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      Educating national security leaders on artificial intelligence

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      Understanding artificial intelligence and how it relates to matters of national security has become a top priority for military and government leaders in recent years. A new three-day custom program entitled “Artificial Intelligence for National Security Leaders” — AI4NSL for short — aims to educate leaders who may not have a technical background on the basics of AI, machine learning, and data science, and how these topics intersect with national security.

      “National security fundamentally is about two things: getting information out of sensors and processing that information. These are two things that AI excels at. The AI4NSL class engages national security leaders in understanding how to navigate the benefits and opportunities that AI affords, while also understanding its potential negative consequences,” says Aleksander Madry, the Cadence Design Systems Professor at MIT and one of the course’s faculty directors.

      Organized jointly by MIT’s School of Engineering, MIT Stephen A. Schwarzman College of Computing, and MIT Sloan Executive Education, AI4NSL wrapped up its fifth cohort in April. The course brings leaders from every branch of the U.S. military, as well as some foreign military leaders from NATO, to MIT’s campus, where they learn from faculty experts on a variety of technical topics in AI, as well as how to navigate organizational challenges that arise in this context.

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      AI for National Security Leaders | MIT Sloan Executive Education

      “We set out to put together a real executive education class on AI for senior national security leaders,” says Madry. “For three days, we are teaching these leaders not only an understanding of what this technology is about, but also how to best adopt these technologies organizationally.”

      The original idea sprang from discussions with senior U.S. Air Force (USAF) leaders and members of the Department of the Air Force (DAF)-MIT AI Accelerator in 2019.

      According to Major John Radovan, deputy director of the DAF-MIT AI Accelerator, in recent years it has become clear that national security leaders needed a deeper understanding of AI technologies and its implications on security, warfare, and military operations. In February 2020, Radovan and his team at the DAF-MIT AI Accelerator started building a custom course to help guide senior leaders in their discussions about AI.

      “This is the only course out there that is focused on AI specifically for national security,” says Radovan. “We didn’t want to make this course just for members of the Air Force — it had to be for all branches of the military. If we are going to operate as a joint force, we need to have the same vocabulary and the same mental models about how to use this technology.”

      After a pilot program in collaboration with MIT Open Learning and the MIT Computer Science and Artificial Intelligence Laboratory, Radovan connected with faculty at the School of Engineering and MIT Schwarzman College of Computing, including Madry, to refine the course’s curriculum. They enlisted the help of colleagues and faculty at MIT Sloan Executive Education to refine the class’s curriculum and cater the content to its audience. The result of this cross-school collaboration was a new iteration of AI4NSL, which was launched last summer.

      In addition to providing participants with a basic overview of AI technologies, the course places a heavy emphasis on organizational planning and implementation.

      “What we wanted to do was to create smart consumers at the command level. The idea was to present this content at a higher level so that people could understand the key frameworks, which will guide their thinking around the use and adoption of this material,” says Roberto Fernandez, the William F. Pounds Professor of Management and one of the AI4NSL instructors, as well as the other course’s faculty director.

      During the three-day course, instructors from MIT’s Department of Electrical Engineering and Computer Science, Department of Aeronautics and Astronautics, and MIT Sloan School of Management cover a wide range of topics.

      The first half of the course starts with a basic overview of concepts including AI, machine learning, deep learning, and the role of data. Instructors also present the problems and pitfalls of using AI technologies, including the potential for adversarial manipulation of machine learning systems, privacy challenges, and ethical considerations.

      In the middle of day two, the course shifts to examine the organizational perspective, encouraging participants to consider how to effectively implement these technologies in their own units.

      “What’s exciting about this course is the way it is formatted first in terms of understanding AI, machine learning, what data is, and how data feeds AI, and then giving participants a framework to go back to their units and build a strategy to make this work,” says Colonel Michelle Goyette, director of the Army Strategic Education Program at the Army War College and an AI4NSL participant.

      Throughout the course, breakout sessions provide participants with an opportunity to collaborate and problem-solve on an exercise together. These breakout sessions build upon one another as the participants are exposed to new concepts related to AI.

      “The breakout sessions have been distinctive because they force you to establish relationships with people you don’t know, so the networking aspect is key. Any time you can do more than receive information and actually get into the application of what you were taught, that really enhances the learning environment,” says Lieutenant General Brian Robinson, the commander of Air Education and Training Command for the USAF and an AI4NSL participant.

      This spirit of teamwork, collaboration, and bringing together individuals from different backgrounds permeates the three-day program. The AI4NSL classroom not only brings together national security leaders from all branches of the military, it also brings together faculty from three schools across MIT.

      “One of the things that’s most exciting about this program is the kind of overarching theme of collaboration,” says Rob Dietel, director of executive programs at Sloan School of Management. “We’re not drawing just from the MIT Sloan faculty, we’re bringing in top faculty from the Schwarzman College of Computing and the School of Engineering. It’s wonderful to be able to tap into those resources that are here on MIT’s campus to really make it the most impactful program that we can.”

      As new developments in generative AI, such as ChatGPT, and machine learning alter the national security landscape, the organizers at AI4NSL will continue to update the curriculum to ensure it is preparing leaders to understand the implications for their respective units.

      “The rate of change for AI and national security is so fast right now that it’s challenging to keep up, and that’s part of the reason we’ve designed this program. We’ve brought in some of our world-class faculty from different parts of MIT to really address the changing dynamic of AI,” adds Dietel. More

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      Building a playbook for elite-level sports

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      “All I did was swim,” says Jerry Lu, recalling his teenage years as a competitive swimmer. “From age 12 to 19, it was close to 30 hours a week of training.” Although Lu no longer competes himself, his understanding of the dedication and impeccable technique required in elite sports continues to shape his path as a master’s student at the MIT Sloan School of Management.

