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      in Data Management & Statistics

      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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      in Data Management & Statistics

      Festival of Learning 2023 underscores importance of well-designed learning environments

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      During its first in-person gathering since 2020, MIT’s Festival of Learning 2023 explored how the learning sciences can inform the Institute on how to best support students. Co-sponsored by MIT Open Learning and the Office of the Vice Chancellor (OVC), this annual event celebrates teaching and learning innovations with MIT instructors, students, and staff.

      Bror Saxberg SM ’85, PhD ’89, founder of LearningForge LLC and former chief learning officer at Kaplan, Inc., was invited as keynote speaker, with opening remarks by MIT Chancellor Melissa Nobles and Vice President for Open Learning Eric Grimson, and discussion moderated by Senior Associate Dean of Open Learning Christopher Capozzola. This year’s festival focused on how creating well-designed learning environments using learning engineering can increase learning success.

      Play video

      2023 Festival of Learning: Highlights

      Well-designed learning environments are key

      In his keynote speech “Learning Engineering: What We Know, What We Can Do,” Saxberg defined “learning engineering” as the practical application of learning sciences to real-world problems at scale. He said, “High levels can be reached by all learners, given access to well-designed instruction and motivation for enough practice opportunities.”

      Informed by decades of empirical evidence from the field of learning science, Saxberg’s own research, and insights from Kaplan, Inc., Saxberg finds that a hands-on strategy he calls “prepare, practice, perform” delivers better learning outcomes than a traditional “read, write, discuss” approach. Saxberg recommends educators devote at least 60 percent of learning time to hands-on approaches, such as producing, creating, and engaging. Only 20-30 percent of learning time should be spent in the more passive “knowledge acquisition” modes of listening and reading.

      “Here at MIT, a place that relies on data to make informed decisions, learning engineering can provide a framework for us to center in on the learner to identify the challenges associated with learning, and to apply the learning sciences in data-driven ways to improve instructional approaches,” said Nobles. During their opening remarks, Nobles and Grimson both emphasized how learning engineering at MIT is informed by the Institute’s commitment to educating the whole student, which encompasses student well-being and belonging in addition to academic rigor. “What lessons can we take away to change the way we think about education moving forward? This is a chance to iterate,” said Grimson.

      Well-designed learning environments are informed by understanding motivation, considering the connection between long-term and working memory, identifying the range of learners’ prior experience, grounding practice in authentic contexts (i.e., work environments), and using data-driven instructional approaches to iterate and improve.

      Play video

      2023 Festival of Learning: Keynote by Bror Saxberg

      Understand learner motivation

      Saxberg asserted that before developing course structures and teaching approaches known to encourage learning, educators must first examine learner motivation. Motivation doesn’t require enjoyment of the subject or task to spur engagement. Similar to how a well-designed physical training program can change your muscle cells, if a learner starts, persists, and exerts mental effort in a well-designed learning environment, they can change their neurons — they learn. Saxberg described four main barriers to learner motivation, and solutions for each:

      The learner doesn’t see the value of the lesson. Ways to address this include helping the learners find value; leveraging the learner’s expertise in another area to better understand the topic at hand; and making the activity itself enjoyable. “Finding value” could be as simple as explaining the practical applications of this knowledge in their future work in the field, or how this lesson prepares learners for their advanced level courses. 
      Self-efficacy for learners who don’t think they’re capable. Educators can point to parallel experiences with similar goals that students may have already achieved in another context. Alternatively, educators can share stories of professionals who have successfully transitioned from one area of expertise to another. 
      “Something” in the learner’s way, such as not having the time, space, or correct materials. This is an opportunity to demonstrate how a learner can use problem-solving skills to find a solution to their perceived problem. As with the barrier of self-efficacy, educators can assure learners that they are in control of the situation by sharing similar stories of those who’ve encountered the same problem and the solution they devised.
      The learner’s emotional state. This is no small barrier to motivation. If a learner is angry, depressed, scared, or grieving, it will be challenging for them to switch their mindset into learning mode. A wide array of emotions require a wide array of possible solutions, from structured conversation techniques to recommending professional help.
      Consider the cognitive load

      Saxberg has found that learning occurs when we use working memory to problem-solve, but our working memory can only process three to five verbal or conscious thoughts at a time. Long-term memory stores knowledge that can be accessed non-verbally and non-consciously, which is why experts appear to remember information effortlessly. Until a learner develops that expertise, extraneous information in a lesson will occupy space in their working memory, running the risk of distracting the learner from the desired learning outcome.

      To accommodate learners’ finite cognitive load, Saxberg suggested the solution of reevaluating which material is essential, then simplifying the exercise or removing unnecessary material accordingly. “That notion of, ‘what do we really need students to be able to do?’ helps you focus,” said Saxberg.

