Faculty-receive-funding-to-develop-artificial-intelligence-techniques-to-combat.jpg

Faculty receive funding to develop artificial intelligence techniques to combat Covid-19 | MIT News

news .mit.edu/sites/default/files/styles/news_article__cover_image__original/public/images/202007/CITRIS-Seed-Award.jpg?itok=2hPUKj_7″ />

Artificial intelligence has the power to help put an end to the Covid-19 pandemic. Not only can techniques of machine learning and natural language processing be used to track and report Covid-19 infection rates, but other AI techniques can also be used to make smarter decisions about everything from when states should reopen to how vaccines are designed. Now, MIT researchers working on seven groundbreaking projects on Covid-19 will be funded to more rapidly develop and apply novel AI techniques to improve medical response and slow the pandemic spread.

Earlier this year, the C3.ai Digital Transformation Institute (C3.ai DTI) formed, with the goal of attracting the world’s leading scientists to join in a coordinated and innovative effort to advance the digital transformation of businesses, governments, and society. The consortium is dedicated to accelerating advances in research and combining machine learning, artificial intelligence, internet of things, ethics, and public policy — for enhancing societal outcomes. MIT, under the auspices of the School of Engineering, joined the C3.ai DTI consortium, along with C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign, the University of California at Berkeley, Princeton University, the University of Chicago, Carnegie Mellon University, and, most recently, Stanford University.

The initial call for project proposals aimed to embrace the challenge of abating the spread of Covid-19 and advance the knowledge, science, and technologies for mitigating the impact of pandemics using AI. Out of a total of 200 research proposals, 26 projects were selected and awarded $5.4 million to continue AI research to mitigate the impact of Covid-19 in the areas of medicine, urban planning, and public policy.

The first round of grant recipients was recently announced, and among them are five projects led by MIT researchers from across the Institute: Saurabh Amin, associate professor of civil and environmental engineering; Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management; Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of the MIT Institute for Data, Systems, and Society; David Gifford, professor of biological engineering and of electrical engineering and computer science; and Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, head of the Department of Electrical Engineering and Computer Science, and deputy dean of academics for MIT Schwarzman College of Computing.

“We are proud to be a part of this consortium, and to collaborate with peers across higher education, industry, and health care to collectively combat the current pandemic, and to mitigate risk associated with future pandemics,” says Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “We are so honored to have the opportunity to accelerate critical Covid-19 research through resources and expertise provided by the C3.ai DTI.”

Additionally, three MIT researchers will collaborate with principal investigators from other institutions on projects blending health and machine learning. Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science, join Ziv Bar-Joseph from Carnegie Mellon University for a project using machine learning to seek treatment for Covid-19. Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science, joins Sendhil Mullainathan of the University of Chicago for a project using machine learning to support emergency triage of pulmonary collapse due to Covid-19 on the basis of X-rays.

Bertsimas’s project develops automated, interpretable, and scalable decision-making systems based on machine learning and artificial intelligence to support clinical practices and public policies as they respond to the Covid-19 pandemic. When it comes to reopening the economy while containing the spread of the pandemic, Ozdaglar’s research provides quantitative analyses of targeted interventions for different groups that will guide policies calibrated to different risk levels and interaction patterns. Amin is investigating the design of actionable information and effective intervention strategies to support safe mobilization of economic activity and reopening of mobility services in urban systems. Dahleh’s research innovatively uses machine learning to determine how to safeguard schools and universities against the outbreak. Gifford was awarded funding for his project that uses machine learning to develop more informed vaccine designs with improved population coverage, and to develop models of Covid-19 disease severity using individual genotypes.

“The enthusiastic support of the distinguished MIT research community is making a huge contribution to the rapid start and significant progress of the C3.ai Digital Transformation Institute,” says Thomas Siebel, chair and CEO of C3.ai. “It is a privilege to be working with such an accomplished team.”