      As an undergraduate at the University of Virginia, Lu majored in systems and information engineering and economics. He had stopped swimming competitively, but he stayed connected to the sport as a technical performance consultant for the university’s nationally ranked swim team. Under his advisor, Ken Ono, Lu built a methodology of analyzing data from sensors worn by swimmers to improve their individual performance. By looking at an athlete’s propulsion and drag data over the course of a race, Lu can advise them on where they can shave off tenths of a second simply by adjusting their stroke to be more efficient.

      That experience inspired Lu to pursue a career in other aspects of sports. At MIT he’s pursuing a master’s in finance to build the analytical skills necessary to enable the sustainability of sports that don’t already enjoy the major commercial success of, say, football or basketball. It’s especially a challenge for Olympic sports, such as swimming, which struggle for commercial ventures outside of Olympic years.

      “My work in swimming is focused on athlete performance to win, but the definition of winning is different for a sport as a whole, and for an organization,” Lu says. “Not only do you need to win medals, a big part of it is how you allocate money because you also need to grow your sport.”

      At MIT, Lu is building a playbook for high-performance sports from both an athletic and financial perspective. He’s been gaining exposure to additional elite sports by working with MIT’s Sports Lab under Professor Anette “Peko” Hosoi. His work there isn’t a requirement for his master’s program, but Lu appreciates that the program’s flexibility allows him time to pursue research that interests him, alongside the required curriculum.

      “I’m quite lucky to be here in the sense that MIT is known to train great people in engineering,  science, or business, but also people with unique passions,” says Lu. “People that love football drafting, people that love to understand how you throw a curveball — they use their knowledge in very unexpected ways, and that’s when innovation happens.”

      Lu’s research with the Sports Lab focuses on optimizing strategies for aesthetic sports, such as figure skating or snowboarding, which are judged very differently than swimming is. Instead of figuring out how to move faster, athletes are interested in structuring routines that net them the most points from a panel of judges. Modelling techniques can be helpful for figuring out how to put together routines to maximize an athlete’s abilities, and also to predict how a judge might assign points based on how or when a skill is demonstrated. Optimizing both athletic performance and judge psychology is a challenge, it’s this type of innovation that excites him. He hopes more sporting organizations will adopt similar data-driven strategies in the future.

      When asked where he’d like to end up after finishing his degree, “The sport industry is the natural choice,” Lu says. Though he is certain his career will lead to sports eventually, he is still open to exploring new paths. This summer he will be a trading intern at Citadel Securities to apply the concepts learned in his degree program courses. He’s also picked up sailing since coming to MIT, already reaching the highest amateur rating in under a year. Lu consistently strives for excellence, whether in himself or for those he works with.

      Since graduating from UVA, Lu has continued to work with swimmers, including national champions and Olympic medalists, as a technical performance consultant. He’s also branched out into another Olympic sport, triathlon. Lu describes it as a side gig, but he’s deeply invested in the athletes he works with, even taking trips to the Olympic Training Center to collect data and help them build strategies for improvement.

      “The most fun part is actually interacting with the athletes and engaging and understanding how they think,” says Lu. “It’s easier for me to do so than others, because if you’ve never swam before and you’ve never trained as an elite athlete before, it’s hard to understand what exactly you can and cannot do and how to communicate these things to a coach or an athlete.” More

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      Bringing the social and ethical responsibilities of computing to the forefront

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      There has been a remarkable surge in the use of algorithms and artificial intelligence to address a wide range of problems and challenges. While their adoption, particularly with the rise of AI, is reshaping nearly every industry sector, discipline, and area of research, such innovations often expose unexpected consequences that involve new norms, new expectations, and new rules and laws.

      To facilitate deeper understanding, the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative in the MIT Schwarzman College of Computing, recently brought together social scientists and humanists with computer scientists, engineers, and other computing faculty for an exploration of the ways in which the broad applicability of algorithms and AI has presented both opportunities and challenges in many aspects of society.

      “The very nature of our reality is changing. AI has the ability to do things that until recently were solely the realm of human intelligence — things that can challenge our understanding of what it means to be human,” remarked Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing, in his opening address at the inaugural SERC Symposium. “This poses philosophical, conceptual, and practical questions on a scale not experienced since the start of the Enlightenment. In the face of such profound change, we need new conceptual maps for navigating the change.”

      The symposium offered a glimpse into the vision and activities of SERC in both research and education. “We believe our responsibility with SERC is to educate and equip our students and enable our faculty to contribute to responsible technology development and deployment,” said Georgia Perakis, the William F. Pounds Professor of Management in the MIT Sloan School of Management, co-associate dean of SERC, and the lead organizer of the symposium. “We’re drawing from the many strengths and diversity of disciplines across MIT and beyond and bringing them together to gain multiple viewpoints.”

      Through a succession of panels and sessions, the symposium delved into a variety of topics related to the societal and ethical dimensions of computing. In addition, 37 undergraduate and graduate students from a range of majors, including urban studies and planning, political science, mathematics, biology, electrical engineering and computer science, and brain and cognitive sciences, participated in a poster session to exhibit their research in this space, covering such topics as quantum ethics, AI collusion in storage markets, computing waste, and empowering users on social platforms for better content credibility.

      Showcasing a diversity of work

      In three sessions devoted to themes of beneficent and fair computing, equitable and personalized health, and algorithms and humans, the SERC Symposium showcased work by 12 faculty members across these domains.

