Electrical engineering and computer science (EECS)

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AI to help researchers see the bigger picture in cell biology

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Biological engineering, Biology, Broad Institute, Cells, Computer science and technology, Diagnostics, Electrical engineering and computer science (EECS), Genetics, Health, IDSS, Laboratory for Information and Decision Systems (LIDS), Machine Learning, Medicine, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering

Studying gene expression in a cancer patient’s cells can help clinical biologists understand the cancer’s origin and predict the success of different treatments. But cells are complex and contain many layers, so how the biologist conducts measurements affects which data they can obtain. For instance, measuring proteins in a cell could yield different information about the […]

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Parking-aware navigation system could prevent frustration and emissions

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Cities, Civil and environmental engineering, Climate change, Computer modeling, Computer science and technology, Electrical engineering and computer science (EECS), Emissions, IDSS, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Energy Initiative, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, Sustainability, Transportation

It happens every day — a motorist heading across town checks a navigation app to see how long the trip will take, but they find no parking spots available when they reach their destination. By the time they finally park and walk to their destination, they’re significantly later than they expected to be.Most popular navigation

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Personalization features can make LLMs more agreeable

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer science and technology, Electrical engineering and computer science (EECS), ethics, Human-computer interaction, IDSS, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

Many of the latest large language models (LLMs) are designed to remember details from past conversations or store user profiles, enabling these models to personalize responses.But researchers from MIT and Penn State University found that, over long conversations, such personalization features often increase the likelihood an LLM will become overly agreeable or begin mirroring the

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Study: Platforms that rank the latest LLMs can be unreliable

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer science and technology, data, Electrical engineering and computer science (EECS), IDSS, Laboratory for Information and Decision Systems (LIDS), Machine Learning, Mathematics, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Research, School of Engineering

A firm that wants to use a large language model (LLM) to summarize sales reports or triage customer inquiries can choose between hundreds of unique LLMs with dozens of model variations, each with slightly different performance.To narrow down the choice, companies often rely on LLM ranking platforms, which gather user feedback on model interactions to

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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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Brian Hedden named co-associate dean of Social and Ethical Responsibilities of Computing

Administration, ai, AI (Artificial Intelligence), Artificial Intelligence, Computer science and technology, Education, teaching, academics, Electrical engineering and computer science (EECS), ethics, Faculty, Humanities, MIT Schwarzman College of Computing, MIT Sloan School of Management, Philosophy, School of Engineering, School of Humanities Arts and Social Sciences, Technology and policy

Brian Hedden PhD ’12 has been appointed co-associate dean of the Social and Ethical Responsibilities of Computing (SERC) at MIT, a cross-cutting initiative in the MIT Schwarzman College of Computing, effective Jan. 16.Hedden is a professor in the Department of Linguistics and Philosophy, holding an MIT Schwarzman College of Computing shared position with the Department

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Antonio Torralba, three MIT alumni named 2025 ACM fellows

ai, AI (Artificial Intelligence), Alumni/ae, Artificial Intelligence, Awards, honors and fellowships, Center for Brains Minds and Machines, Computer graphics, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical engineering and computer science (EECS), Faculty, MIT Schwarzman College of Computing, MIT Siegel Family Quest for Intelligence, MIT-IBM Watson AI Lab, School of Engineering

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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Generative AI tool helps 3D print personal items that sustain daily use

3-D printing, ai, AI (Artificial Intelligence), Artificial Intelligence, Center for Bits and Atoms, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Design, Electrical engineering and computer science (EECS), Fabrication, Invention, Machine Learning, MIT Schwarzman College of Computing, School of Engineering

Generative artificial intelligence models have left such an indelible impact on digital content creation that it’s getting harder to recall what the internet was like before it. You can call on these AI tools for clever projects such as videos and photos — but their flair for the creative hasn’t quite crossed over into the

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