or replace physical models of the uppermost layer of ocean, which drives the rate of heat and carbon uptake. “If the model has a small footprint and succeeds under many of the physical conditions encountered in the real world, it could be incorporated into a global climate model and hopefully improve climate projections,” she says.

    In the course of the project, Hillier learned how to code in the programming language Julia. She also got a crash course in fluid dynamics. “You’re trying to model the effects of turbulent dynamics in the ocean,” she says. “It helps to know what the processes and physics behind them look like.”

    In search of more efficient deep learning models

    There are thousands of ways to design a deep learning model to solve a given task. Automating the design process promises to narrow the options and make these tools more accessible. But finding the optimal architecture is anything but simple. Most automated searches pick the model that maximizes validation accuracy without considering the structure of the underlying data, which may suggest a simpler, more robust solution. As a result, more reliable or data-efficient architectures are passed over.

    “Instead of looking at the accuracy of the model alone, we should focus on the structure of the data,” says MIT senior Kristian Georgiev. In a project with MIT Professor Asu Ozdaglar and graduate student Alireza Fallah, Georgiev is looking at ways to automatically query the data to find the model that best suits its constraints. “If you choose your architecture based on the data, you’re more likely to get a good and robust solution from a learning theory perspective,” he says.

    The hardest part of the project was the exploratory phase at the start, he says. To find a good research question he read through papers ranging from topics in autoML to representation theory. But it was worth it, he says, to be able to work at the intersection of optimization and generalization. “To make good progress in machine learning you need to combine both of these fields.”

    What makes humans so good at recognizing faces?

    Face recognition comes easily to humans. Picking out familiar faces in a blurred or distorted photo is a cinch. But we don’t really understand why or how to replicate this superpower in machines. To home in on the principles important to recognizing faces, researchers have shown headshots to human subjects that are progressively degraded to see where recognition starts to break down. They are now performing similar experiments on computers to see if deeper insights can be gained

    In a project with MIT Professor Pawan Sinha and the MIT Quest for Intelligence, junior Ashika Verma applied a set of filters to a dataset of celebrity photos. She blurred their faces, distorted them, and changed their color to see if a face-recognition model could pick out photos of the same face. She found that the model did best when the photos were either natural color or grayscale, consistent with the human studies. Accuracy slipped when a color filter was added, but not as much as it did for the human subjects — a wrinkle that Verma plans to investigate further.

    The work is part of a broader effort to understand what makes humans so good at recognizing faces, and how machine vision might be improved as a result. It also ties in with Project Prakash, a nonprofit in India that treats blind children and tracks their recovery to learn more about the visual system and brain plasticity. “Running human experiments takes more time and resources than running computational experiments,” says Verma’s advisor, Kyle Keane, a researcher with MIT Quest. “We’re trying to make AI as human-like as possible so we can run a lot of computational experiments to identify the most promising experiments to run on humans.”

    Degrading the images to use in the experiments, and then running them through the deep nets, was a challenge, says Verma. “It’s very slow,” she says. “You work 20 minutes at a time and then you wait.” But working in a lab with an advisor made it worth it, she says. “It was fun to dip my toes into neuroscience.”

    SuperUROP projects were funded, in part, by the MIT-IBM Watson AI Lab, MIT Quest Corporate, and by Eric Schmidt, technical advisor to Alphabet Inc., and his wife, Wendy. More

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    Q&A: Vivienne Sze on crossing the hardware-software divide for efficient artificial intelligence

    Not so long ago, watching a movie on a smartphone seemed impossible. Vivienne Sze was a graduate student at MIT at the time, in the mid 2000s, and she was drawn to the challenge of compressing video to keep image quality high without draining the phone’s battery. The solution she hit upon called for co-designing energy-efficient circuits with energy-efficient algorithms.

    Sze would go on to be part of the team that won an Engineering Emmy Award for developing the video compression standards still in use today. Now an associate professor in MIT’s Department of Electrical Engineering and Computer Science, Sze has set her sights on a new milestone: bringing artificial intelligence applications to smartphones and tiny robots.

    Her research focuses on designing more-efficient deep neural networks to process video, and more-efficient hardware to run those applications. She recently co-published a book on the topic, and will teach a professional education course on how to design efficient deep learning systems in June.