The following projects are the MIT recipients of the inaugural C3.ai DTI Awards: 

“Pandemic Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions” — Saurabh Amin, associate professor of civil and environmental engineering; and Patrick Jaillet, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science

“Effective Cocktail Treatments for SARS-CoV-2 Based on Modeling Lung Single Cell Response Data” — Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science (Principal investigator: Ziv Bar-Joseph of Carnegie Mellon University)

“Toward Analytics-Based Clinical and Policy Decision Support to Respond to the Covid-19 Pandemic” — Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management and associate dean for business analytics; and Alexandre Jacquillat, assistant professor of operations research and statistics

“Reinforcement Learning to Safeguard Schools and Universities Against the Covid-19 Outbreak” — Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of MIT Institute for Data, Systems, and Society; and Peko Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of engineering

“Machine Learning-Based Vaccine Design and HLA Based Risk Prediction for Viral Infections” — David Gifford, professor of biological engineering and of electrical engineering and computer science

“Machine Learning Support for Emergency Triage of Pulmonary Collapse in Covid-19” — Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science (Principal investigator: Sendhil Mullainathan of the University of Chicago)

“Targeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic” — Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, department head of electrical engineering and computer science, and deputy dean of academics for MIT Schwarzman College of Computing; and Daron Acemoglu, Institute Professor

Covid Abruzzo Basilicata Calabria Campania Emilia Romagna Friuli Venezia Giulia Lazio Liguria Lombardia Marche Molise Piemonte Puglia Sardegna Sicilia Toscana Trentino Alto Adige Umbria Valle d’Aosta Veneto Italia Agrigento Alessandria Ancona Aosta Arezzo Ascoli Piceno Asti Avellino Bari Barletta-Andria-Trani Belluno Benevento Bergamo Biella Bologna Bolzano Brescia Brindisi Cagliari Caltanissetta Campobasso Carbonia-Iglesias Caserta Catania Catanzaro Chieti Como Cosenza Cremona Crotone Cuneo Enna Fermo Ferrara Firenze Foggia Forlì-Cesena Frosinone Genova Gorizia Grosseto Imperia Isernia La Spezia L’Aquila Latina Lecce Lecco Livorno Lodi Lucca Macerata Mantova Massa-Carrara Matera Messina Milano Modena Monza e della Brianza Napoli Novara Nuoro Olbia-Tempio Oristano Padova Palermo Parma Pavia Perugia Pesaro e Urbino Pescara Piacenza Pisa Pistoia Pordenone Potenza Prato Ragusa Ravenna Reggio Calabria Reggio Emilia Rieti Rimini Roma Rovigo Salerno Medio Campidano Sassari Savona Siena Siracusa Sondrio Taranto Teramo Terni Torino Ogliastra Trapani Trento Treviso Trieste Udine Varese Venezia Verbano-Cusio-Ossola Vercelli Verona Vibo Valentia Vicenza Viterbo

Research-into-AI-Neuroscience-Psychology-Aims-to-Make-AI-Less-Artificial.jpg

Research into AI, Neuroscience, Psychology Aims to Make AI Less Artificial 

AI and neuroscience are developing together, making each stronger. (Source: Getty Images) 

By AI Trends Staff  

Research at the intersection of AI, psychology, and neuroscience is attracting interest and investment. The study of the nervous system is called by some the “ultimate challenge” of the biological sciences.    

Irina Rish, Associate Professor, Computer Science and Operations Research department, Université de Montréal, and a core member of Mila – the Quebec AI Institute

The trend is exemplified in the experience of Irina Rish, now an Associate Professor  in the Computer Science and Operations Research department at the Université de Montréal (UdeM),and a core member of Mila – the Quebec AI Institute.   

Rish was 14 years old and going to high school in the central Asian city of Samarkand, Uzbekistan, when she first came across the notion of artificial intelligence. “I saw a book, translated from English into Russian, the cover was black with yellow letters, and the title was ‘Can Machines Think?’” Rish recalled in a recent article in Mirage.   

Rish was intrigued. “The book was about AI, and I said to myself: ‘Gosh, that’s exactly what I was wondering: what algorithms can we design to solve difficult problems, and how can we boost our own ‘natural intelligence,’” she recalled. 

That curiosity set her on a path to her life’s work. She graduated from universities in Moscow and California, then embarked on what became a 20-year career at IBM, including 10 years as a research scientist at the Watson Research Center. Last October, she moved to Canada to become an associate professor at Université de Montréal and a core faculty member at its affiliated AI institute, Mila. 

This summer she was awarded a Canada Excellence Research Chair (CERC), which came with a $34 million grant over several years from the federal government and other sources, including industry players Samsung, IBM, Microsoft, and Element AI.  

