Posts in category: MIT

The health care system today largely focuses on helping people after they have problems. When they do receive treatment, it’s based on what has worked best on average across a huge, diverse group of patients.

Now the company Health at Scale is making health care more proactive and personalized — and, true to its name, it’s doing so for millions of people.

Health at Scale uses a new approach for making care recommendations based on new classes of machine-learning models that work even when only small amounts of data on individual patients, providers, and treatments are available.

The company is already working with health plans, insurers, and employers to match patients with doctors. It’s also helping to identify people at rising risk of visiting the emergency department or being hospitalized in the future, and to predict the progression of chronic diseases. Recently, Health at Scale showed its models can identify people at risk of severe respiratory infections like influenza or pneumonia, or, potentially, Covid-19.

“From the beginning, we decided all of our predictions would be related to achieving better outcomes for patients,” says John Guttag, chief technology officer of Health at Scale and the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT. “We’re trying to predict what treatment or physician or intervention would lead to better outcomes for people.”

A new approach to improving health

Health at Scale co-founder and CEO Zeeshan Syed met Guttag while studying electrical engineering and computer science at MIT. Guttag served as Syed’s advisor for his bachelor’s and master’s degrees. When Syed decided to pursue his PhD, he only applied to one school, and his advisor was easy to choose.

Syed did his PhD through the Harvard-MIT Program in Health Sciences and Technology (HST). During that time, he looked at how patients who’d had heart attacks could be better managed. The work was personal for Syed: His father had recently suffered a serious heart attack.

Through the work, Syed met Mohammed Saeed SM ’97, PhD ’07, who was also in the HST program. Syed, Guttag, and Saeed founded Health at Scale in 2015 along with  David Guttag ’05, focusing on using core advances in machine learning to solve some of health care’s hardest problems.

“It started with the burning itch to address real challenges in health care about personalization and prediction,” Syed says.

From the beginning, the founders knew their solutions needed to work with widely available data like health care claims, which include information on diagnoses, tests, prescriptions, and more. They also sought to build tools for cleaning up and processing raw data sets, so that their models would be part of what Guttag refers to as a “full machine-learning stack for health care.”

Finally, to deliver effective, personalized solutions, the founders knew their models needed to work with small numbers of encounters for individual physicians, clinics, and patients, which posed severe challenges for conventional AI and machine learning.

“The large companies getting into [the health care AI] space had it wrong in that they viewed it as a big data problem,” Guttag says. “They thought, ‘We’re the experts. No one’s better at crunching large amounts of data than us.’ We thought if you want to make the right decision for individuals, the problem was a small data problem: Each patient is different, and we didn’t want to recommend to patients what was best on average. We wanted what was best for each individual.”

The company’s first models helped recommend skilled nursing facilities for post-acute care patients. Many such patients experience further health problems and return to the hospital. Health at Scale’s models showed that some facilities were better at helping specific kinds of people with specific health problems. For example, a 64-year-old man with a history of cardiovascular disease may fare better at one facility compared to another.

Today the company’s recommendations help guide patients to the primary care physicians, surgeons, and specialists that are best suited for them. Guttag even used the service when he got his hip replaced last year.

Health at Scale also helps organizations identify people at rising risk of specific adverse health events, like heart attacks, in the future.

“We’ve gone beyond the notion of identifying people who have frequently visited emergency departments or hospitals in the past, to get to the much more actionable problem of finding those people at an inflection point, where they are likely to experience worse outcomes and higher costs,” Syed says.

The company’s other solutions help determine the best treatment options for patients and help reduce health care fraud, waste, and abuse. Each use case is designed to improve patient health outcomes by giving health care organizations decision-support for action.

“Broadly speaking, we are interested in building models that can be used to help avoid problems, rather than simply predict them,” says Guttag. “For example, identifying those individuals at highest risk for serious complications of a respiratory infection [enables care providers] to target them for interventions that reduce their chance of developing such an infection.”