      Another solution is to leverage the knowledge, skills, and interests learners already bring to the course — these long-term memories can scaffold the new material. “What do you have in your head already, what do you love, what’s easy to draw from long-term memory? That would be the starting point for challenging new skills. It’s not the ending point because you want to use your new skills to then find out new things,” Saxberg said. Finally, consider how your course engages with the syllabi. Do you explain the reasoning behind the course structure? Do you show how the exercises or material will be applied to future courses or the field? Do you share best practices for engaging working memory and learning? By acknowledging and empathizing with the practical challenges that learners face, you can remove a barrier from their cognitive load.

      Ground practice in authentic contexts

      Saxberg stated that few experts read textbooks to learn new information — they discover what they need to know while working in the field, using those relevant facts in context. As such, students will have an easier time remembering facts if they’re practicing in relevant or similar environments to their future work.

      If students can practice classifying problems in real work contexts rather than theoretical practice problems, they can build a framework to classify what’s important. That helps students recognize the type of problem they’re trying to solve before trying to solve the problem itself. With enough hands-on practice and examples of how experts use processes and identify which principles are relevant, learners can holistically learn entire procedures. And that learning continues once learners graduate to the workforce: professionals often meet to exchange knowledge at conferences, charrettes, and other gatherings.

      Enhancing teaching at MIT

      The Festival of Learning furthers the Office of the Chancellor’s mission to advance academic innovation that will foster the growth of MIT students. The festival also aligns with the MIT Open Learning’s Residential Education team’s goal of making MIT education more effective and efficient. Throughout the year, their team offers continuous support to MIT faculty and instructors using digital technologies to augment and transform how they teach.

      “We are doubling down on our commitment to continuous growth in how we teach,” said Nobles. More

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      A breakthrough on “loss and damage,” but also disappointment, at UN climate conference

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      As the 2022 United Nations climate change conference, known as COP27, stretched into its final hours on Saturday, Nov. 19, it was uncertain what kind of agreement might emerge from two weeks of intensive international negotiations.

      In the end, COP27 produced mixed results: on the one hand, a historic agreement for wealthy countries to compensate low-income countries for “loss and damage,” but on the other, limited progress on new plans for reducing the greenhouse gas emissions that are warming the planet.

      “We need to drastically reduce emissions now — and this is an issue this COP did not address,” said U.N. Secretary-General António Guterres in a statement at the conclusion of COP27. “A fund for loss and damage is essential — but it’s not an answer if the climate crisis washes a small island state off the map — or turns an entire African country to desert.”

      Throughout the two weeks of the conference, a delegation of MIT students, faculty, and staff was at the Sharm El-Sheikh International Convention Center to observe the negotiations, conduct and share research, participate in panel discussions, and forge new connections with researchers, policymakers, and advocates from around the world.

      Loss and damage

      A key issue coming in to COP27 (COP stands for “conference of the parties” to the U.N. Framework Convention on Climate Change, held for the 27th time) was loss and damage: a term used by the U.N. to refer to harms caused by climate change — either through acute catastrophes like extreme weather events or slower-moving impacts like sea level rise — to which communities and countries are unable to adapt. 

      Ultimately, a deal on loss and damage proved to be COP27’s most prominent accomplishment. Negotiators reached an eleventh-hour agreement to “establish new funding arrangements for assisting developing countries that are particularly vulnerable to the adverse effects of climate change.” 

      “Providing financial assistance to developing countries so they can better respond to climate-related loss and damage is not only a moral issue, but also a pragmatic one,” said Michael Mehling, deputy director of the MIT Center for Energy and Environmental Policy Research, who attended COP27 and participated in side events. “Future emissions growth will be squarely centered in the developing world, and offering support through different channels is key to building the trust needed for more robust global cooperation on mitigation.”

      Youssef Shaker, a graduate student in the MIT Technology and Policy Program and a research assistant with the MIT Energy Initiative, attended the second week of the conference, where he followed the negotiations over loss and damage closely. 

      “While the creation of a fund is certainly an achievement,” Shaker said, “significant questions remain to be answered, such as the size of the funding available as well as which countries receive access to it.” A loss-and-damage fund that is not adequately funded, Shaker noted, “would not be an impactful outcome.” 

      The agreement on loss and damage created a new committee, made up of 24 country representatives, to “operationalize” the new funding arrangements, including identifying funding sources. The committee is tasked with delivering a set of recommendations at COP28, which will take place next year in Dubai.

      Advising the U.N. on net zero

      Though the decisions reached at COP27 did not include major new commitments on reducing emissions from the combustion of fossil fuels, the transition to a clean global energy system was nevertheless a key topic of conversation throughout the conference.