      One such project from a multidisciplinary team of archaeologists, architects, digital artists, and computational social scientists aimed to preserve endangered heritage sites in Afghanistan with digital twins. The project team produced highly detailed interrogable 3D models of the heritage sites, in addition to extended reality and virtual reality experiences, as learning resources for audiences that cannot access these sites.

      In a project for the United Network for Organ Sharing, researchers showed how they used applied analytics to optimize various facets of an organ allocation system in the United States that is currently undergoing a major overhaul in order to make it more efficient, equitable, and inclusive for different racial, age, and gender groups, among others.

      Another talk discussed an area that has not yet received adequate public attention: the broader implications for equity that biased sensor data holds for the next generation of models in computing and health care.

      A talk on bias in algorithms considered both human bias and algorithmic bias, and the potential for improving results by taking into account differences in the nature of the two kinds of bias.

      Other highlighted research included the interaction between online platforms and human psychology; a study on whether decision-makers make systemic prediction mistakes on the available information; and an illustration of how advanced analytics and computation can be leveraged to inform supply chain management, operations, and regulatory work in the food and pharmaceutical industries.

      Improving the algorithms of tomorrow

      “Algorithms are, without question, impacting every aspect of our lives,” said Asu Ozdaglar, deputy dean of academics for the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science, in kicking off a panel she moderated on the implications of data and algorithms.

      “Whether it’s in the context of social media, online commerce, automated tasks, and now a much wider range of creative interactions with the advent of generative AI tools and large language models, there’s little doubt that much more is to come,” Ozdaglar said. “While the promise is evident to all of us, there’s a lot to be concerned as well. This is very much time for imaginative thinking and careful deliberation to improve the algorithms of tomorrow.”

      Turning to the panel, Ozdaglar asked experts from computing, social science, and data science for insights on how to understand what is to come and shape it to enrich outcomes for the majority of humanity.

      Sarah Williams, associate professor of technology and urban planning at MIT, emphasized the critical importance of comprehending the process of how datasets are assembled, as data are the foundation for all models. She also stressed the need for research to address the potential implication of biases in algorithms that often find their way in through their creators and the data used in their development. “It’s up to us to think about our own ethical solutions to these problems,” she said. “Just as it’s important to progress with the technology, we need to start the field of looking at these questions of what biases are in the algorithms? What biases are in the data, or in that data’s journey?”

      Shifting focus to generative models and whether the development and use of these technologies should be regulated, the panelists — which also included MIT’s Srini Devadas, professor of electrical engineering and computer science, John Horton, professor of information technology, and Simon Johnson, professor of entrepreneurship — all concurred that regulating open-source algorithms, which are publicly accessible, would be difficult given that regulators are still catching up and struggling to even set guardrails for technology that is now 20 years old.

      Returning to the question of how to effectively regulate the use of these technologies, Johnson proposed a progressive corporate tax system as a potential solution. He recommends basing companies’ tax payments on their profits, especially for large corporations whose massive earnings go largely untaxed due to offshore banking. By doing so, Johnson said that this approach can serve as a regulatory mechanism that discourages companies from trying to “own the entire world” by imposing disincentives.

      The role of ethics in computing education

      As computing continues to advance with no signs of slowing down, it is critical to educate students to be intentional in the social impact of the technologies they will be developing and deploying into the world. But can one actually be taught such things? If so, how?

      Caspar Hare, professor of philosophy at MIT and co-associate dean of SERC, posed this looming question to faculty on a panel he moderated on the role of ethics in computing education. All experienced in teaching ethics and thinking about the social implications of computing, each panelist shared their perspective and approach.

      A strong advocate for the importance of learning from history, Eden Medina, associate professor of science, technology, and society at MIT, said that “often the way we frame computing is that everything is new. One of the things that I do in my teaching is look at how people have confronted these issues in the past and try to draw from them as a way to think about possible ways forward.” Medina regularly uses case studies in her classes and referred to a paper written by Yale University science historian Joanna Radin on the Pima Indian Diabetes Dataset that raised ethical issues on the history of that particular collection of data that many don’t consider as an example of how decisions around technology and data can grow out of very specific contexts.

      Milo Phillips-Brown, associate professor of philosophy at Oxford University, talked about the Ethical Computing Protocol that he co-created while he was a SERC postdoc at MIT. The protocol, a four-step approach to building technology responsibly, is designed to train computer science students to think in a better and more accurate way about the social implications of technology by breaking the process down into more manageable steps. “The basic approach that we take very much draws on the fields of value-sensitive design, responsible research and innovation, participatory design as guiding insights, and then is also fundamentally interdisciplinary,” he said.

      Fields such as biomedicine and law have an ethics ecosystem that distributes the function of ethical reasoning in these areas. Oversight and regulation are provided to guide front-line stakeholders and decision-makers when issues arise, as are training programs and access to interdisciplinary expertise that they can draw from. “In this space, we have none of that,” said John Basl, associate professor of philosophy at Northeastern University. “For current generations of computer scientists and other decision-makers, we’re actually making them do the ethical reasoning on their own.” Basl commented further that teaching core ethical reasoning skills across the curriculum, not just in philosophy classes, is essential, and that the goal shouldn’t be for every computer scientist be a professional ethicist, but for them to know enough of the landscape to be able to ask the right questions and seek out the relevant expertise and resources that exists.

      After the final session, interdisciplinary groups of faculty, students, and researchers engaged in animated discussions related to the issues covered throughout the day during a reception that marked the conclusion of the symposium. More

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      Joining the battle against health care bias

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      Medical researchers are awash in a tsunami of clinical data. But we need major changes in how we gather, share, and apply this data to bring its benefits to all, says Leo Anthony Celi, principal research scientist at the MIT Laboratory for Computational Physiology (LCP). 