Impact at scale

Earlier this year, as the scope of the Covid-19 pandemic was becoming clear, Health at Scale began considering ways its models could help.

“The lack of data in the beginning of the pandemic motivated us to look at the experiences we have gained from combatting other respiratory infections like influenza and pneumonia,” says Saeed, who serves as Health at Scale’s chief medical officer.

The idea led to a peer-reviewed paper where researchers affiliated with the company, the University of Michigan, and MIT showed Health at Scale’s models could accurately predict hospitalizations and visits to the emergency department related to respiratory infections.

“We did the work on the paper using the tech we’d already built,” Guttag says. “We had interception products deployed for predicting patients at-risk of emergent hospitalizations for a variety of causes, and we saw that we could extend that approach. We had customers that we gave the solution to for free.”

The paper proved out another use case for a technology that is already being used by some of the largest health plans in the U.S. That’s an impressive customer base for a five-year-old company of only 20 people — about half of which have MIT affiliations.

“The culture MIT creates to solve problems that are worth solving, to go after impact, I think that’s been reflected in the way the company got together and has operated,” Syed says. “I’m deeply proud that we’ve maintained that MIT spirit.”

And, Syed believes, there’s much more to come.

“We set out with the goal of driving impact,” Syed says. “We currently run some of the largest production deployments of machine learning at scale, affecting millions, if not tens of millions, of patients, and we  are only just getting started.”

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Nearly every aspect of the modern world is being transformed by computing. As computing technology continues to revolutionize the way people live, work, learn, and interact, computing research and education are increasingly playing a role in a broad range of academic disciplines, and are in turn being shaped by this expanding breadth.

To connect computing and other disciplines in addressing critical challenges and opportunities facing the world today, the MIT Stephen A. Schwarzman College of Computing is planning to create 25 new faculty positions that will be shared between the college and an MIT department or school. Hiring for these new positions will be focused on six strategic areas of inquiry, to build capacity at MIT in key computing domains that cut across departments and schools. The shared faculty members are expected to engage in research and teaching that contributes to their home department, that is of mutual value to that department and the college, and that helps form and strengthen cross-departmental ties.

“These new shared faculty positions present an unprecedented opportunity to develop crucial areas at MIT which connect computing with other disciplines,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing. “By coordinated hiring between the college and departments and schools, we expect to have significant impact with multiple touch points across MIT.”

The six strategic areas and the schools expected to be involved in hiring for each are as follows:

Social, Economic, and Ethical Implications of Computing and Networks. Associated schools: School of Humanities, Arts and the Social Sciences and MIT Sloan School of Management.

There have been tremendous advances in new digital platforms and algorithms, which have already transformed our economic, social, and even political lives. But the future societal implications of these technologies and the consequences of the use and misuse of massive social data are poorly understood. There are exciting opportunities for building on the growing intellectual connections between computer science, data science, and social science and humanities, in order to bring a better conceptual framework to understand the social and economic implications, ethical dimensions, and regulation of these technologies.

Focusing on the interplay between computing systems and our understanding of individuals and societal institutions, this strategic hiring area will include faculty whose work focuses on the broader consequences of the changing digital and information environment, market design, digital commerce and competition, and economic and social networks. Issues of interest include how computing and AI technologies have shaped and are shaping the work of the future; how social media tools have reshaped political campaigns, changed the nature and organization of mass protests, and spurred governments to either reduce or dramatically enhance censorship and social control; increasing challenges in adjudicating what information is reliable, what is slanted, and what is entirely fake; conceptions of privacy, fairness, and transparency of algorithms; and the effects of new technologies on democratic governance.

Computing and Natural Intelligence: Cognition, Perception, and Language. Associated schools: School of Science; School of Humanities, Arts, and Social Sciences; and School of Architecture and Planning.