      The Council of Engineers for the Energy Transition (CEET), an independent, international body of engineers and energy systems experts formed to provide advice to the U.N. on achieving net-zero emissions globally by 2050, convened for the first time at COP27. Jessika Trancik, a professor in the MIT Institute for Data, Systems, and Society and a member of CEET, spoke on a U.N.-sponsored panel on solutions for the transition to clean energy.

      Trancik noted that the energy transition will look different in different regions of the world. “As engineers, we need to understand those local contexts and design solutions around those local contexts — that’s absolutely essential to support a rapid and equitable energy transition.”

      At the same time, Trancik noted that there is now a set of “low-cost, ready-to-scale tools” available to every region — tools that resulted from a globally competitive process of innovation, stimulated by public policies in different countries, that dramatically drove down the costs of technologies like solar energy and lithium-ion batteries. The key, Trancik said, is for regional transition strategies to “tap into global processes of innovation.”

      Reinventing climate adaptation

      Elfatih Eltahir, the H. M. King Bhumibol Professor of Hydrology and Climate, traveled to COP27 to present plans for the Jameel Observatory Climate Resilience Early Warning System (CREWSnet), one of the five projects selected in April 2022 as a flagship in MIT’s Climate Grand Challenges initiative. CREWSnet focuses on climate adaptation, the term for adapting to climate impacts that are unavoidable.

      The aim of CREWSnet, Eltahir told the audience during a panel discussion, is “nothing short of reinventing the process of climate change adaptation,” so that it is proactive rather than reactive; community-led; data-driven and evidence-based; and so that it integrates different climate risks, from heat waves to sea level rise, rather than treating them individually.

      “However, it’s easy to talk about these changes,” said Eltahir. “The real challenge, which we are now just launching and engaging in, is to demonstrate that on the ground.” Eltahir said that early demonstrations will happen in a couple of key locations, including southwest Bangladesh, where multiple climate risks — rising sea levels, increasing soil salinity, and intensifying heat waves and cyclones — are combining to threaten the area’s agricultural production.

      Building on COP26

      Some members of MIT’s delegation attended COP27 to advance efforts that had been formally announced at last year’s U.N. climate conference, COP26, in Glasgow, Scotland.

      At an official U.N. side event co-organized by MIT on Nov. 11, Greg Sixt, the director of the Food and Climate Systems Transformation (FACT) Alliance led by the Abdul Latif Jameel Water and Food Systems Lab, provided an update on the alliance’s work since its launch at COP26.

      Food systems are a major source of greenhouse gas emissions — and are increasingly vulnerable to climate impacts. The FACT Alliance works to better connect researchers to farmers, food businesses, policymakers, and other food systems stakeholders to make food systems (which include food production, consumption, and waste) more sustainable and resilient. 

      Sixt told the audience that the FACT Alliance now counts over 20 research and stakeholder institutions around the world among its members, but also collaborates with other institutions in an “open network model” to advance work in key areas — such as a new research project exploring how climate scenarios could affect global food supply chains.

      Marcela Angel, research program director for the Environmental Solutions Initiative (ESI), helped convene a meeting at COP27 of the Afro-InterAmerican Forum on Climate Change, which also launched at COP26. The forum works with Afro-descendant leaders across the Americas to address significant environmental issues, including climate risks and biodiversity loss. 

      At the event — convened with the Colombian government and the nonprofit Conservation International — ESI brought together leaders from six countries in the Americas and presented recent work that estimates that there are over 178 million individuals who identify as Afro-descendant living in the Americas, in lands of global environmental importance. 

      “There is a significant overlap between biodiversity hot spots, protected areas, and areas of high Afro-descendant presence,” said Angel. “But the role and climate contributions of these communities is understudied, and often made invisible.”    

      Limiting methane emissions

      Methane is a short-lived but potent greenhouse gas: When released into the atmosphere, it immediately traps about 120 times more heat than carbon dioxide does. More than 150 countries have now signed the Global Methane Pledge, launched at COP26, which aims to reduce methane emissions by at least 30 percent by 2030 compared to 2020 levels.

      Sergey Paltsev, the deputy director of the Joint Program on the Science and Policy of Global Change and a senior research scientist at the MIT Energy Initiative, gave the keynote address at a Nov. 17 event on methane, where he noted the importance of methane reductions from the oil and gas sector to meeting the 2030 goal.

      “The oil and gas sector is where methane emissions reductions could be achieved the fastest,” said Paltsev. “We also need to employ an integrated approach to address methane emissions in all sectors and all regions of the world because methane emissions reductions provide a near-term pathway to avoiding dangerous tipping points in the global climate system.”

      “Keep fighting relentlessly”

      Arina Khotimsky, a senior majoring in materials science and engineering and a co-president of the MIT Energy and Climate Club, attended the first week of COP27. She reflected on the experience in a social media post after returning home. 