      One key change is to make clinical data of all kinds openly available, with the proper privacy safeguards, says Celi, a practicing intensive care unit (ICU) physician at the Beth Israel Deaconess Medical Center (BIDMC) in Boston. Another key is to fully exploit these open data with multidisciplinary collaborations among clinicians, academic investigators, and industry. A third key is to focus on the varying needs of populations across every country, and to empower the experts there to drive advances in treatment, says Celi, who is also an associate professor at Harvard Medical School. 

      In all of this work, researchers must actively seek to overcome the perennial problem of bias in understanding and applying medical knowledge. This deeply damaging problem is only heightened with the massive onslaught of machine learning and other artificial intelligence technologies. “Computers will pick up all our unconscious, implicit biases when we make decisions,” Celi warns.

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      Sharing medical data 

      Founded by the LCP, the MIT Critical Data consortium builds communities across disciplines to leverage the data that are routinely collected in the process of ICU care to understand health and disease better. “We connect people and align incentives,” Celi says. “In order to advance, hospitals need to work with universities, who need to work with industry partners, who need access to clinicians and data.” 

      The consortium’s flagship project is the MIMIC (medical information marked for intensive care) ICU database built at BIDMC. With about 35,000 users around the world, the MIMIC cohort is the most widely analyzed in critical care medicine. 

      International collaborations such as MIMIC highlight one of the biggest obstacles in health care: most clinical research is performed in rich countries, typically with most clinical trial participants being white males. “The findings of these trials are translated into treatment recommendations for every patient around the world,” says Celi. “We think that this is a major contributor to the sub-optimal outcomes that we see in the treatment of all sorts of diseases in Africa, in Asia, in Latin America.” 

      To fix this problem, “groups who are disproportionately burdened by disease should be setting the research agenda,” Celi says. 

      That’s the rule in the “datathons” (health hackathons) that MIT Critical Data has organized in more than two dozen countries, which apply the latest data science techniques to real-world health data. At the datathons, MIT students and faculty both learn from local experts and share their own skill sets. Many of these several-day events are sponsored by the MIT Industrial Liaison Program, the MIT International Science and Technology Initiatives program, or the MIT Sloan Latin America Office. 

      Datathons are typically held in that country’s national language or dialect, rather than English, with representation from academia, industry, government, and other stakeholders. Doctors, nurses, pharmacists, and social workers join up with computer science, engineering, and humanities students to brainstorm and analyze potential solutions. “They need each other’s expertise to fully leverage and discover and validate the knowledge that is encrypted in the data, and that will be translated into the way they deliver care,” says Celi. 

      “Everywhere we go, there is incredible talent that is completely capable of designing solutions to their health-care problems,” he emphasizes. The datathons aim to further empower the professionals and students in the host countries to drive medical research, innovation, and entrepreneurship.

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      Fighting built-in bias 

      Applying machine learning and other advanced data science techniques to medical data reveals that “bias exists in the data in unimaginable ways” in every type of health product, Celi says. Often this bias is rooted in the clinical trials required to approve medical devices and therapies. 

      One dramatic example comes from pulse oximeters, which provide readouts on oxygen levels in a patient’s blood. It turns out that these devices overestimate oxygen levels for people of color. “We have been under-treating individuals of color because the nurses and the doctors have been falsely assured that their patients have adequate oxygenation,” he says. “We think that we have harmed, if not killed, a lot of individuals in the past, especially during Covid, as a result of a technology that was not designed with inclusive test subjects.” 

      Such dangers only increase as the universe of medical data expands. “The data that we have available now for research is maybe two or three levels of magnitude more than what we had even 10 years ago,” Celi says. MIMIC, for example, now includes terabytes of X-ray, echocardiogram, and electrocardiogram data, all linked with related health records. Such enormous sets of data allow investigators to detect health patterns that were previously invisible. 

      “But there is a caveat,” Celi says. “It is trivial for computers to learn sensitive attributes that are not very obvious to human experts.” In a study released last year, for instance, he and his colleagues showed that algorithms can tell if a chest X-ray image belongs to a white patient or person of color, even without looking at any other clinical data. 

      “More concerningly, groups including ours have demonstrated that computers can learn easily if you’re rich or poor, just from your imaging alone,” Celi says. “We were able to train a computer to predict if you are on Medicaid, or if you have private insurance, if you feed them with chest X-rays without any abnormality. So again, computers are catching features that are not visible to the human eye.” And these features may lead algorithms to advise against therapies for people who are Black or poor, he says. 

      Opening up industry opportunities 

      Every stakeholder stands to benefit when pharmaceutical firms and other health-care corporations better understand societal needs and can target their treatments appropriately, Celi says. 

      “We need to bring to the table the vendors of electronic health records and the medical device manufacturers, as well as the pharmaceutical companies,” he explains. “They need to be more aware of the disparities in the way that they perform their research. They need to have more investigators representing underrepresented groups of people, to provide that lens to come up with better designs of health products.” 

      Corporations could benefit by sharing results from their clinical trials, and could immediately see these potential benefits by participating in datathons, Celi says. “They could really witness the magic that happens when that data is curated and analyzed by students and clinicians with different backgrounds from different countries. So we’re calling out our partners in the pharmaceutical industry to organize these events with us!”  More

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      Success at the intersection of technology and finance

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      Citadel founder and CEO Ken Griffin had some free advice for an at-capacity crowd of MIT students at the Wong Auditorium during a campus visit in April. “If you find yourself in a career where you’re not learning,” he told them, “it’s time to change jobs. In this world, if you’re not learning, you can find yourself irrelevant in the blink of an eye.”