Intelligence — what it is, how the brain produces it, and how it can be engineered — is simultaneously one of the greatest open questions in natural sciences and the most important engineering challenges of our time. Significant advances in computing and machine learning have enabled a better understanding of the brain and the mind. Concurrently, neuroscience and cognitive science have started to give meaningful engineering guidance to AI and related computing efforts. Yet, huge gaps remain in connecting the science and engineering of intelligence.

Integrating science, computing, and social sciences and humanities, this strategic hiring area aims to address the gap between science and engineering of intelligence, in order to make transformative advances in AI and deepen our understanding of natural intelligence. Hiring in this area is expected to advance a holistic approach to understanding human perception and cognition through work such as the study of computational properties of language by bridging linguistic theory, cognitive science, and computer science; improving the art of listening by re-engineering music through music classification and machine learning, music cognition, and new interfaces for musical expression; discovering how artificial systems might help explain natural intelligence and vice versa; and seeking ways in which computing can aid in human expression, communications, health, and meaning.

Computing in Health and Life Sciences. Associated schools: School of Engineering; School of Science; and MIT Sloan School of Management.

Computing is increasingly becoming an indispensable tool in the health and life sciences. A key area is facilitating new approaches to identifying molecular and biomolecular agents with desired functions and for discovering new medications and new means of diagnosis. For instance, machine learning provides a unique opportunity in the pursuit of molecular and biomolecular discovery to parameterize and augment physics-based models, or possibly even replace them, and enable a revolution in molecular science and engineering. Another major area is health-care delivery, where novel algorithms, high performance computing, and machine learning offer new possibilities to transform health monitoring and treatment planning, facilitating better patient care, and enabling more effective ways to help prevent disease. In diagnosis, machine learning methods hold the promise of improved detection of diseases, increasing both specificity and sensitivity of imaging and testing.

This strategic area aims to hire faculty who help create transformative new computational methods in health and life sciences, while complementing the considerable existing work at MIT by forging additional connections. The broad scope ranges from computational approaches to fundamental problems in molecular design and synthesis for human health; to reshaping health-care delivery and personalized medicine; to understanding radiation effects and optimizing dose delivery on target cells; to improving tracing, imaging, and diagnosis techniques.

Computing for Health of the Planet. Associated schools: School of Engineering; School of Science; and School of Architecture and Planning.

The health of the planet is one of the most important challenges facing humankind today. Rapid industrialization has led to a number of serious threats to human and ecosystem health, including climate change, unsafe levels of air and water pollution, coastal and agricultural land erosion, and many others. Ensuring the health and safety of our planet necessitates an interdisciplinary approach that connects scientific understanding, engineering solutions, social, economic, and political aspects, with new computational methods to provide data-driven models and solutions for providing clean air, usable water, resilient food, efficient transportation systems, and identifying sustainable sources of energy.

This strategic hiring area will help facilitate such collaborations by bringing together expertise that will enable us to advance physical understanding of low-carbon energy solutions, earth-climate modelling, and urban planning through high performance computing, transformational numerical methods, and/or machine learning techniques.

Computing and Human Experience. Associated schools: School of Humanities, Arts, and Social Sciences and School of Architecture and Planning.

Computing and digital technologies are challenging the very ways in which people understand reality and our role in it. These technologies are embedded in the everyday lives of people around the world, and while frequently highly useful, they can reflect cultural assumptions and technological heritage, even though they are often viewed as being neutral prescriptions for structuring the world. Indeed, as becomes increasingly apparent, these technologies are able to alter individual and societal perceptions and actions, or affect societal institutions, in ways that are not broadly understood or intended. Moreover, although these technologies are conventionally developed for improved efficacy or efficiency, they can also provide opportunities for less utilitarian purposes such as supporting introspection and personal reflection.

This strategic hiring area focuses on growing the set of scholars in the social sciences, humanities, and computing who examine technology designs, systems, policies, and practices that can address the dual challenges of the lack of understanding of these technologies and their implications, including the design of systems that may help ameliorate rather than exacerbate inequalities. It further aims to develop techniques and systems that help people interpret and gain understanding from societal and historical data, including in humanities disciplines such as comparative literature, history, and art and architectural history.