      “COP will always have its haters. Is there greenwashing? Of course! Is everyone who should have a say in this process in the room? Not even close,” wrote Khotimsky. “So what does it take for COP to matter? It takes everyone who attended to not only put ‘climate’ on front-page news for two weeks, but to return home and keep fighting relentlessly against climate change. I know that I will.” More

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      MIT Policy Hackathon produces new solutions for technology policy challenges

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      Almost three years ago, the Covid-19 pandemic changed the world. Many are still looking to uncover a “new normal.”

      “Instead of going back to normal, [there’s a new generation that] wants to build back something different, something better,” says Jorge Sandoval, a second-year graduate student in MIT’s Technology and Policy Program (TPP) at the Institute for Data, Systems and Society (IDSS). “How do we communicate this mindset to others, that the world cannot be the same as before?”

      This was the inspiration behind “A New (Re)generation,” this year’s theme for the IDSS-student-run MIT Policy Hackathon, which Sandoval helped to organize as the event chair. The Policy Hackathon is a weekend-long, interdisciplinary competition that brings together participants from around the globe to explore potential solutions to some of society’s greatest challenges. 

      Unlike other competitions of its kind, Sandoval says MIT’s event emphasizes a humanistic approach. “The idea of our hackathon is to promote applications of technology that are humanistic or human-centered,” he says. “We take the opportunity to examine aspects of technology in the spaces where they tend to interact with society and people, an opportunity most technical competitions don’t offer because their primary focus is on the technology.”

      The competition started with 50 teams spread across four challenge categories. This year’s categories included Internet and Cybersecurity, Environmental Justice, Logistics, and Housing and City Planning. While some people come into the challenge with friends, Sandoval said most teams form organically during an online networking meeting hosted by MIT.

      “We encourage people to pair up with others outside of their country and to form teams of different diverse backgrounds and ages,” Sandoval says. “We try to give people who are often not invited to the decision-making table the opportunity to be a policymaker, bringing in those with backgrounds in not only law, policy, or politics, but also medicine, and people who have careers in engineering or experience working in nonprofits.”

      Once an in-person event, the Policy Hackathon has gone through its own regeneration process these past three years, according to Sandoval. After going entirely online during the pandemic’s height, last year they successfully hosted the first hybrid version of the event, which served as their model again this year.

      “The hybrid version of the event gives us the opportunity to allow people to connect in a way that is lost if it is only online, while also keeping the wide range of accessibility, allowing people to join from anywhere in the world, regardless of nationality or income, to provide their input,” Sandoval says.

      For Swetha Tadisina, an undergraduate computer science major at Lafayette College and participant in the internet and cybersecurity category, the hackathon was a unique opportunity to meet and work with people much more advanced in their careers. “I was surprised how such a diverse team that had never met before was able to work so efficiently and creatively,” Tadisina says.

      Erika Spangler, a public high school teacher from Massachusetts and member of the environmental justice category’s winning team, says that while each member of “Team Slime Mold” came to the table with a different set of skills, they managed to be in sync from the start — even working across the nine-and-a-half-hour time difference the four-person team faced when working with policy advocate Shruti Nandy from Calcutta, India.

      “We divided the project into data, policy, and research and trusted each other’s expertise,” Spangler says, “Despite having separate areas of focus, we made sure to have regular check-ins to problem-solve and cross-pollinate ideas.”

      During the 48-hour period, her team proposed the creation of an algorithm to identify high-quality brownfields that could be cleaned up and used as sites for building renewable energy. Their corresponding policy sought to mandate additional requirements for renewable energy businesses seeking tax credits from the Inflation Reduction Act.

      “Their policy memo had the most in-depth technical assessment, including deep dives in a few key cities to show the impact of their proposed approach for site selection at a very granular level,” says Amanda Levin, director of policy analysis for the Natural Resources Defense Council (NRDC). Levin acted as both a judge and challenge provider for the environmental justice category.

      “They also presented their policy recommendations in the memo in a well-thought-out way, clearly noting the relevant actor,” she adds. This clarity around what can be done, and who would be responsible for those actions, is highly valuable for those in policy.”

      Levin says the NRDC, one of the largest environmental nonprofits in the United States, provided five “challenge questions,” making it clear that teams did not need to address all of them. She notes that this gave teams significant leeway, bringing a wide variety of recommendations to the table. 

      “As a challenge partner, the work put together by all the teams is already being used to help inform discussions about the implementation of the Inflation Reduction Act,” Levin says. “Being able to tap into the collective intelligence of the hackathon helped uncover new perspectives and policy solutions that can help make an impact in addressing the important policy challenges we face today.”