    On April 29, Sze will join Assistant Professor Song Han for an MIT Quest AI Roundtable on the co-design of efficient hardware and software moderated by Aude Oliva, director of MIT Quest Corporate and the MIT director of the MIT-IBM Watson AI Lab. Here, Sze discusses her recent work.

    Q: Why do we need low-power AI now?

    A: AI applications are moving to smartphones, tiny robots, and internet-connected appliances and other devices with limited power and processing capabilities. The challenge is that AI has high computing requirements. Analyzing sensor and camera data from a self-driving car consumes about 2,500 watts, but the computing budget of a smartphone is just about a single watt. Closing this gap requires rethinking the entire stack, a trend that will define the next decade of AI.

    Q: What’s the big deal about running AI on a smartphone?

    A: It means that the data processing no longer has to take place in the “cloud,” on racks of warehouse servers. Untethering compute from the cloud allows us to broaden AI’s reach. It gives people in developing countries with limited communication infrastructure access to AI. It also speeds up response time by reducing the lag caused by communicating with distant servers. This is crucial for interactive applications like autonomous navigation and augmented reality, which need to respond instantaneously to changing conditions. Processing data on the device can also protect medical and other sensitive records. Data can be processed right where they’re collected.

    Q: What makes modern AI so inefficient?

    A: The cornerstone of modern AI — deep neural networks — can require hundreds of millions to billions of calculations — orders of magnitude greater than compressing video on a smartphone. But it’s not just number crunching that makes deep networks energy-intensive — it’s the cost of shuffling data to and from memory to perform these computations. The farther the data have to travel, and the more data there are, the greater the bottleneck.

    Q: How are you redesigning AI hardware for greater energy efficiency?

    A: We focus on reducing data movement and the amount of data needed for computation. In some deep networks, the same data are used multiple times for different computations. We design specialized hardware to reuse data locally rather than send them off-chip. Storing reused data on-chip makes the process extremely energy-efficient.  

    We also optimize the order in which data are processed to maximize their reuse. That’s the key property of the Eyeriss chip that I co-designed with Joel Emer. In our followup work, Eyeriss v2, we made the chip flexible enough to reuse data across a wider range of deep networks. The Eyeriss chip also uses compression to reduce data movement, a common tactic among AI chips. The low-power Navion chip that I co-designed with Sertac Karaman for mapping and navigation applications in robotics uses two to three orders of magnitude less energy than a CPU, in part by using optimizations that reduce the amount of data processed and stored on-chip. 

    Q: What changes have you made on the software side to boost efficiency?

    A: The more that software aligns with hardware-related performance metrics like energy efficiency, the better we can do. Pruning, for example, is a popular way to remove weights from a deep network to reduce computation costs. But rather than remove weights based on their magnitude, our work on energy-aware pruning suggests you can remove the more energy-intensive weights to improve overall energy consumption. Another method we’ve developed, NetAdapt, automates the process of adapting and optimizing a deep network for a smartphone or other hardware platforms. Our recent followup work, NetAdaptv2, accelerates the optimization process to further boost efficiency.

    Q: What low-power AI applications are you working on?

    A: I’m exploring autonomous navigation for low-energy robots with Sertac Karaman. I’m also working with Thomas Heldt to develop a low-cost and potentially more effective way of diagnosing and monitoring people with neurodegenerative disorders like Alzheimer’s and Parkinson’s by tracking their eye movements. Eye-movement properties like reaction time could potentially serve as biomarkers for brain function. In the past, eye-movement tracking took place in clinics because of the expensive equipment required. We’ve shown that an ordinary smartphone camera can take measurements from a patient’s home, making data collection easier and less costly. This could help to monitor disease progression and track improvements in clinical drug trials.

    Q: Where is low-power AI headed next?

    A: Reducing AI’s energy requirements will extend AI to a wider range of embedded devices, extending its reach into tiny robots, smart homes, and medical devices. A key challenge is that efficiency often requires a tradeoff in performance. For wide adoption, it will be important to dig deeper into these different applications to establish the right balance between efficiency and accuracy. More

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    Top collegiate inventors awarded 2021 Lemelson-MIT Student Prize

    Following a year that demonstrated the importance and practical applications of scientific advancement and invention, the Lemelson-MIT Program announced seven winners of its annual 2021 Lemelson-MIT Student Prize on April 26, World Intellectual Property Day. The program awarded a total of $90,000 to four graduate students and three undergraduate teams from across the country. The majority of winners have filed for patents, while others have been awarded full or provisional patents. Their inventions range from an innovative approach to plastic pollution in Uganda to self-driving wheelchair technology.