“It’s a wonderful opportunity for me and my team at Mila,” Rish said. “Over the coming years, this chair will allow us to explore the frontiers of AI research at the intersection of machine learning and neuroscience, and advance the field toward more autonomous, human-level AI by developing novel models and methods for broad and robust AI systems, as opposed to today’s narrow and brittle ones,” she said. 

Rish holds 64 patents, has published over 90 research papers, written several book chapters, edited three books and published a monograph on sparse modelling, an area of statistical machine learning particularly important for scientific data analysis such as computational biology and neuroimaging. 

So far at Mila, among her projects has been working with scientific director Yoshua Bengio to help develop Covi, a contact-tracing app for Covid-19.  

Her goal is “to develop continual, lifelong learning AI capabilities, similar to those of humans, as well as approaches to making AI more robust to changes in its environment and tasks it has to solve, and capable of better understanding and generalization, akin to human capabilities,” she stated.  

She sees the work as “the intersection of artificial intelligence, neuroscience, and psychology, using computers to analyze brain data and find interesting patterns there related to human behavior, to mental states and their changes, and using what you learn to better understand how the brain works and to make computers work better and AI less artificial.” 

Using AI to Decode How the Brain Sends Signals to Limbs  

Chethan Pandarinath, biomedical engineer at Emory University and the Georgia Institute of Technology

Researcher Chethan Pandarinath, a biomedical engineer at Emory University and the Georgia Institute of Technology, both in Atlanta, is working on enabling people with paralyzed limbs to reach out and grasp with a robotic arm as they would their own. He is collecting recordings of brain activity in people with paralysis, in the hopes of identifying patterns of electrical activity in neurons that correspond to moving an arm in a particular way, so that the instruction can be fed to an artificial limb. That is akin to reading minds.  

“It turns out, that’s a really challenging problem,” Pandarinath stated in a recent account in Nature. “These signals from the brain—they’re really complicated.” He decided to feed his brain activity recordings into an artificial neural network, a software architecture inspired by the brain, to try to get it to reproduce the data.   

Patterns the researchers call latent factors were found to control the overall behavior of the recorded activity. The effort revealed the brain’s temporal dynamics, the way a pattern of neural activity changes from one moment to the next. This allowed a more fine-grained set of instructions to be produced for arm movements than previous methods. “Now, we can very precisely say, on an almost millisecond-by-millisecond basis, right now the animal is trying to move at this precise angle,” Pandarinath stated. “That’s exactly what we need to know to control a robotic arm.” 

In this way, AI is helping brain science and brain science is giving more insight to AI researchers. “The technology is coming full circle and being applied back to understand the brain,” he stated.  

An artificial neural network is only a rough analogy of how the brain works, stated David Sussillo, a computational neuroscientist with the Google Brain Team in San Francisco, who collaborated with Pandarinath on his work on latent factors. For instance, it models synapses as numbers in a matrix, when in reality they are complex pieces of biological machinery that use both chemical and electrical activity to send or terminate signals, and that interact with their neighbors in dynamic patterns. “You couldn’t get further from the truth of what a synapse actually is than a single number in a matrix,” Sussillo stated. 

Still, artificial neural networks have proved useful for studying the brain. If such a system can produce a pattern of neural activity that resembles the pattern that is recorded from the brain, scientists can examine how the system generates its output and then make inferences about how the brain does the same thing. This approach can be applied to any cognitive task of interest to neuroscientists, including processing an image. “If you can train a neural network to do it,” stated Sussillo, “then perhaps you can understand how that network functions, and then use that to understand the biological data.”  

Comparing How Machine Learning Works to How the Brain Works 

Gabriel A. Silva, professor of Bioengineering and Neurosciences, University of California, San Diego

A similar conclusion was reached by Gabriel A. Silva, a professor of Bioengineering and Neurosciences at the University of California, San Diego, whose work includes how study of the brain can have practical benefits for new AI systems.  

“I and other researchers in the field, including a number of its leaders, have a growing sense that finding out more about how the brain processes information could help programmers translate the concepts of thinking from the wet and squishy world of biology into all-new forms of machine learning in the digital world,” Silva stated in an article in Neuroscience News.   

How machine learning works and how the brain works are very different. To recognize an image of a cow, a machine learning system needs to be fed many, many images of cows in order to learn. Whereas, “The brain takes in a very small amount of input data—like a photograph of a cow and a drawing of a cow—very quickly. And after only a very small number of examples, even a toddler will grasp the idea of what a cow looks like and be able to identify one in new images, from different angles, and in different colors,” Silva wrote. 