      During a conversation with Bryan Landman ’11, senior quantitative research lead for Citadel’s Global Quantitative Strategies business, Griffin reflected on his career and offered predictions for the impact of technology on the finance sector. Citadel, which he launched in 1990, is now one of the world’s leading investment firms. Griffin also serves as non-executive chair of Citadel Securities, a market maker that is known as a key player in the modernization of markets and market structures.

      “We are excited to hear Ken share his perspective on how technology continues to shape the future of finance, including the emerging trends of quantum computing and AI,” said David Schmittlein, the John C Head III Dean and professor of marketing at MIT Sloan School of Management, who kicked off the program. The presentation was jointly sponsored by MIT Sloan, the MIT Schwarzman College of Computing, the School of Engineering, MIT Career Advising and Professional Development, and Citadel Securities Campus Recruiting.

      The future, in Griffin’s view, “is all about the application of engineering, software, and mathematics to markets. Successful entrepreneurs are those who have the tools to solve the unsolved problems of that moment in time.” He launched Citadel only one year after graduating from college. “History so far has been kind to the vision I had back in the late ’80s,” he said.

      Griffin realized very early in his career “that you could use a personal computer and quantitative finance to price traded securities in a way that was much more advanced than you saw on your typical equity trading desk on Wall Street.” Both businesses, he told the audience, are ultimately driven by research. “That’s where we formulate the ideas, and trading is how we monetize that research.”

      It’s also why Citadel and Citadel Securities employ several hundred software engineers. “We have a huge investment today in using modern technology to power our decision-making and trading,” said Griffin.

      One example of Citadel’s application of technology and science is the firm’s hiring of a meteorological team to expand the weather analytics expertise within its commodities business. While power supply is relatively easy to map and analyze, predicting demand is much more difficult. Citadel’s weather team feeds forecast data obtained from supercomputers to its traders. “Wind and solar are huge commodities,” Griffin explained, noting that the days with highest demand in the power market are cloudy, cold days with no wind. When you can forecast those days better than the market as a whole, that’s where you can identify opportunities, he added.

      Pros and cons of machine learning

      Asking about the impact of new technology on their sector, Landman noted that both Citadel and Citadel Securities are already leveraging machine learning. “In the market-making business,” Griffin said, “you see a real application for machine learning because you have so much data to parametrize the models with. But when you get into longer time horizon problems, machine learning starts to break down.”

      Griffin noted that the data obtained through machine learning is most helpful for investments with short time horizons, such as in its quantitative strategies business. “In our fundamental equities business,” he said, “machine learning is not as helpful as you would want because the underlying systems are not stationary.”

      Griffin was emphatic that “there has been a moment in time where being a really good statistician or really understanding machine-learning models was sufficient to make money. That won’t be the case for much longer.” One of the guiding principles at Citadel, he and Landman agreed, was that machine learning and other methodologies should not be used blindly. Each analyst has to cite the underlying economic theory driving their argument on investment decisions. “If you understand the problem in a different way than people who are just using the statistical models,” he said, “you have a real chance for a competitive advantage.”

      ChatGPT and a seismic shift

      Asked if ChatGPT will change history, Griffin predicted that the rise of capabilities in large language models will transform a substantial number of white collar jobs. “With open AI for most routine commercial legal documents, ChatGPT will do a better job writing a lease than a young lawyer. This is the first time we are seeing traditionally white-collar jobs at risk due to technology, and that’s a sea change.”

      Griffin urged MIT students to work with the smartest people they can find, as he did: “The magic of Citadel has been a testament to the idea that by surrounding yourself with bright, ambitious people, you can accomplish something special. I went to great lengths to hire the brightest people I could find and gave them responsibility and trust early in their careers.”

      Even more critical to success is the willingness to advocate for oneself, Griffin said, using Gerald Beeson, Citadel’s chief operating officer, as an example. Beeson, who started as an intern at the firm, “consistently sought more responsibility and had the foresight to train his own successors.” Urging students to take ownership of their careers, Griffin advised: “Make it clear that you’re willing to take on more responsibility, and think about what the roadblocks will be.”

      When microphones were handed to the audience, students inquired what changes Griffin would like to see in the hedge fund industry, how Citadel assesses the risk and reward of potential projects, and whether hedge funds should give back to the open source community. Asked about the role that Citadel — and its CEO — should play in “the wider society,” Griffin spoke enthusiastically of his belief in participatory democracy. “We need better people on both sides of the aisle,” he said. “I encourage all my colleagues to be politically active. It’s unfortunate when firms shut down political dialogue; we actually embrace it.”

      Closing on an optimistic note, Griffin urged the students in the audience to go after success, declaring, “The world is always awash in challenge and its shortcomings, but no matter what anybody says, you live at the greatest moment in the history of the planet. Make the most of it.” More

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      Boosting passenger experience and increasing connectivity at the Hong Kong International Airport

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      Recently, a cohort of 36 students from MIT and universities across Hong Kong came together for the MIT Entrepreneurship and Maker Skills Integrator (MEMSI), an intense two-week startup boot camp hosted at the MIT Hong Kong Innovation Node.

      “We’re very excited to be in Hong Kong,” said Professor Charles Sodini, LeBel Professor of Electrical Engineering and faculty director of the Node. “The dream always was to bring MIT and Hong Kong students together.”