    “We are thrilled with and inspired by the quality of inventions this year,” says Michael J. Cima, faculty director of the Lemelson-MIT Program and associate dean of innovation at the MIT School of Engineering. “This group of students has performed tremendous work amidst difficult circumstances, often working remotely, knowing their research is too important to slow down. Science and technology have been at the forefront of conversation over the past year, and this diverse group of students is well-positioned to lead us toward great advances for years to come,” Cima says.

    Supported by The Lemelson Foundation and administered by the School of Engineering, the Lemelson-MIT Student Prize recognizes and provides catalyst funding to young inventors who have dedicated themselves to providing scalable solutions to real-world problems around the globe. This year’s winners have invented solutions that address pregnancy-related complications, market losses in the agricultural industry, obstacles impeding smooth patient recoveries, and other pressing problems in society. Recipients were selected from a diverse and highly competitive pool of hundreds of applicants from colleges and universities across the United States. 

    “Congratulations to this year’s winners for their remarkable achievements and dedication to solving some of the biggest challenges facing society today,” says Carol Dahl, executive director of the Lemelson Foundation. “It’s particularly exciting to see this year’s cohort of graduate winners is all women, given the fact that a large gender disparity exists in patenting. More inventors are needed from communities historically underrepresented in invention, including women, if we are going to effectively solve the challenges of today and tomorrow.”

    2021 Lemelson-MIT Student Prize winners were selected based on the overall inventiveness of their work, the invention’s potential for scalable commercialization or adoption, and youth mentorship experience. They are:

    The “Cure it!” Lemelson-MIT Student Prize: Rewarding technology-based inventions that involve health care.

    •    Nicole Black of Harvard University, $15,000 Graduate Winner The eardrum often becomes damaged through traumatic head injuries, blast injuries, chronic ear infections, and other incidents, affecting millions of people worldwide every year. Current eardrum graft materials are tissues taken from other parts of the body. These current grafts intend to repair damage, yet do not integrate well with the eardrum and surrounding tissue, resulting in poor healing and hearing outcomes that often require further surgery. Using novel biodegradable materials and 3D printing techniques, Black invented a tunable, biomimetic eardrum graft called PhonoGraft. Because PhonoGraft is able to retain the circular and radial structure of the eardrum, its sound-induced motion is similar to that of original eardrum tissue. Additionally, PhonoGraft acts as a kind of scaffolding that bridges the hole and becomes part of the native tissue, allowing the eardrum to essentially heal itself and restore hearing more effectively.

    •    Mira Moufarrej of Stanford University, $15,000 Graduate WinnerPregnancy-related complications like preeclampsia and preterm delivery pose significant risks to both fetal and maternal health and are often difficult to detect in time for effective medical intervention. Moufarrej developed three novel liquid biopsy tests that monitor prenatal health and identify high-risk pregnancies by more accurately predicting due date, risk of preeclampsia, and likelihood of preterm delivery, making assessments possible well in advance of the mother becoming symptomatic. Following preclinical validation, these affordable, simple, and reliable maternal blood tests may change the standard of care for preeclampsia and preterm delivery — risks that no other test can currently predict early enough to allow for meaningful clinical intervention.

    •    Innerva: Bruce Enzmann, Michael Lan, and Anson Zhou of Johns Hopkins University, $10,000 Undergraduate Team WinnerTargeted muscle reinnervation (TMR), a procedure to connect severed nerves to smaller motor nerves, is an increasingly popular method for treating peripheral nerve injuries, as it partially guides nerve regeneration and makes it possible for amputees to more effectively operate prosthetic devices. About 30 percent of TMR patients, however, experience pain due to nerve tumors, or neuromas, that result from the inherent differences in size between the newly connected nerves. Innerva’s invention is a nerve conduit that creates an interface between the different sized nerves connected during TMR, modulating nerve regeneration and preventing the formation of neuromas.

    The “Eat it!” Lemelson-MIT Student Prize: Rewarding technology-based inventions that involve food/water and agriculture.

