Computer Science and Artificial Intelligence Laboratory (CSAIL)

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Helping data centers deliver higher performance with less hardware

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Defense Advanced Research Projects Agency (DARPA), Electrical engineering and computer science (EECS), Electronics, Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, Sustainability

To improve data center efficiency, multiple storage devices are often pooled together over a network so many applications can share them. But even with pooling, significant device capacity remains underutilized due to performance variability across the devices.MIT researchers have now developed a system that boosts the performance of storage devices by handling three major sources […]

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Preview tool helps makers visualize 3D-printed objects

3-D printing, Additive manufacturing, ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Machine Learning, Maker movement, Mechanical engineering, MIT Schwarzman College of Computing, Research, School of Engineering, Sustainability

Designers, makers, and others often use 3D printing to rapidly prototype a range of functional objects, from movie props to medical devices. Accurate print previews are essential so users know a fabricated object will perform as expected.But previews generated by most 3D-printing software focus on function rather than aesthetics. A printed object may end up

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Augmenting citizen science with computer vision for fish monitoring

Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), ai, AI (Artificial Intelligence), Animals, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, data, Ecology, Electrical engineering and computer science (EECS), Environment, Machine Learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT Sea Grant, National Science Foundation (NSF), Oceanography and ocean engineering, Research, School of Engineering, Undergraduate Research Opportunities Program (UROP), Video, Volunteering, outreach, public service, Water

Each spring, river herring populations migrate from Massachusetts coastal waters to begin their annual journey up rivers and streams to freshwater spawning habitat. River herring have faced severe population declines over the past several decades, and their migration is extensively monitored across the region, primarily through traditional visual counting and volunteer-based programs. Monitoring fish movement and understanding

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MIT-IBM Watson AI Lab seed to signal: Amplifying early-career faculty impact

Aeronautical and astronautical engineering, ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Careers, Collaboration, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Industry, Laboratory for Information and Decision Systems (LIDS), Machine Learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Natural Language Processing, Research, Robotics, School of Engineering

The early years of faculty members’ careers are a formative and exciting time in which to establish a firm footing that helps determine the trajectory of researchers’ studies. This includes building a research team, which demands innovative ideas and direction, creative collaborators, and reliable resources. For a group of MIT faculty working with and on artificial

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Improving AI models’ ability to explain their predictions

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, data, Electrical engineering and computer science (EECS), Human-computer interaction, Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output.Concept bottleneck modeling is one method that enables artificial intelligence systems to explain their decision-making process. These methods force a deep-learning model to use a

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Mixing generative AI with physics to create personal items that work in the real world

3-D printing, ai, AI (Artificial Intelligence), Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Design, Electrical engineering and computer science (EECS), Fabrication, Human-computer interaction, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering

Have you ever had an idea for something that looked cool, but wouldn’t work well in practice? When it comes to designing things like decor and personal accessories, generative artificial intelligence (genAI) models can relate. They can produce creative and elaborate 3D designs, but when you try to fabricate such blueprints into real-world objects, they

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Helping AI agents search to get the best results out of large language models

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Human-computer interaction, Machine Learning, MIT Schwarzman College of Computing, Programming, Programming languages, Research, School of Engineering, Software

Whether you’re a scientist brainstorming research ideas or a CEO hoping to automate a task in human resources or finance, you’ll find that artificial intelligence tools are becoming the assistants you didn’t know you needed. In particular, many professionals are tapping into the talents of semi-autonomous software systems called AI agents, which can call on AI

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Antonio Torralba, three MIT alumni named 2025 ACM fellows

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Antonio Torralba, Delta Electronics Professor of Electrical Engineering and Computer Science and faculty head of artificial intelligence and decision-making at MIT, has been named to the 2025 cohort of Association for Computing Machinery (ACM) Fellows. He shares the honor of an ACM Fellowship with three MIT alumni: Eytan Adar ’97, MEng ’98; George Candea ’97,

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The philosophical puzzle of rational artificial intelligence

ai, AI (Artificial Intelligence), Artificial Intelligence, Classes and programs, Collaboration, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Education, teaching, academics, Electrical engineering and computer science (EECS), Machine Learning, MIT Schwarzman College of Computing, Philosophy, School of Engineering, School of Humanities Arts and Social Sciences, Technology and society

To what extent can an artificial system be rational?A new MIT course, 6.S044/24.S00 (AI and Rationality), doesn’t seek to answer this question. Instead, it challenges students to explore this and other philosophical problems through the lens of AI research. For the next generation of scholars, concepts of rationality and agency could prove integral in AI

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Why it’s critical to move beyond overly aggregated machine-learning metrics

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Health care, Institute for Medical Engineering and Science (IMES), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering, Technology and society

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to test whenever a model is deployed in a new setting.“We demonstrate that even when you train models on large amounts of data, and choose the

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