Tag
#Computer Science and Artificial Intelligence Laboratory (CSAIL)
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MIT-IBM Watson AI Lab seed to signal: Amplifying early-career faculty impact
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Improving AI models’ ability to explain their predictions
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Mixing generative AI with physics to create personal items that work in the real world
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Helping AI agents search to get the best results out of large language models
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Antonio Torralba, three MIT alumni named 2025 ACM fellows
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Why it’s critical to move beyond overly aggregated machine-learning metrics
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Generative AI tool helps 3D print personal items that sustain daily use
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MIT scientists investigate memorization risk in the age of clinical AI
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Guided learning lets “untrainable” neural networks realize their potential
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A new way to increase the capabilities of large language models
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Enabling small language models to solve complex reasoning tasks
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New method improves the reliability of statistical estimations
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New control system teaches soft robots the art of staying safe
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MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases
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Understanding the nuances of human-like intelligence
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Charting the future of AI, from safer answers to faster thinking
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MIT researchers propose a new model for legible, modular software
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3 Questions: How AI is helping us monitor and support vulnerable ecosystems
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Five with MIT ties elected to National Academy of Medicine for 2025
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Creating AI that matters
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New software designs eco-friendly clothing that can reassemble into new items
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Method teaches generative AI models to locate personalized objects
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Using generative AI to diversify virtual training grounds for robots
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AI maps how a new antibiotic targets gut bacteria
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Responding to the climate impact of generative AI
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New AI system could accelerate clinical research
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How to build AI scaling laws for efficient LLM training and budget maximization
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Machine-learning tool gives doctors a more detailed 3D picture of fetal health
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A greener way to 3D print stronger stuff
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MIT researchers develop AI tool to improve flu vaccine strain selection
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Researchers glimpse the inner workings of protein language models
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MIT tool visualizes and edits “physically impossible” objects
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New algorithms enable efficient machine learning with symmetric data
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Robot, know thyself: New vision-based system teaches machines to understand their bodies
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A new way to edit or generate images
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The unique, mathematical shortcuts language models use to predict dynamic scenarios
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Can AI really code? Study maps the roadblocks to autonomous software engineering
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AI shapes autonomous underwater “gliders”
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Study could lead to LLMs that are better at complex reasoning
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Using generative AI to help robots jump higher and land safely
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Unpacking the bias of large language models
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Teaching AI models what they don’t know
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Teaching AI models the broad strokes to sketch more like humans do
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AI learns how vision and sound are connected, without human intervention
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Study shows vision-language models can’t handle queries with negation words
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Hybrid AI model crafts smooth, high-quality videos in seconds
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Novel AI model inspired by neural dynamics from the brain
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Making AI models more trustworthy for high-stakes settings
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The MIT-Portugal Program enters Phase 4
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“Periodic table of machine learning” could fuel AI discovery
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3D modeling you can feel
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Making AI-generated code more accurate in any language
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Training LLMs to self-detoxify their language
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New method efficiently safeguards sensitive AI training data
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Could LLMs help design our next medicines and materials?
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New method assesses and improves the reliability of radiologists’ diagnostic reports
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For this computer scientist, MIT Open Learning was the start of a life-changing journey
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At the core of problem-solving
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Robotic helper making mistakes? Just nudge it in the right direction
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Like human brains, large language models reason about diverse data in a general way
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AI model deciphers the code in proteins that tells them where to go