The U.S. National Science Foundation (NSF) has unveiled a groundbreaking investment exceeding $100 million to establish five cutting-edge artificial intelligence (AI) institutes, with each receiving approximately $20 million over a five-year period. Among these pioneering initiatives, the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) emerges as a game-changer, spearheaded by MIT's prestigious Laboratory for Nuclear Science (LNS) and serving as the intellectual hub for over 25 distinguished physics and AI senior researchers from MIT, Harvard, Northeastern, and Tufts universities.
By seamlessly integrating research methodologies from physics and artificial intelligence, the IAIFI aims to conquer some of the most formidable challenges in physics, ranging from precision calculations of matter's structure to detecting gravitational waves from merging black holes and extracting novel physical laws from complex, noisy datasets.
"Our vision at IAIFI is to pioneer the next generation of AI technologies, built upon the transformative concept that artificial intelligence can directly incorporate physics intelligence," explains Jesse Thaler, associate professor of physics at MIT, LNS researcher, and IAIFI director. "By merging the 'deep learning' revolution with physics' time-tested 'deep thinking' strategies, we strive to achieve profound insights into our universe and the fundamental principles underlying intelligence itself."
Researchers at IAIFI emphasize that their innovative approach will facilitate revolutionary physics discoveries while simultaneously advancing the broader AI landscape through the development of novel AI approaches that integrate first principles from fundamental physics.
"The application of simple principles like translational symmetry—which in nature leads to momentum conservation—has already dramatically improved image recognition capabilities," notes Mike Williams, associate professor of physics at MIT, LNS researcher, and IAIFI deputy director. "We're confident that incorporating more sophisticated physics principles will revolutionize how AI is utilized to study fundamental interactions, while simultaneously advancing AI's foundational concepts."
Furthermore, a central component of IAIFI's mission involves transferring their innovative technologies to the wider AI community, fostering collaboration and accelerating progress across multiple disciplines.
"Recognizing AI's critical role in shaping our future, NSF is investing in collaborative research and education hubs like the NSF IAIFI at MIT, which will unite academia, industry, and government to uncover profound discoveries and develop new capabilities," states NSF Director Sethuraman Panchanathan. "Just as previous NSF investments enabled the breakthroughs powering today's AI revolution, these awards will drive discovery and innovation that will maintain American leadership and competitiveness in AI for decades to come."
Revolutionary Research in AI and Fundamental Interactions
Fundamental interactions are described by two pillars of modern physics: the Standard Model of particle physics at short distances and the Lambda Cold Dark Matter model of Big Bang cosmology at long distances. Both models are built upon physical first principles such as causality and space-time symmetries. While abundant experimental evidence supports these theories, it also reveals their limitations—most notably, the Standard Model's inability to explain dark matter's nature, which plays a crucial role in cosmology.
Artificial intelligence holds tremendous potential to help answer these and other profound questions in physics, offering new approaches to age-old scientific challenges.
For numerous physics problems, the governing equations encoding fundamental physical laws are known. However, performing essential calculations within these frameworks—critical for testing our understanding of the universe and guiding physics discovery—can be computationally intensive or even intractable. IAIFI researchers are developing AI solutions for these first-principles theory studies, which naturally demand AI approaches that rigorously encode physics knowledge.
"My research group is creating new provably exact algorithms for theoretical nuclear physics," shares Phiala Shanahan, assistant professor of physics and LNS researcher at MIT. "Our first-principles approach has revealed applications in other scientific domains and even robotics, leading to exciting collaborations with industry partners."
Integrating physics principles into AI could also significantly impact numerous experimental applications, such as designing more easily verifiable AI methods. 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 rank among Earth's most sensitive instruments, yet the computational systems operating them primarily rely on technology from the previous century," observes Lisa Barsotti, Principal Research Scientist at the MIT Kavli Institute for Astrophysics and Space Research. "We've only begun to explore AI's potential in this domain; what we've seen so far suggests that IAIFI will be transformative."
These physics applications' unique characteristics also offer compelling research opportunities for AI more broadly. For instance, physics-informed architectures and hardware development could accelerate AI algorithms' speed, while statistical physics work provides a theoretical foundation for understanding AI dynamics.
"Physics has inspired many time-tested concepts in machine learning: maximizing entropy, Boltzmann machines, and variational inference, to name a few," says Pulkit Agrawal, 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 collaboration between physics and AI researchers will catalyze the next generation of machine learning algorithms."
Nurturing Next-Generation Talent
As AI technologies advance at an unprecedented pace, training junior researchers at the intersection of physics and AI becomes both crucial and challenging. The IAIFI aims to recruit and train a talented, diverse group of early-career researchers, including postdoctoral fellows through its IAIFI Fellows Program.
"By offering our fellows their choice of research problems and opportunities 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," explains Marin Soljacic, MIT professor of physics from the Research Laboratory of Electronics (RLE).
IAIFI researchers hope these fellows will spark interdisciplinary and multi-investigator collaborations, generate innovative 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 initiative spearheaded by Thaler, Williams, and Alexander Rakhlin—associate professor of brain and cognitive science at MIT and researcher in the Institute for Data, Systems, and Society (IDSS)—involves developing 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 form foundational pillars of AI. Integrating physics into this interdisciplinary doctoral program will spawn new ideas and exploration areas while nurturing a new generation of leaders at the intersection of physics, statistics, and AI," notes Rakhlin.
Education, Outreach, and Strategic Partnerships
The IAIFI aims to cultivate "human intelligence" by promoting education and outreach initiatives. 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 to work with undergraduate MIT Summer Research Program students, introducing them to physics and AI research tools they might not otherwise access at their home institutions."
The IAIFI plans to expand its impact through numerous outreach efforts, including a K-12 program where students receive data from the LHC and LIGO and are challenged to rediscover the Higgs boson and gravitational waves.
"After confirming these recent Nobel Prize-winning discoveries, we can ask students to find tiny artificial signals embedded in the data using AI and fundamental physics principles," shares Phil Harris, assistant professor of physics and LNS researcher at MIT. "Through projects like this, we hope to disseminate knowledge about—and enthusiasm for—physics, AI, and their intersection."
Additionally, the IAIFI will collaborate with industry and government to advance both AI and physics frontiers, as well as societal sectors that stand to benefit from AI innovation. IAIFI members already maintain active collaborations with industry partners, including DeepMind, Microsoft Research, and Amazon.
"We will tackle two of science's greatest mysteries: how our universe works and how intelligence functions," says Max Tegmark, MIT professor of physics and MIT Kavli Institute researcher. "Our key strategy is to connect these domains, using physics to enhance AI and AI to advance physics. We're delighted that NSF is providing the essential seed funding to launch this exciting endeavor."
Building New Connections at MIT and Beyond
Leveraging MIT's culture of collaboration, the IAIFI aims to generate new connections and strengthen existing ones across MIT and beyond its campus.
Of the 27 current IAIFI senior investigators, 16 are at MIT and members of the LNS, RLE, MIT Kavli Institute, CSAIL, and IDSS. Additionally, IAIFI investigators participate in 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 anticipate numerous 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 collaborate with my physics colleagues on topics bridging these fields."
More broadly, the IAIFI aims to establish Cambridge, Massachusetts, and the surrounding Boston area as a premier hub for collaborative efforts to advance both physics and AI.
"As we teach in 8.01 and 8.02, part of physics' power lies in providing a universal language applicable to diverse scientific problems," says Thaler. "Through the IAIFI, we will create a common language that transcends intellectual boundaries between physics and AI to facilitate groundbreaking discoveries."