      While having partners with experience in data science and policy definitely helped, fellow Team Slime Mold member Sara Sheffels, a PhD candidate in MIT’s biomaterials program, says she was surprised how much her experiences outside of science and policy were relevant to the challenge: “My experience organizing MIT’s Graduate Student Union shaped my ideas about more meaningful community involvement in renewables projects on brownfields. It is not meaningful to merely educate people about the importance of renewables or ask them to sign off on a pre-planned project without addressing their other needs.”

      “I wanted to test my limits, gain exposure, and expand my world,” Tadisina adds. “The exposure, friendships, and experiences you gain in such a short period of time are incredible.”

      For Willy R. Vasquez, an electrical and computer engineering PhD student at the University of Texas, the hackathon is not to be missed. “If you’re interested in the intersection of tech, society, and policy, then this is a must-do experience.” More

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      Exploring emerging topics in artificial intelligence policy

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      Members of the public sector, private sector, and academia convened for the second AI Policy Forum Symposium last month to explore critical directions and questions posed by artificial intelligence in our economies and societies.

      The virtual event, hosted by the AI Policy Forum (AIPF) — an undertaking by the MIT Schwarzman College of Computing to bridge high-level principles of AI policy with the practices and trade-offs of governing — brought together an array of distinguished panelists to delve into four cross-cutting topics: law, auditing, health care, and mobility.

      In the last year there have been substantial changes in the regulatory and policy landscape around AI in several countries — most notably in Europe with the development of the European Union Artificial Intelligence Act, the first attempt by a major regulator to propose a law on artificial intelligence. In the United States, the National AI Initiative Act of 2020, which became law in January 2021, is providing a coordinated program across federal government to accelerate AI research and application for economic prosperity and security gains. Finally, China recently advanced several new regulations of its own.

      Each of these developments represents a different approach to legislating AI, but what makes a good AI law? And when should AI legislation be based on binding rules with penalties versus establishing voluntary guidelines?

      Jonathan Zittrain, professor of international law at Harvard Law School and director of the Berkman Klein Center for Internet and Society, says the self-regulatory approach taken during the expansion of the internet had its limitations with companies struggling to balance their interests with those of their industry and the public.

      “One lesson might be that actually having representative government take an active role early on is a good idea,” he says. “It’s just that they’re challenged by the fact that there appears to be two phases in this environment of regulation. One, too early to tell, and two, too late to do anything about it. In AI I think a lot of people would say we’re still in the ‘too early to tell’ stage but given that there’s no middle zone before it’s too late, it might still call for some regulation.”

      A theme that came up repeatedly throughout the first panel on AI laws — a conversation moderated by Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and chair of the AI Policy Forum — was the notion of trust. “If you told me the truth consistently, I would say you are an honest person. If AI could provide something similar, something that I can say is consistent and is the same, then I would say it’s trusted AI,” says Bitange Ndemo, professor of entrepreneurship at the University of Nairobi and the former permanent secretary of Kenya’s Ministry of Information and Communication.

      Eva Kaili, vice president of the European Parliament, adds that “In Europe, whenever you use something, like any medication, you know that it has been checked. You know you can trust it. You know the controls are there. We have to achieve the same with AI.” Kalli further stresses that building trust in AI systems will not only lead to people using more applications in a safe manner, but that AI itself will reap benefits as greater amounts of data will be generated as a result.

      The rapidly increasing applicability of AI across fields has prompted the need to address both the opportunities and challenges of emerging technologies and the impact they have on social and ethical issues such as privacy, fairness, bias, transparency, and accountability. In health care, for example, new techniques in machine learning have shown enormous promise for improving quality and efficiency, but questions of equity, data access and privacy, safety and reliability, and immunology and global health surveillance remain at large.

      MIT’s Marzyeh Ghassemi, an assistant professor in the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science, and David Sontag, an associate professor of electrical engineering and computer science, collaborated with Ziad Obermeyer, an associate professor of health policy and management at the University of California Berkeley School of Public Health, to organize AIPF Health Wide Reach, a series of sessions to discuss issues of data sharing and privacy in clinical AI. The organizers assembled experts devoted to AI, policy, and health from around the world with the goal of understanding what can be done to decrease barriers to access to high-quality health data to advance more innovative, robust, and inclusive research results while being respectful of patient privacy.

      Over the course of the series, members of the group presented on a topic of expertise and were tasked with proposing concrete policy approaches to the challenge discussed. Drawing on these wide-ranging conversations, participants unveiled their findings during the symposium, covering nonprofit and government success stories and limited access models; upside demonstrations; legal frameworks, regulation, and funding; technical approaches to privacy; and infrastructure and data sharing. The group then discussed some of their recommendations that are summarized in a report that will be released soon.

