Archives des MIT-IBM Watson AI Lab

MIT engineers design proteins by their motion, not just their shape
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A better method for identifying overconfident large language models
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MIT-IBM Watson AI Lab seed to signal: Amplifying early-career faculty impact
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A better method for planning complex visual tasks
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New method could increase LLM training efficiency
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Mixing generative AI with physics to create personal items that work in the real world
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Study: Platforms that rank the latest LLMs can be unreliable
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Antonio Torralba, three MIT alumni named 2025 ACM fellows
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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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A smarter way for large language models to think about hard problems
20 Avr
Charting the future of AI, from safer answers to faster thinking
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Creating AI that matters
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Method teaches generative AI models to locate personalized objects
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How to build AI scaling laws for efficient LLM training and budget maximization
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MIT tool visualizes and edits “physically impossible” objects
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This “smart coach” helps LLMs switch between text and code
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Study could lead to LLMs that are better at complex reasoning
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Inroads to personalized AI trip planning
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AI-enabled control system helps autonomous drones stay on target in uncertain environments
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AI learns how vision and sound are connected, without human intervention
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Training LLMs to self-detoxify their language
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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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Researchers teach LLMs to solve complex planning challenges
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AI tool generates high-quality images faster than state-of-the-art approaches
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Like human brains, large language models reason about diverse data in a general way
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#MIT-IBM Watson AI Lab

MIT engineers design proteins by their motion, not just their shape

A better method for identifying overconfident large language models

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

A better method for planning complex visual tasks

New method could increase LLM training efficiency

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

Study: Platforms that rank the latest LLMs can be unreliable

Antonio Torralba, three MIT alumni named 2025 ACM fellows

Guided learning lets “untrainable” neural networks realize their potential

A new way to increase the capabilities of large language models

Enabling small language models to solve complex reasoning tasks

A smarter way for large language models to think about hard problems

Charting the future of AI, from safer answers to faster thinking

Creating AI that matters

Method teaches generative AI models to locate personalized objects

How to build AI scaling laws for efficient LLM training and budget maximization

MIT tool visualizes and edits “physically impossible” objects

This “smart coach” helps LLMs switch between text and code

Study could lead to LLMs that are better at complex reasoning

Inroads to personalized AI trip planning

AI-enabled control system helps autonomous drones stay on target in uncertain environments

AI learns how vision and sound are connected, without human intervention

Training LLMs to self-detoxify their language

Could LLMs help design our next medicines and materials?

New method assesses and improves the reliability of radiologists’ diagnostic reports

Researchers teach LLMs to solve complex planning challenges

AI tool generates high-quality images faster than state-of-the-art approaches

Like human brains, large language models reason about diverse data in a general way