Category: MIT Schwarzman College of Computing
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Scientists use generative AI to answer complex questions in physics
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in Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Machine Learning, Mathematics, MIT Schwarzman College of Computing, Physics, Research, School of Engineering, School of ScienceWhen water freezes, it transitions from a liquid phase to a solid phase, resulting in a drastic change in properties like density and volume. Phase transitions in water are so common most of us probably don’t even think about them, but phase transitions in novel materials or complex physical systems are an important area of…
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Using ideas from game theory to improve the reliability of language models
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in Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Game theory, Human-computer interaction, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Natural language processing, Research, Robotics, School of EngineeringImagine you and a friend are playing a game where your goal is to communicate secret messages to each other using only cryptic sentences. Your friend’s job is to guess the secret message behind your sentences. Sometimes, you give clues directly, and other times, your friend has to guess the message by asking yes-or-no questions…
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The power of App Inventor: Democratizing possibilities for mobile applications
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in Apps, Artificial Intelligence, Education, teaching, academics, Electrical Engineering & Computer Science (eecs), Global, History of MIT, History of science, K-12 education, Media Lab, MIT Schwarzman College of Computing, Online learning, Open access, Open source, Programming, School of Architecture and Planning, School of Engineering, STEM educationIn June 2007, Apple unveiled the first iPhone. But the company made a strategic decision about iPhone software: its new App Store would be a walled garden. An iPhone user wouldn’t be able to install applications that Apple itself hadn’t vetted, at least not without breaking Apple’s terms of service. That business decision, however, left…
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Creating bespoke programming languages for efficient visual AI systems
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in Artificial Intelligence, Computer graphics, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Faculty, games, Information systems and technology, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Profile, Programming, programming languages, School of Engineering, videoA single photograph offers glimpses into the creator’s world — their interests and feelings about a subject or space. But what about creators behind the technologies that help to make those images possible? MIT Department of Electrical Engineering and Computer Science Associate Professor Jonathan Ragan-Kelley is one such person, who has designed everything from tools…
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Natural language boosts LLM performance in coding, planning, and robotics
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in Artificial Intelligence, Brain and cognitive sciences, Center for Brains Minds and Machines, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Defense Advanced Research Projects Agency (DARPA), Department of Defense (DoD), Electrical Engineering & Computer Science (eecs), Human-computer interaction, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Natural language processing, Programming, programming languages, Quest for Intelligence, Research, Robotics, School of Engineering, School of ScienceLarge language models (LLMs) are becoming increasingly useful for programming and robotics tasks, but for more complicated reasoning problems, the gap between these systems and humans looms large. Without the ability to learn new concepts like humans do, these systems fail to form good abstractions — essentially, high-level representations of complex concepts that skip less-important…
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Julie Shah named head of the Department of Aeronautics and Astronautics
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in Administration, Aeronautical and astronautical engineering, Alumni/ae, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), Faculty, Leadership, MIT Schwarzman College of Computing, Robotics, School of EngineeringJulie Shah ’04, SM ’06, PhD ’11, the H.N. Slater Professor in Aeronautics and Astronautics, has been named the new head of the Department of Aeronautics and Astronautics (AeroAstro), effective May 1. “Julie brings an exceptional record of visionary and interdisciplinary leadership to this role. She has made substantial technical contributions in the field of…
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This tiny chip can safeguard user data while enabling efficient computing on a smartphone
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Health-monitoring apps can help people manage chronic diseases or stay on track with fitness goals, using nothing more than a smartphone. However, these apps can be slow and energy-inefficient because the vast machine-learning models that power them must be shuttled between a smartphone and a central memory server. Engineers often speed things up using hardware…
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Mapping the brain pathways of visual memorability
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in Artificial Intelligence, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Functional magnetic resonance imaging (fMRI), Image Processing, Imaging, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Neuroscience, Research, School of Engineering, VisionFor nearly a decade, a team of MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have been seeking to uncover why certain images persist in a people’s minds, while many others fade. To do this, they set out to map the spatio-temporal brain dynamics involved in recognizing a visual image. And now for the…
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To build a better AI helper, start by modeling the irrational behavior of humans
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in Algorithms, Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of EngineeringTo build AI systems that can collaborate effectively with humans, it helps to have a good model of human behavior to start with. But humans tend to behave suboptimally when making decisions. This irrationality, which is especially difficult to model, often boils down to computational constraints. A human can’t spend decades thinking about the ideal…