      Students collaborated on six teams to meet real-world industry challenges through action learning, defining a problem, designing a solution, and crafting a business plan. The experience culminated in the MEMSI Showcase, where each team presented its process and unique solution to a panel of judges. “The MEMSI program is a great demonstration of important international educational goals for MIT,” says Professor Richard Lester, associate provost for international activities and chair of the Node Steering Committee at MIT. “It creates opportunities for our students to solve problems in a particular and distinctive cultural context, and to learn how innovations can cross international boundaries.” 

      Meeting an urgent challenge in the travel and tourism industry

      The Hong Kong Airport Authority (AAHK) served as the program’s industry partner for the third consecutive year, challenging students to conceive innovative ideas to make passenger travel more personalized from end-to-end while increasing connectivity. As the travel industry resuscitates profitability and welcomes crowds back amidst ongoing delays and labor shortages, the need for a more passenger-centric travel ecosystem is urgent.

      The airport is the third-busiest international passenger airport and the world’s busiest cargo transit. Students experienced an insider’s tour of the Hong Kong International Airport to gain on-the-ground orientation. They observed firsthand the complex logistics, possibilities, and constraints of operating with a team of 78,000 employees who serve 71.5 million passengers with unique needs and itineraries.

      Throughout the program, the cohort was coached and supported by MEMSI alumni, travel industry mentors, and MIT faculty such as Richard de Neufville, professor of engineering systems.

      The mood inside the open-plan MIT Hong Kong Innovation Node was nonstop energetic excitement for the entire program. Each of the six teams was composed of students from MIT and from Hong Kong universities. They learned to work together under time pressure, develop solutions, receive feedback from industry mentors, and iterate around the clock.

      “MEMSI was an enriching and amazing opportunity to learn about entrepreneurship while collaborating with a diverse team to solve a complex problem,” says Maria Li, a junior majoring in computer science, economics, and data science at MIT. “It was incredible to see the ideas we initially came up with as a team turn into a single, thought-out solution by the end.”

      Unsurprisingly given MIT’s focus on piloting the latest technology and the tech-savvy culture of Hong Kong as a global center, many team projects focused on virtual reality, apps, and wearable technology designed to make passengers’ journeys more individualized, efficient, or enjoyable.

      After observing geospatial patterns charting passengers’ movement through an airport, one team realized that many people on long trips aim to meet fitness goals by consciously getting their daily steps power walking the expansive terminals. The team’s prototype, FitAir, is a smart, biometric token integrated virtual coach, which plans walking routes within the airport to promote passenger health and wellness.

      Another team noted a common frustration among frequent travelers who manage multiple mileage rewards program profiles, passwords, and status reports. They proposed AirPoint, a digital wallet that consolidates different rewards programs and presents passengers with all their airport redemption opportunities in one place.

      “Today, there is no loser,” said Vivian Cheung, chief operating officer of AAHK, who served as one of the judges. “Everyone is a winner. I am a winner, too. I have learned a lot from the showcase. Some of the ideas, I believe, can really become a business.”

      Cheung noted that in just 12 days, all teams observed and solved her organization’s pain points and successfully designed solutions to address them.

      More than a competition

      Although many of the models pitched are inventive enough to potentially shape the future of travel, the main focus of MEMSI isn’t to act as yet another startup challenge and incubator.

      “What we’re really focusing on is giving students the ability to learn entrepreneurial thinking,” explains Marina Chan, senior director and head of education at the Node. “It’s the dynamic experience in a highly connected environment that makes being in Hong Kong truly unique. When students can adapt and apply theory to an international context, it builds deeper cultural competency.”

      From an aerial view, the boot camp produced many entrepreneurs in the making and lasting friendships, and respect for other cultural backgrounds and operating environments.

      “I learned the overarching process of how to make a startup pitch, all the way from idea generation, market research, and making business models, to the pitch itself and the presentation,” says Arun Wongprommoon, a senior double majoring in computer science and engineering and linguistics.  “It was all a black box to me before I came into the program.”

      He said he gained tremendous respect for the startup world and the pure hard work and collaboration required to get ahead.

      Spearheaded by the Node, MEMSI is a collaboration among the MIT Innovation Initiative, the Martin Trust Center for Entrepreneurship, the MIT International Science and Technology Initiatives, and Project Manus. Learn more about applying to MEMSI. More

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      in Data Management & Statistics

      Report: CHIPS Act just the first step in addressing threats to US leadership in advanced computing

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      When Liu He, a Chinese economist, politician, and “chip czar,” was tapped to lead the charge in a chipmaking arms race with the United States, his message lingered in the air, leaving behind a dewy glaze of tension: “For our country, technology is not just for growth… it is a matter of survival.”

      Once upon a time, the United States’ early technological prowess positioned the nation to outpace foreign rivals and cultivate a competitive advantage for domestic businesses. Yet, 30 years later, America’s lead in advanced computing is continuing to wane. What happened?

      A new report from an MIT researcher and two colleagues sheds light on the decline in U.S. leadership. The scientists looked at high-level measures to examine the shrinkage: overall capabilities, supercomputers, applied algorithms, and semiconductor manufacturing. Through their analysis, they found that not only has China closed the computing gap with the U.S., but nearly 80 percent of American leaders in the field believe that their Chinese competitors are improving capabilities faster — which, the team says, suggests a “broad threat to U.S. competitiveness.”

      To delve deeply into the fray, the scientists conducted the Advanced Computing Users Survey, sampling 120 top-tier organizations, including universities, national labs, federal agencies, and industry. The team estimates that this group comprises one-third and one-half of all the most significant computing users in the United States.

      “Advanced computing is crucial to scientific improvement, economic growth and the competitiveness of U.S. companies,” says Neil Thompson, director of the FutureTech Research Project at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who helped lead the study.