Quantum Computing. Associated schools: School of Engineering and School of Science.

One of the most promising directions for continuing improvements in computing power comes from quantum mechanics. In the coming years, new hardware, algorithms, and discoveries offer the potential to dramatically increase the power of quantum computers far beyond current machines. Achieving these advances poses challenges that span multiple scientific and engineering fields, and from quantum hardware to quantum computing algorithms. Potential quantum computing applications span a broad range of fields, including chemistry, biology, materials science, atmospheric modeling, urban system simulation, nuclear engineering, finance, optimization, and others, requiring a deep understanding of both quantum computing algorithms and the problem space.

This strategic hiring area aims to build on MIT’s rich set of activities in the space to catalyze research and education in quantum computing and quantum information across the Institute, including the study of quantum materials; developing robust controllable quantum devices and networks that can faithfully transmit quantum information; and new algorithms for machine learning, AI, optimization, and data processing to fully leverage the promise of quantum computing.

A coordinated approach

Over the past few months, the MIT Schwarzman College of Computing has undertaken a strategic planning exercise to identify key areas for hiring the new shared faculty. The process has been led by Huttenlocher, together with MIT Provost Martin Schmidt and the deans of the five schools — Anantha Chandrakasan, dean of the School of Engineering; Melissa Nobles, Kenan Sahin Dean of the School of Humanities, Arts, and Social Sciences; Hashim Sarkis, dean of the School of Architecture and Planning; David Schmittlein, John C. Head III Dean of MIT Sloan; and Michael Sipser, dean of the School of Science — beginning with input from departments across the Institute.

This input was in the form of proposals for interdisciplinary computing areas that were solicited from department heads. A total of 29 proposals were received. Over a six-week period, the committee worked with proposing departments to identify strategic hiring themes. The process yielded the six areas that cover several critically important directions. 

“These areas not only bring together computing with numerous departments and schools, but also involve multiple modes of academic inquiry, offering opportunities for new collaborations in research and teaching across a broad range of fields,” says Schmidt. “I’m excited to see us launch this critical part of the college’s mission.”

The college will also coordinate with each of the five schools to ensure that diversity, equity, and inclusion is at the forefront for all of the hiring areas.

Hiring for the 2020-21 academic year

While the number of searches and involved schools will vary from year to year, the plan for the coming academic year is to have five searches, one with each school. These searches will be in three of the six strategic hiring areas as follows:

Social, Economic, and Ethical Implications of Computing and Networks will focus on two searches, one with the Department of Philosophy in the School of Humanities, Arts, and Social Sciences, and one with the MIT Sloan School of Management.

Computing and Natural Intelligence: Cognition, Perception and Language will focus on one search with the Department of Brain and Cognitive Sciences in the School of Science.

Computing for Health of the Planet will focus on two searches, one with the Department of Urban Studies and Planning in the School of Architecture and Planning, and one with a department to be identified in the School of Engineering.

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The ability to reason abstractly about events as they unfold is a defining feature of human intelligence. We know instinctively that crying and writing are means of communicating, and that a panda falling from a tree and a plane landing are variations on descending. 

Organizing the world into abstract categories does not come easily to computers, but in recent years researchers have inched closer by training machine learning models on words and images infused with structural information about the world, and how objects, animals, and actions relate. In a new study at the European Conference on Computer Vision this month, researchers unveiled a hybrid language-vision model that can compare and contrast a set of dynamic events captured on video to tease out the high-level concepts connecting them. 

Their model did as well as or better than humans at two types of visual reasoning tasks — picking the video that conceptually best completes the set, and picking the video that doesn’t fit. Shown videos of a dog barking and a man howling beside his dog, for example, the model completed the set by picking the crying baby from a set of five videos. Researchers replicated their results on two datasets for training AI systems in action recognition: MIT’s Multi-Moments in Time and DeepMind’s Kinetics.