      One of the findings calls for the need to make more data available for research use. Recommendations that stem from this finding include updating regulations to promote data sharing to enable easier access to safe harbors such as the Health Insurance Portability and Accountability Act (HIPAA) has for de-identification, as well as expanding funding for private health institutions to curate datasets, amongst others. Another finding, to remove barriers to data for researchers, supports a recommendation to decrease obstacles to research and development on federally created health data. “If this is data that should be accessible because it’s funded by some federal entity, we should easily establish the steps that are going to be part of gaining access to that so that it’s a more inclusive and equitable set of research opportunities for all,” says Ghassemi. The group also recommends taking a careful look at the ethical principles that govern data sharing. While there are already many principles proposed around this, Ghassemi says that “obviously you can’t satisfy all levers or buttons at once, but we think that this is a trade-off that’s very important to think through intelligently.”

      In addition to law and health care, other facets of AI policy explored during the event included auditing and monitoring AI systems at scale, and the role AI plays in mobility and the range of technical, business, and policy challenges for autonomous vehicles in particular.

      The AI Policy Forum Symposium was an effort to bring together communities of practice with the shared aim of designing the next chapter of AI. In his closing remarks, Aleksander Madry, the Cadence Designs Systems Professor of Computing at MIT and faculty co-lead of the AI Policy Forum, emphasized the importance of collaboration and the need for different communities to communicate with each other in order to truly make an impact in the AI policy space.

      “The dream here is that we all can meet together — researchers, industry, policymakers, and other stakeholders — and really talk to each other, understand each other’s concerns, and think together about solutions,” Madry said. “This is the mission of the AI Policy Forum and this is what we want to enable.” More

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      in Data Management & Statistics

      The promise and pitfalls of artificial intelligence explored at TEDxMIT event

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      Scientists, students, and community members came together last month to discuss the promise and pitfalls of artificial intelligence at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) for the fourth TEDxMIT event held at MIT. 

      Attendees were entertained and challenged as they explored “the good and bad of computing,” explained CSAIL Director Professor Daniela Rus, who organized the event with John Werner, an MIT fellow and managing director of Link Ventures; MIT sophomore Lucy Zhao; and grad student Jessica Karaguesian. “As you listen to the talks today,” Rus told the audience, “consider how our world is made better by AI, and also our intrinsic responsibilities for ensuring that the technology is deployed for the greater good.”

      Rus mentioned some new capabilities that could be enabled by AI: an automated personal assistant that could monitor your sleep phases and wake you at the optimal time, as well as on-body sensors that monitor everything from your posture to your digestive system. “Intelligent assistance can help empower and augment our lives. But these intriguing possibilities should only be pursued if we can simultaneously resolve the challenges that these technologies bring,” said Rus. 

      The next speaker, CSAIL principal investigator and professor of electrical engineering and computer science Manolis Kellis, started off by suggesting what sounded like an unattainable goal — using AI to “put an end to evolution as we know it.” Looking at it from a computer science perspective, he said, what we call evolution is basically a brute force search. “You’re just exploring all of the search space, creating billions of copies of every one of your programs, and just letting them fight against each other. This is just brutal. And it’s also completely slow. It took us billions of years to get here.” Might it be possible, he asked, to speed up evolution and make it less messy?

      The answer, Kellis said, is that we can do better, and that we’re already doing better: “We’re not killing people like Sparta used to, throwing the weaklings off the mountain. We are truly saving diversity.”

      Knowledge, moreover, is now being widely shared, passed on “horizontally” through accessible information sources, he noted, rather than “vertically,” from parent to offspring. “I would like to argue that competition in the human species has been replaced by collaboration. Despite having a fixed cognitive hardware, we have software upgrades that are enabled by culture, by the 20 years that our children spend in school to fill their brains with everything that humanity has learned, regardless of which family came up with it. This is the secret of our great acceleration” — the fact that human advancement in recent centuries has vastly out-clipped evolution’s sluggish pace.

      The next step, Kellis said, is to harness insights about evolution in order to combat an individual’s genetic susceptibility to disease. “Our current approach is simply insufficient,” he added. “We’re treating manifestations of disease, not the causes of disease.” A key element in his lab’s ambitious strategy to transform medicine is to identify “the causal pathways through which genetic predisposition manifests. It’s only by understanding these pathways that we can truly manipulate disease causation and reverse the disease circuitry.” 

      Kellis was followed by Aleksander Madry, MIT professor of electrical engineering and computer science and CSAIL principal investigator, who told the crowd, “progress in AI is happening, and it’s happening fast.” Computer programs can routinely beat humans in games like chess, poker, and Go. So should we be worried about AI surpassing humans? 

      Madry, for one, is not afraid — or at least not yet. And some of that reassurance stems from research that has led him to the following conclusion: Despite its considerable success, AI, especially in the form of machine learning, is lazy. “Think about being lazy as this kind of smart student who doesn’t really want to study for an exam. Instead, what he does is just study all the past years’ exams and just look for patterns. Instead of trying to actually learn, he just tries to pass the test. And this is exactly the same way in which current AI is lazy.”