The brain and machine learning systems use fundamentally different algorithms. Because of this, “each excels in ways the other fails miserably,” Silva observes.  

It is challenging to try to distinguish which brain processes might work well as machine learning algorithms. One approach is to focus on ideas that improve machine learning and identify new areas of neuroscience, at the same time.  

“Lessons can go both ways, from brain science to artificial intelligence—and back, with AI research highlighting new questions for biological neuroscientists,” Silva suggests.  

Read the source articles in Mirage, in Nature and in Neuroscience News. 

Covid Abruzzo Basilicata Calabria Campania Emilia Romagna Friuli Venezia Giulia Lazio Liguria Lombardia Marche Molise Piemonte Puglia Sardegna Sicilia Toscana Trentino Alto Adige Umbria Valle d’Aosta Veneto Italia Agrigento Alessandria Ancona Aosta Arezzo Ascoli Piceno Asti Avellino Bari Barletta-Andria-Trani Belluno Benevento Bergamo Biella Bologna Bolzano Brescia Brindisi Cagliari Caltanissetta Campobasso Carbonia-Iglesias Caserta Catania Catanzaro Chieti Como Cosenza Cremona Crotone Cuneo Enna Fermo Ferrara Firenze Foggia Forlì-Cesena Frosinone Genova Gorizia Grosseto Imperia Isernia La Spezia L’Aquila Latina Lecce Lecco Livorno Lodi Lucca Macerata Mantova Massa-Carrara Matera Messina Milano Modena Monza e della Brianza Napoli Novara Nuoro Olbia-Tempio Oristano Padova Palermo Parma Pavia Perugia Pesaro e Urbino Pescara Piacenza Pisa Pistoia Pordenone Potenza Prato Ragusa Ravenna Reggio Calabria Reggio Emilia Rieti Rimini Roma Rovigo Salerno Medio Campidano Sassari Savona Siena Siracusa Sondrio Taranto Teramo Terni Torino Ogliastra Trapani Trento Treviso Trieste Udine Varese Venezia Verbano-Cusio-Ossola Vercelli Verona Vibo Valentia Vicenza Viterbo

National-Science-Foundation-announces-MIT-led-Institute-for-Artificial-Intelligence-and-scaled.jpg

National Science Foundation announces MIT-led Institute for Artificial Intelligence and Fundamental Interactions | MIT News

news .mit.edu/sites/default/files/styles/news_article__cover_image__original/public/images/202008/iaifi-mit-announcement-00.jpg?itok=U_HIT8Mi” />

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.”

Covid Abruzzo Basilicata Calabria Campania Emilia Romagna Friuli Venezia Giulia Lazio Liguria Lombardia Marche Molise Piemonte Puglia Sardegna Sicilia Toscana Trentino Alto Adige Umbria Valle d’Aosta Veneto Italia Agrigento Alessandria Ancona Aosta Arezzo Ascoli Piceno Asti Avellino Bari Barletta-Andria-Trani Belluno Benevento Bergamo Biella Bologna Bolzano Brescia Brindisi Cagliari Caltanissetta Campobasso Carbonia-Iglesias Caserta Catania Catanzaro Chieti Como Cosenza Cremona Crotone Cuneo Enna Fermo Ferrara Firenze Foggia Forlì-Cesena Frosinone Genova Gorizia Grosseto Imperia Isernia La Spezia L’Aquila Latina Lecce Lecco Livorno Lodi Lucca Macerata Mantova Massa-Carrara Matera Messina Milano Modena Monza e della Brianza Napoli Novara Nuoro Olbia-Tempio Oristano Padova Palermo Parma Pavia Perugia Pesaro e Urbino Pescara Piacenza Pisa Pistoia Pordenone Potenza Prato Ragusa Ravenna Reggio Calabria Reggio Emilia Rieti Rimini Roma Rovigo Salerno Medio Campidano Sassari Savona Siena Siracusa Sondrio Taranto Teramo Terni Torino Ogliastra Trapani Trento Treviso Trieste Udine Varese Venezia Verbano-Cusio-Ossola Vercelli Verona Vibo Valentia Vicenza Viterbo

Recent Posts

Archives

X
wpChatIcon