“We show that you can build abstraction into an AI system to perform ordinary visual reasoning tasks close to a human level,” says the study’s senior author Aude Oliva, a senior research scientist at MIT, co-director of the MIT Quest for Intelligence, and MIT director of the MIT-IBM Watson AI Lab. “A model that can recognize abstract events will give more accurate, logical predictions and be more useful for decision-making.”

As deep neural networks become expert at recognizing objects and actions in photos and video, researchers have set their sights on the next milestone: abstraction, and training models to reason about what they see. In one approach, researchers have merged the pattern-matching power of deep nets with the logic of symbolic programs to teach a model to interpret complex object relationships in a scene. Here, in another approach, researchers capitalize on the relationships embedded in the meanings of words to give their model visual reasoning power.

“Language representations allow us to integrate contextual information learned from text databases into our visual models,” says study co-author Mathew Monfort, a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “Words like ‘running,’ ‘lifting,’ and ‘boxing’ share some common characteristics that make them more closely related to the concept ‘exercising,’ for example, than ‘driving.’ ”

Using WordNet, a database of word meanings, the researchers mapped the relation of each action-class label in Moments and Kinetics to the other labels in both datasets. Words like “sculpting,” “carving,” and “cutting,” for example, were connected to higher-level concepts like “crafting,” “making art,” and “cooking.” Now when the model recognizes an activity like sculpting, it can pick out conceptually similar activities in the dataset. 

This relational graph of abstract classes is used to train the model to perform two basic tasks. Given a set of videos, the model creates a numerical representation for each video that aligns with the word representations of the actions shown in the video. An abstraction module then combines the representations generated for each video in the set to create a new set representation that is used to identify the abstraction shared by all the videos in the set.

To see how the model would do compared to humans, the researchers asked human subjects to perform the same set of visual reasoning tasks online. To their surprise, the model performed as well as humans in many scenarios, sometimes with unexpected results. In a variation on the set completion task, after watching a video of someone wrapping a gift and covering an item in tape, the model suggested a video of someone at the beach burying someone else in the sand. 

“It’s effectively ‘covering,’ but very different from the visual features of the other clips,” says Camilo Fosco, a PhD student at MIT who is co-first author of the study with PhD student Alex Andonian. “Conceptually it fits, but I had to think about it.”

Limitations of the model include a tendency to overemphasize some features. In one case, it suggested completing a set of sports videos with a video of a baby and a ball, apparently associating balls with exercise and competition.

A deep learning model that can be trained to “think” more abstractly may be capable of learning with fewer data, say researchers. Abstraction also paves the way toward higher-level, more human-like reasoning.

“One hallmark of human cognition is our ability to describe something in relation to something else — to compare and to contrast,” says Oliva. “It’s a rich and efficient way to learn that could eventually lead to machine learning models that can understand analogies and are that much closer to communicating intelligently with us.”

Other authors of the study are Allen Lee from MIT, Rogerio Feris from IBM, and Carl Vondrick from Columbia University.

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The research described in this article has been published on a preprint server but has not yet been peer-reviewed by scientific or medical experts.

During the current coronavirus pandemic, one of the riskiest parts of a health care worker’s job is assessing people who have symptoms of Covid-19. Researchers from MIT and Brigham and Women’s Hospital hope to reduce that risk by using robots to remotely measure patients’ vital signs.

The robots, which are controlled by a handheld device, can also carry a tablet that allows doctors to ask patients about their symptoms without being in the same room.

“In robotics, one of our goals is to use automation and robotic technology to remove people from dangerous jobs,” says Henwei Huang, an MIT postdoc. “We thought it should be possible for us to use a robot to remove the health care worker from the risk of directly exposing themselves to the patient.”

Using four cameras mounted on a dog-like robot developed by Boston Dynamics, the researchers have shown that they can measure skin temperature, breathing rate, pulse rate, and blood oxygen saturation in healthy patients, from a distance of 2 meters. They are now making plans to test it in patients with Covid-19 symptoms.