      Thompson, who is also a principal investigator at MIT’s Initiative on the Digital Economy, wrote the paper with Chad Evans, executive vice president and secretary and treasurer to the board at the Council on Competitiveness, and Daniel Armbrust, who is the co-founder, initial CEO, and member of the board of directors at Silicon Catalyst and former president of SEMATECH, the semiconductor consortium that developed industry roadmaps.

      The semiconductor, supercomputer, and algorithm bonanza

      Supercomputers — the room-sized, “giant calculators” of the hardware world — are an industry no longer dominated by the United States. Through 2015, about half of the most powerful computers were sitting firmly in the U.S., and China was growing slowly from a very slow base. But in the past six years, China has swiftly caught up, reaching near parity with America.

      This disappearing lead matters. Eighty-four percent of U.S. survey respondents said they’re computationally constrained in running essential programs. “This result was telling, given who our respondents are: the vanguard of American research enterprises and academic institutions with privileged access to advanced national supercomputing resources,” says Thompson. 

      With regards to advanced algorithms, historically, the U.S. has fronted the charge, with two-thirds of all significant improvements dominated by U.S.-born inventors. But in recent decades, U.S. dominance in algorithms has relied on bringing in foreign talent to work in the U.S., which the researchers say is now in jeopardy. China has outpaced the U.S. and many other countries in churning out PhDs in STEM fields since 2007, with one report postulating a near-distant future (2025) where China will be home to nearly twice as many PhDs than in the U.S. China’s rise in algorithms can also be seen with the “Gordon Bell Prize,” an achievement for outstanding work in harnessing the power of supercomputers in varied applications. U.S. winners historically dominated the prize, but China has now equaled or surpassed Americans’ performance in the past five years.

      While the researchers note the CHIPS and Science Act of 2022 is a critical step in re-establishing the foundation of success for advanced computing, they propose recommendations to the U.S. Office of Science and Technology Policy. 

      First, they suggest democratizing access to U.S. supercomputing by building more mid-tier systems that push boundaries for many users, as well as building tools so users scaling up computations can have less up-front resource investment. They also recommend increasing the pool of innovators by funding many more electrical engineers and computer scientists being trained with longer-term US residency incentives and scholarships. Finally, in addition to this new framework, the scientists urge taking advantage of what already exists, via providing the private sector access to experimentation with high-performance computing through supercomputing sites in academia and national labs.

      All that and a bag of chips

      Computing improvements depend on continuous advances in transistor density and performance, but creating robust, new chips necessitate a harmonious blend of design and manufacturing.

      Over the last six years, China was not known as the savants of noteworthy chips. In fact, in the past five decades, the U.S. designed most of them. But this changed in the past six years when China created the HiSilicon Kirin 9000, propelling itself to the international frontier. This success was mainly obtained through partnerships with leading global chip designers that began in the 2000s. Now, China now has 14 companies among the world’s top 50 fabless designers. A decade ago, there was only one. 

      Competitive semiconductor manufacturing has been more mixed, where U.S.-led policies and internal execution issues have slowed China’s rise, but as of July 2022, the Semiconductor Manufacturing International Corporation (SMIC) has evidence of 7 nanometer logic, which was not expected until much later. However, with extreme ultraviolet export restrictions, progress below 7 nm means domestic technology development would be expensive. Currently, China is only at parity or better in two out of 12 segments of the semiconductor supply chain. Still, with government policy and investments, the team expects a whopping increase to seven segments in 10 years. So, for the moment, the U.S. retains leadership in hardware manufacturing, but with fewer dimensions of advantage.

      The authors recommend that the White House Office of Science and Technology Policy work with key national agencies, such as the U.S. Department of Defense, U.S. Department of Energy, and the National Science Foundation, to define initiatives to build the hardware and software systems needed for important computing paradigms and workloads critical for economic and security goals. “It is crucial that American enterprises can get the benefit of faster computers,” says Thompson. “With Moore’s Law slowing down, the best way to do this is to create a portfolio of specialized chips (or “accelerators”) that are customized to our needs.”

      The scientists further believe that to lead the next generation of computing, four areas must be addressed. First, by issuing grand challenges to the CHIPS Act National Semiconductor Technology Center, researchers and startups would be motivated to invest in research and development and to seek startup capital for new technologies in areas such as spintronics, neuromorphics, optical and quantum computing, and optical interconnect fabrics. By supporting allies in passing similar acts, overall investment in these technologies would increase, and supply chains would become more aligned and secure. Establishing test beds for researchers to test algorithms on new computing architectures and hardware would provide an essential platform for innovation and discovery. Finally, planning for post-exascale systems that achieve higher levels of performance through next-generation advances would ensure that current commercial technologies don’t limit future computing systems.

      “The advanced computing landscape is in rapid flux — technologically, economically, and politically, with both new opportunities for innovation and rising global rivalries,” says Daniel Reed, Presidential Professor and professor of computer science and electrical and computer engineering at the University of Utah. “The transformational insights from both deep learning and computational modeling depend on both continued semiconductor advances and their instantiation in leading edge, large-scale computing systems — hyperscale clouds and high-performance computing systems. Although the U.S. has historically led the world in both advanced semiconductors and high-performance computing, other nations have recognized that these capabilities are integral to 21st century economic competitiveness and national security, and they are investing heavily.”