      A machine-learning model might recognize grazing sheep, for instance, simply by picking out pictures that have green grass in them. If a model is trained to identify fish from photos of anglers proudly displaying their catches, Madry explained, “the model figures out that if there’s a human holding something in the picture, I will just classify it as a fish.” The consequences can be more serious for an AI model intended to pick out malignant tumors. If the model is trained on images containing rulers that indicate the size of tumors, the model may end up selecting only those photos that have rulers in them.

      This leads to Madry’s biggest concerns about AI in its present form. “AI is beating us now,” he noted. “But the way it does it [involves] a little bit of cheating.” He fears that we will apply AI “in some way in which this mismatch between what the model actually does versus what we think it does will have some catastrophic consequences.” People relying on AI, especially in potentially life-or-death situations, need to be much more mindful of its current limitations, Madry cautioned.

      There were 10 speakers altogether, and the last to take the stage was MIT associate professor of electrical engineering and computer science and CSAIL principal investigator Marzyeh Ghassemi, who laid out her vision for how AI could best contribute to general health and well-being. But in order for that to happen, its models must be trained on accurate, diverse, and unbiased medical data.

      It’s important to focus on the data, Ghassemi stressed, because these models are learning from us. “Since our data is human-generated … a neural network is learning how to practice from a doctor. But doctors are human, and humans make mistakes. And if a human makes a mistake, and we train an AI from that, the AI will, too. Garbage in, garbage out. But it’s not like the garbage is distributed equally.”

      She pointed out that many subgroups receive worse care from medical practitioners, and members of these subgroups die from certain conditions at disproportionately high rates. This is an area, Ghassemi said, “where AI can actually help. This is something we can fix.” Her group is developing machine-learning models that are robust, private, and fair. What’s holding them back is neither algorithms nor GPUs. It’s data. Once we collect reliable data from diverse sources, Ghassemi added, we might start reaping the benefits that AI can bring to the realm of health care.

      In addition to CSAIL speakers, there were talks from members across MIT’s Institute for Data, Systems, and Society; the MIT Mobility Initiative; the MIT Media Lab; and the SENSEable City Lab.

      The proceedings concluded on that hopeful note. Rus and Werner then thanked everyone for coming. “Please continue to reflect about the good and bad of computing,” Rus urged. “And we look forward to seeing you back here in May for the next TEDxMIT event.”

      The exact theme of the spring 2022 gathering will have something to do with “superpowers.” But — if December’s mind-bending presentations were any indication — the May offering is almost certain to give its attendees plenty to think about. And maybe provide the inspiration for a startup or two. More

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      in Data Management & Statistics

      Lincoln Laboratory convenes top network scientists for Graph Exploitation Symposium

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      As the Covid-19 pandemic has shown, we live in a richly connected world, facilitating not only the efficient spread of a virus but also of information and influence. What can we learn by analyzing these connections? This is a core question of network science, a field of research that models interactions across physical, biological, social, and information systems to solve problems.

      The 2021 Graph Exploitation Symposium (GraphEx), hosted by MIT Lincoln Laboratory, brought together top network science researchers to share the latest advances and applications in the field.

      “We explore and identify how exploitation of graph data can offer key technology enablers to solve the most pressing problems our nation faces today,” says Edward Kao, a symposium organizer and technical staff in Lincoln Laboratory’s AI Software Architectures and Algorithms Group.

      The themes of the virtual event revolved around some of the year’s most relevant issues, such as analyzing disinformation on social media, modeling the pandemic’s spread, and using graph-based machine learning models to speed drug design.

      “The special sessions on influence operations and Covid-19 at GraphEx reflect the relevance of network and graph-based analysis for understanding the phenomenology of these complicated and impactful aspects of modern-day life, and also may suggest paths forward as we learn more and more about graph manipulation,” says William Streilein, who co-chaired the event with Rajmonda Caceres, both of Lincoln Laboratory.

      Social networks

      Several presentations at the symposium focused on the role of network science in analyzing influence operations (IO), or organized attempts by state and/or non-state actors to spread disinformation narratives.  

      Lincoln Laboratory researchers have been developing tools to classify and quantify the influence of social media accounts that are likely IO accounts, such as those willfully spreading false Covid-19 treatments to vulnerable populations.

      “A cluster of IO accounts acts as an echo chamber to amplify the narrative. The vulnerable population is then engaging in these narratives,” says Erika Mackin, a researcher developing the tool, called RIO or Reconnaissance of Influence Operations.