“We are thrilled to have forged this industry-academia partnership in which scientists with engineering and robotics expertise worked with clinical teams at the hospital to bring sophisticated technologies to the bedside,” says Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women’s Hospital, and the senior author of the study.

The researchers have posted a paper on their system on the preprint server techRxiv, and have submitted it to a peer-reviewed journal. Huang is one of the lead authors of the study, along with Peter Chai, an assistant professor of emergency medicine at Brigham and Women’s Hospital, and Claas Ehmke, a visiting scholar from ETH Zurich.

Measuring vital signs

When Covid-19 cases began surging in Boston in March, many hospitals, including Brigham and Women’s, set up triage tents outside their emergency departments to evaluate people with Covid-19 symptoms. One major component of this initial evaluation is measuring vital signs, including body temperature.

The MIT and BWH researchers came up with the idea to use robotics to enable contactless monitoring of vital signs, to allow health care workers to minimize their exposure to potentially infectious patients. They decided to use existing computer vision technologies that can measure temperature, breathing rate, pulse, and blood oxygen saturation, and worked to make them mobile.

To achieve that, they used a robot known as Spot, which can walk on four legs, similarly to a dog. Health care workers can maneuver the robot to wherever patients are sitting, using a handheld controller. The researchers mounted four different cameras onto the robot — an infrared camera plus three monochrome cameras that filter different wavelengths of light.

The researchers developed algorithms that allow them to use the infrared camera to measure both elevated skin temperature and breathing rate. For body temperature, the camera measures skin temperature on the face, and the algorithm correlates that temperature with core body temperature. The algorithm also takes into account the ambient temperature and the distance between the camera and the patient, so that measurements can be taken from different distances, under different weather conditions, and still be accurate.

Measurements from the infrared camera can also be used to calculate the patient’s breathing rate. As the patient breathes in and out, wearing a mask, their breath changes the temperature of the mask. Measuring this temperature change allows the researchers to calculate how rapidly the patient is breathing.

The three monochrome cameras each filter a different wavelength of light — 670, 810, and 880 nanometers. These wavelengths allow the researchers to measure the slight color changes that result when hemoglobin in blood cells binds to oxygen and flows through blood vessels. The researchers’ algorithm uses these measurements to calculate both pulse rate and blood oxygen saturation.

“We didn’t really develop new technology to do the measurements,” Huang says. “What we did is integrate them together very specifically for the Covid application, to analyze different vital signs at the same time.”

Continuous monitoring

In this study, the researchers performed the measurements on healthy volunteers, and they are now making plans to test their robotic approach in people who are showing symptoms of Covid-19, in a hospital emergency department.

While in the near term, the researchers plan to focus on triage applications, in the longer term, they envision that the robots could be deployed in patients’ hospital rooms. This would allow the robots to continuously monitor patients and also allow doctors to check on them, via tablet, without having to enter the room. Both applications would require approval from the U.S. Food and Drug Administration.

The research was funded by the MIT Department of Mechanical Engineering and the Karl van Tassel (1925) Career Development Professorship.

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The U.S. National Science Foundation (NSF) announced today an investment of more than $100 million to establish five artificial intelligence (AI) institutes, each receiving roughly $20 million over five years. One of these, the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), will be led by MIT’s Laboratory for Nuclear Science (LNS) and become the intellectual home of more than 25 physics and AI senior researchers at MIT and Harvard, Northeastern, and Tufts universities. 

By merging research in physics and AI, the IAIFI seeks to tackle some of the most challenging problems in physics, including precision calculations of the structure of matter, gravitational-wave detection of merging black holes, and the extraction of new physical laws from noisy data.

“The goal of the IAIFI is to develop the next generation of AI technologies, based on the transformative idea that artificial intelligence can directly incorporate physics intelligence,” says Jesse Thaler, an associate professor of physics at MIT, LNS researcher, and IAIFI director.  “By fusing the ‘deep learning’ revolution with the time-tested strategies of ‘deep thinking’ in physics, we aim to gain a deeper understanding of our universe and of the principles underlying intelligence.”