    •    Hilary Johnson of MIT, $15,000 Graduate WinnerCentrifugal pumps are integral drivers in many fluid systems, such as clean water distribution, wastewater treatment, crop irrigation, oil and gas production, and pumped hydro energy storage. Requiring significant energy to operate, collectively these pumps consume 6 percent of annual U.S. electricity. Hilary’s invention is a variable volute pump, a new category of centrifugal pumps that mechanically adapts the hydraulic chamber to adjust to fluctuating system demand. Variable volute pumps show the potential to significantly improve efficiency and operating range across applications by adjusting the spiral fluid passages to match the flow rate.

    •    Grain Weevil: Benjamin Johnson and Zane Zents of the University of Nebraska at Omaha, $10,000 Undergraduate Team WinnerLarge grain bins are used to store surplus grain supplies and allow farmers to hold their yield for higher prices. Managing grain condition and extraction require farmers to physically enter the grain bin, which is difficult and dangerous, often trapping and even killing farmers. A lack of proper management and extraction systems cause a 30 percent loss in cereal grain value worldwide. The Grain Weevil is a grain extraction and bin management robot that scurries across the top of the grain within a bin, smoothing out clumps so that the grain can be properly aerated and easily extracted from the bin. This device helps farmers safely and efficiently manage the extraction of grain from the bin, as well as maintain grain quality while in storage.

    The “Move it!” Lemelson-MIT Student Prize: Rewarding technology-based inventions that involve transportation and mobility.

    •    Adventus Robotics: Maya Burhanpurkar and Seung Hwan An of Harvard University, $10,000 Undergraduate Team WinnerPower wheelchairs present formidable barriers to mobility for users unable to operate a joystick, and manual wheelchairs operated by porters within hospitals can increase the potential for disease transmission between patients and staff. To solve these issues, the Adventus team developed a hardware and software kit that can be retrofitted to power wheelchairs already on the market to convert them into Level 5 (fully autonomous) self-driving wheelchairs. Adventus’ system transcends existing assistive technologies by using artificial intelligence and fail-safe sensors for edge detection and collision prevention. In light of Covid-19, the team’s technology has the potential to be used in a variety of other applications like autonomous floor cleaning and disinfecting.

    The “Use it!” Lemelson-MIT Student Prize: Rewarding technology-based inventions that involve consumer devices and products.

    •    Paige Balcom of the University of California at Berkeley, $15,000 Graduate WinnerTakataka Plastics is a technology and systems-level solution for plastic waste in Uganda that locally recycles plastic waste and creates jobs for vulnerable youth. Paige developed small-scale, locally built, low-cost machines to transform plastic waste into saleable products such as wall tiles for buildings, personal protective equipment, and consumer goods. This technology is especially innovative for PET waste because PET plastic (water and soda bottles) currently cannot be recycled anywhere in Uganda, and exporting the waste is difficult and inaccessible to most local recyclers.

    Collegiate inventors interested in applying for the 2022 Lemelson-MIT Student Prize can find more information via the Lemelson-MIT Program. The 2022 Student Prize application will open in late spring 2021. More

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    Navigating beneath the Arctic ice

    There is a lot of activity beneath the vast, lonely expanses of ice and snow in the Arctic. Climate change has dramatically altered the layer of ice that covers much of the Arctic Ocean. Areas of water that used to be covered by a solid ice pack are now covered by thin layers only 3 feet deep. Beneath the ice, a warm layer of water, part of the Beaufort Lens, has changed the makeup of the aquatic environment.    

    For scientists to understand the role this changing environment in the Arctic Ocean plays in global climate change, there is a need for mapping the ocean below the ice cover.

    A team of MIT engineers and naval officers led by Henrik Schmidt, professor of mechanical and ocean engineering, is trying to understand environmental changes, their impact on acoustic transmission beneath the surface, and how these changes affect navigation and communication for vehicles traveling below the ice.

    “Basically, what we want to understand is how does this new Arctic environment brought about by global climate change affect the use of underwater sound for communication, navigation, and sensing?” explains Schmidt.

    To answer this question, Schmidt traveled to the Arctic with members of the Laboratory for Autonomous Marine Sensing Systems (LAMSS) including Daniel Goodwin and Bradli Howard, graduate students in the MIT-Woods Hole Oceanographic Institution Joint Program in oceanographic engineering.