      The research was funded, in part, through Thompson’s grant from Good Ventures, which supports his FutureTech Research Group. The paper is being published by the Georgetown Public Policy Review. More

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      in Data Management & Statistics

      Putting clear bounds on uncertainty

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      In science and technology, there has been a long and steady drive toward improving the accuracy of measurements of all kinds, along with parallel efforts to enhance the resolution of images. An accompanying goal is to reduce the uncertainty in the estimates that can be made, and the inferences drawn, from the data (visual or otherwise) that have been collected. Yet uncertainty can never be wholly eliminated. And since we have to live with it, at least to some extent, there is much to be gained by quantifying the uncertainty as precisely as possible.

      Expressed in other terms, we’d like to know just how uncertain our uncertainty is.

      That issue was taken up in a new study, led by Swami Sankaranarayanan, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), and his co-authors — Anastasios Angelopoulos and Stephen Bates of the University of California at Berkeley; Yaniv Romano of Technion, the Israel Institute of Technology; and Phillip Isola, an associate professor of electrical engineering and computer science at MIT. These researchers succeeded not only in obtaining accurate measures of uncertainty, they also found a way to display uncertainty in a manner the average person could grasp.

      Their paper, which was presented in December at the Neural Information Processing Systems Conference in New Orleans, relates to computer vision — a field of artificial intelligence that involves training computers to glean information from digital images. The focus of this research is on images that are partially smudged or corrupted (due to missing pixels), as well as on methods — computer algorithms, in particular — that are designed to uncover the part of the signal that is marred or otherwise concealed. An algorithm of this sort, Sankaranarayanan explains, “takes the blurred image as the input and gives you a clean image as the output” — a process that typically occurs in a couple of steps.

      First, there is an encoder, a kind of neural network specifically trained by the researchers for the task of de-blurring fuzzy images. The encoder takes a distorted image and, from that, creates an abstract (or “latent”) representation of a clean image in a form — consisting of a list of numbers — that is intelligible to a computer but would not make sense to most humans. The next step is a decoder, of which there are a couple of types, that are again usually neural networks. Sankaranarayanan and his colleagues worked with a kind of decoder called a “generative” model. In particular, they used an off-the-shelf version called StyleGAN, which takes the numbers from the encoded representation (of a cat, for instance) as its input and then constructs a complete, cleaned-up image (of that particular cat). So the entire process, including the encoding and decoding stages, yields a crisp picture from an originally muddied rendering.

      But how much faith can someone place in the accuracy of the resultant image? And, as addressed in the December 2022 paper, what is the best way to represent the uncertainty in that image? The standard approach is to create a “saliency map,” which ascribes a probability value — somewhere between 0 and 1 — to indicate the confidence the model has in the correctness of every pixel, taken one at a time. This strategy has a drawback, according to Sankaranarayanan, “because the prediction is performed independently for each pixel. But meaningful objects occur within groups of pixels, not within an individual pixel,” he adds, which is why he and his colleagues are proposing an entirely different way of assessing uncertainty.

      Their approach is centered around the “semantic attributes” of an image — groups of pixels that, when taken together, have meaning, making up a human face, for example, or a dog, or some other recognizable thing. The objective, Sankaranarayanan maintains, “is to estimate uncertainty in a way that relates to the groupings of pixels that humans can readily interpret.”

      Whereas the standard method might yield a single image, constituting the “best guess” as to what the true picture should be, the uncertainty in that representation is normally hard to discern. The new paper argues that for use in the real world, uncertainty should be presented in a way that holds meaning for people who are not experts in machine learning. Rather than producing a single image, the authors have devised a procedure for generating a range of images — each of which might be correct. Moreover, they can set precise bounds on the range, or interval, and provide a probabilistic guarantee that the true depiction lies somewhere within that range. A narrower range can be provided if the user is comfortable with, say, 90 percent certitude, and a narrower range still if more risk is acceptable.

      The authors believe their paper puts forth the first algorithm, designed for a generative model, which can establish uncertainty intervals that relate to meaningful (semantically-interpretable) features of an image and come with “a formal statistical guarantee.” While that is an important milestone, Sankaranarayanan considers it merely a step toward “the ultimate goal. So far, we have been able to do this for simple things, like restoring images of human faces or animals, but we want to extend this approach into more critical domains, such as medical imaging, where our ‘statistical guarantee’ could be especially important.”

      Suppose that the film, or radiograph, of a chest X-ray is blurred, he adds, “and you want to reconstruct the image. If you are given a range of images, you want to know that the true image is contained within that range, so you are not missing anything critical” — information that might reveal whether or not a patient has lung cancer or pneumonia. In fact, Sankaranarayanan and his colleagues have already begun working with a radiologist to see if their algorithm for predicting pneumonia could be useful in a clinical setting.

      Their work may also have relevance in the law enforcement field, he says. “The picture from a surveillance camera may be blurry, and you want to enhance that. Models for doing that already exist, but it is not easy to gauge the uncertainty. And you don’t want to make a mistake in a life-or-death situation.” The tools that he and his colleagues are developing could help identify a guilty person and help exonerate an innocent one as well.

      Much of what we do and many of the things happening in the world around us are shrouded in uncertainty, Sankaranarayanan notes. Therefore, gaining a firmer grasp of that uncertainty could help us in countless ways. For one thing, it can tell us more about exactly what it is we do not know.

      Angelopoulos was supported by the National Science Foundation. Bates was supported by the Foundations of Data Science Institute and the Simons Institute. Romano was supported by the Israel Science Foundation and by a Career Advancement Fellowship from Technion. Sankaranarayanan’s and Isola’s research for this project was sponsored by the U.S. Air Force Research Laboratory and the U.S. Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2- 1000. MIT SuperCloud and the Lincoln Laboratory Supercomputing Center also provided computing resources that contributed to the results reported in this work. More