IAIFI researchers say their approach will enable making groundbreaking physics discoveries, and advance AI more generally, through the development of novel AI approaches that incorporate first principles from fundamental physics.  

“Invoking the simple principle of translational symmetry — which in nature gives rise to conservation of momentum — led to dramatic improvements in image recognition,” says Mike Williams, an associate professor of physics at MIT, LNS researcher, and IAIFI deputy director. “We believe incorporating more complex physics principles will revolutionize how AI is used to study fundamental interactions, while simultaneously advancing the foundations of AI.”

In addition, a core element of the IAIFI mission is to transfer their technologies to the broader AI community.

“Recognizing the critical role of AI, NSF is investing in collaborative research and education hubs, such as the NSF IAIFI anchored at MIT, which will bring together academia, industry, and government to unearth profound discoveries and develop new capabilities,” says NSF Director Sethuraman Panchanathan. “Just as prior NSF investments enabled the breakthroughs that have given rise to today’s AI revolution, the awards being announced today will drive discovery and innovation that will sustain American leadership and competitiveness in AI for decades to come.”

Research in AI and fundamental interactions

Fundamental interactions are described by two pillars of modern physics: at short distances by the Standard Model of particle physics, and at long distances by the Lambda Cold Dark Matter model of Big Bang cosmology. Both models are based on physical first principles such as causality and space-time symmetries.  An abundance of experimental evidence supports these theories, but also exposes where they are incomplete, most pressingly that the Standard Model does not explain the nature of dark matter, which plays an essential role in cosmology.

AI has the potential to help answer these questions and others in physics.

For many physics problems, the governing equations that encode the fundamental physical laws are known. However, undertaking key calculations within these frameworks, as is essential to test our understanding of the universe and guide physics discovery, can be computationally demanding or even intractable. IAIFI researchers are developing AI for such first-principles theory studies, which naturally require AI approaches that rigorously encode physics knowledge. 

“My group is developing new provably exact algorithms for theoretical nuclear physics,” says Phiala Shanahan, an assistant professor of physics and LNS researcher at MIT. “Our first-principles approach turns out to have applications in other areas of science and even in robotics, leading to exciting collaborations with industry partners.”

Incorporating physics principles into AI could also have a major impact on many experimental applications, such as designing AI methods that are more easily verifiable. IAIFI researchers are working to enhance the scientific potential of various facilities, including the Large Hadron Collider (LHC) and the Laser Interferometer Gravity Wave Observatory (LIGO). 

“Gravitational-wave detectors are among the most sensitive instruments on Earth, but the computational systems used to operate them are mostly based on technology from the previous century,” says Principal Research Scientist Lisa Barsotti of the MIT Kavli Institute for Astrophysics and Space Research. “We have only begun to scratch the surface of what can be done with AI; just enough to see that the IAIFI will be a game-changer.”

The unique features of these physics applications also offer compelling research opportunities in AI more broadly. For example, physics-informed architectures and hardware development could lead to advances in the speed of AI algorithms, and work in statistical physics is providing a theoretical foundation for understanding AI dynamics. 

“Physics has inspired many time-tested ideas in machine learning: maximizing entropy, Boltzmann machines, and variational inference, to name a few,” says Pulkit Agrawal, an assistant professor of electrical engineering and computer science at MIT, and researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “We believe that close interaction between physics and AI researchers will be the catalyst that leads to the next generation of machine learning algorithms.” 

Cultivating early-career talent

AI technologies are advancing rapidly, making it both important and challenging to train junior researchers at the intersection of physics and AI. The IAIFI aims to recruit and train a talented and diverse group of early-career researchers, including at the postdoc level through its IAIFI Fellows Program.  