      To classify IO accounts, Mackin and her team trained an algorithm to detect probable IO accounts in Twitter networks based on a specific hashtag or narrative. One example they studied was #MacronLeaks, a disinformation campaign targeting Emmanuel Macron during the 2017 French presidential election. The algorithm is trained to label accounts within this network as being IO on the basis of several factors, such as the number of interactions with foreign news accounts, the number of links tweeted, or number of languages used. Their model then uses a statistical approach to score an account’s level of influence in spreading the narrative within that network.

      The team has found that their classifier outperforms existing detectors of IO accounts, because it can identify both bot accounts and human-operated ones. They’ve also discovered that IO accounts that pushed the 2017 French election disinformation narrative largely overlap with accounts influentially spreading Covid-19 pandemic disinformation today. “This suggests that these accounts will continue to transition to disinformation narratives,” Mackin says.

      Pandemic modeling

      Throughout the Covid-19 pandemic, leaders have been looking to epidemiological models, which predict how disease will spread, to make sound decisions. Alessandro Vespignani, director of the Network Science Institute at Northeastern University, has been leading Covid-19 modeling efforts in the United States, and shared a keynote on this work at the symposium.

      Besides taking into account the biological facts of the disease, such as its incubation period, Vespignani’s model is especially powerful in its inclusion of community behavior. To run realistic simulations of disease spread, he develops “synthetic populations” that are built by using publicly available, highly detailed datasets about U.S. households. “We create a population that is not real, but is statistically real, and generate a map of the interactions of those individuals,” he says. This information feeds back into the model to predict the spread of the disease. 

      Today, Vespignani is considering how to integrate genomic analysis of the virus into this kind of population modeling in order to understand how variants are spreading. “It’s still a work in progress that is extremely interesting,” he says, adding that this approach has been useful in modeling the dispersal of the Delta variant of SARS-CoV-2. 

      As researchers model the virus’ spread, Lucas Laird at Lincoln Laboratory is considering how network science can be used to design effective control strategies. He and his team are developing a model for customizing strategies for different geographic regions. The effort was spurred by the differences in Covid-19 spread across U.S. communities, and what the researchers found to be a gap in intervention modeling to address those differences.

      As examples, they applied their planning algorithm to three counties in Florida, Massachusetts, and California. Taking into account the characteristics of a specific geographic center, such as the number of susceptible individuals and number of infections there, their planner institutes different strategies in those communities throughout the outbreak duration.

      “Our approach eradicates disease in 100 days, but it also is able to do it with much more targeted interventions than any of the global interventions. In other words, you don’t have to shut down a full country.” Laird adds that their planner offers a “sandbox environment” for exploring intervention strategies in the future.

      Machine learning with graphs

      Graph-based machine learning is receiving increasing attention for its potential to “learn” the complex relationships between graphical data, and thus extract new insights or predictions about these relationships. This interest has given rise to a new class of algorithms called graph neural networks. Today, graph neural networks are being applied in areas such as drug discovery and material design, with promising results.

      “We can now apply deep learning much more broadly, not only to medical images and biological sequences. This creates new opportunities in data-rich biology and medicine,” says Marinka Zitnik, an assistant professor at Harvard University who presented her research at GraphEx.

      Zitnik’s research focuses on the rich networks of interactions between proteins, drugs, disease, and patients, at the scale of billions of interactions. One application of this research is discovering drugs to treat diseases with no or few approved drug treatments, such as for Covid-19. In April, Zitnik’s team published a paper on their research that used graph neural networks to rank 6,340 drugs for their expected efficacy against SARS-CoV-2, identifying four that could be repurposed to treat Covid-19.

      At Lincoln Laboratory, researchers are similarly applying graph neural networks to the challenge of designing advanced materials, such as those that can withstand extreme radiation or capture carbon dioxide. Like the process of designing drugs, the trial-and-error approach to materials design is time-consuming and costly. The laboratory’s team is developing graph neural networks that can learn relationships between a material’s crystalline structure and its properties. This network can then be used to predict a variety of properties from any new crystal structure, greatly speeding up the process of screening materials with desired properties for specific applications.

      “Graph representation learning has emerged as a rich and thriving research area for incorporating inductive bias and structured priors during the machine learning process, with broad applications such as drug design, accelerated scientific discovery, and personalized recommendation systems,” Caceres says. 

      A vibrant community

      Lincoln Laboratory has hosted the GraphEx Symposium annually since 2010, with the exception of last year’s cancellation due to Covid-19. “One key takeaway is that despite the postponement from last year and the need to be virtual, the GraphEx community is as vibrant and active as it’s ever been,” Streilein says. “Network-based analysis continues to expand its reach and is applied to ever-more important areas of science, society, and defense with increasing impact.”

      In addition to those from Lincoln Laboratory, technical committee members and co-chairs of the GraphEx Symposium included researchers from Harvard University, Arizona State University, Stanford University, Smith College, Duke University, the U.S. Department of Defense, and Sandia National Laboratories. More