    With funding from the Office of Naval Research, the team participated in ICEX — or Ice Exercise — 2020, a three-week program hosted by the U.S. Navy, where military personnel, scientists, and engineers work side-by-side executing a variety of research projects and missions.

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    Understanding the Arctic | MIT MechE

    A strategic waterway

    The rapidly changing environment in the Arctic has wide-ranging impacts. In addition to giving researchers more information about the impact of global warming and the effects it has on marine mammals, the thinning ice could potentially open up new shipping lanes and trade routes in areas that were previously untraversable.

    Perhaps most crucially for the U.S. Navy, understanding the altered environment also has geopolitical importance.

    “If the Arctic environment is changing and we don’t understand it, that could have implications in terms of national security,” says Goodwin.

    Several years ago, Schmidt and his colleague Arthur Baggeroer, professor of mechanical and ocean engineering, were among the first to recognize that the warmer waters, part of the Beaufort Lens, coupled with the changing ice composition, impacted how sound traveled in the water.

    To successfully navigate throughout the Arctic, the U.S. Navy and other entities in the region need to understand how these changes in sound propagation affect a vehicle’s ability to communicate and navigate through the water.

    Using an unpiloted, autonomous underwater vehicle (AUV) built by General Dynamics-Mission Systems (GD-MS), and a system of sensors rigged on buoys developed by the Woods Hole Oceanographic Institution, Schmidt and his team, joined by Dan McDonald and Josiah DeLange of GD-MS, set out to demonstrate a new integrated acoustic communication and navigation concept.

    The framework, which was also supported and developed by LAMSS members Supun Randeni, EeShan Bhatt, Rui Chen, and Oscar Viquez, as well as LAMSS alumnus Toby Schneider of GobySoft LLC, would allow vehicles to travel through the water with GPS-level accuracy while employing oceanographic sensors for data collection.

    “In order to prove that you can use this navigational concept in the Arctic, we have to first ensure we fully understand the environment that we’re operating in,” adds Goodwin.

    Understanding the environment belowAfter arriving at the Arctic Submarine Lab’s ice camp last spring, the research team deployed a number of conductivity-temperature-depth probes to gather data about the aquatic environment in the Arctic.

    “By using temperature and salinity as a function of depth, we calculate the sound speed profile. This helps us understand if the AUV’s location is good for communication or bad,” says Howard, who was responsible for monitoring environmental changes to the water column throughout ICEX.

    Because of the way sound bends in water, through a concept known as Snell’s Law, sine-like pressure waves collect in some parts of the water column and disperse in others. Understanding the propagation trajectories is key to predicting good and bad locations for the AUV to operate.  

    To map the areas of the water with optimal acoustic properties, Howard modified the traditional signal-to-noise-ratio (SNR) by using a metric known as the multi-path penalty (MPP), which penalizes areas where the AUV receives echoes of the messages. As a result, the vehicle prioritizes operations in areas with less reverb.

    These data allowed the team to identify exactly where the vehicle should be positioned in the water column for optimal communications which results in accurate navigation.

    While Howard gathered data on how the characteristics of the water impact acoustics, Goodwin focused on how sound is projected and reflected off the ever-changing ice on the surface.

    To get these data, the AUV was outfitted with a device that measured the motion of the vehicle relative to the ice above. That sound was picked up by several receivers attached to moorings hanging from the ice.

    The data from the vehicle and the receivers were then used by the researchers to compute exactly where the vehicle was at a given time. This location information, together with the data Howard gathered on the acoustic environment in the water, offer a new navigational concept for vehicles traveling in the Arctic Sea.

    Protecting the Arctic

    After a series of setbacks and challenges due to the unforgiving conditions in the Arctic, the team was able to successfully prove their navigational concept worked. Thanks to the team’s efforts, naval operations and future trade vessels may be able to take advantage of the changing conditions in the Arctic to maximize navigational accuracy and improve underwater communications.

    “Our work could improve the ability for the U.S. Navy to safely and effectively operate submarines under the ice for extended periods,” Howard says.

    Howard acknowledges that in addition to the changes in physical climate, the geopolitical climate continues to change. This only strengthens the need for improved navigation in the Arctic.

    “The U.S. Navy’s goal is to preserve peace and protect global trade by ensuring freedom of navigation throughout the world’s oceans,” she adds. “The navigational concept we proved during ICEX will serve to help the Navy in that mission.” More