“By offering our fellows their choice of research problems, and the chance to focus on cutting-edge challenges in physics and AI, we will prepare many talented young scientists to become future leaders in both academia and industry,” says MIT professor of physics Marin Soljacic of the Research Laboratory of Electronics (RLE). 

IAIFI researchers hope these fellows will spark interdisciplinary and multi-investigator collaborations, generate new ideas and approaches, translate physics challenges beyond their native domains, and help develop a common language across disciplines. Applications for the inaugural IAIFI fellows are due in mid-October. 

Another related effort spearheaded by Thaler, Williams, and Alexander Rakhlin, an associate professor of brain and cognitive science at MIT and researcher in the Institute for Data, Systems, and Society (IDSS), is the development of a new interdisciplinary PhD program in physics, statistics, and data science, a collaborative effort between the Department of Physics and the Statistics and Data Science Center.

“Statistics and data science are among the foundational pillars of AI. Physics joining the interdisciplinary doctoral program will bring forth new ideas and areas of exploration, while fostering a new generation of leaders at the intersection of physics, statistics, and AI,” says Rakhlin.  

Education, outreach, and partnerships 

The IAIFI aims to cultivate “human intelligence” by promoting education and outreach. For example, IAIFI members will contribute to establishing a MicroMasters degree program at MIT for students from non-traditional backgrounds.    

“We will increase the number of students in both physics and AI from underrepresented groups by providing fellowships for the MicroMasters program,” says Isaac Chuang, professor of physics and electrical engineering, senior associate dean for digital learning, and RLE researcher at MIT. “We also plan on working with undergraduate MIT Summer Research Program students, to introduce them to the tools of physics and AI research that they might not have access to at their home institutions.”

The IAIFI plans to expand its impact via numerous outreach efforts, including a K-12 program in which students are given data from the LHC and LIGO and tasked with rediscovering the Higgs boson and gravitational waves. 

“After confirming these recent Nobel Prizes, we can ask the students to find tiny artificial signals embedded in the data using AI and fundamental physics principles,” says assistant professor of physics Phil Harris, an LNS researcher at MIT. “With projects like this, we hope to disseminate knowledge about — and enthusiasm for — physics, AI, and their intersection.”

In addition, the IAIFI will collaborate with industry and government to advance the frontiers of both AI and physics, as well as societal sectors that stand to benefit from AI innovation. IAIFI members already have many active collaborations with industry partners, including DeepMind, Microsoft Research, and Amazon. 

“We will tackle two of the greatest mysteries of science: how our universe works and how intelligence works,” says MIT professor of physics Max Tegmark, an MIT Kavli Institute researcher. “Our key strategy is to link them, using physics to improve AI and AI to improve physics. We’re delighted that the NSF is investing the vital seed funding needed to launch this exciting effort.”

Building new connections at MIT and beyond

Leveraging MIT’s culture of collaboration, the IAIFI aims to generate new connections and to strengthen existing ones across MIT and beyond.

Of the 27 current IAIFI senior investigators, 16 are at MIT and members of the LNS, RLE, MIT Kavli Institute, CSAIL, and IDSS. In addition, IAIFI investigators are members of related NSF-supported efforts at MIT, such as the Center for Brains, Minds, and Machines within the McGovern Institute for Brain Research and the MIT-Harvard Center for Ultracold Atoms.  

“We expect a lot of creative synergies as we bring physics and computer science together to study AI,” says Bill Freeman, the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science and researcher in CSAIL. “I’m excited to work with my physics colleagues on topics that bridge these fields.”

More broadly, the IAIFI aims to make Cambridge, Massachusetts, and the surrounding Boston area a hub for collaborative efforts to advance both physics and AI. 

“As we teach in 8.01 and 8.02, part of what makes physics so powerful is that it provides a universal language that can be applied to a wide range of scientific problems,” says Thaler. “Through the IAIFI, we will create a common language that transcends the intellectual borders between physics and AI to facilitate groundbreaking discoveries.”

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