MIT-IBM Watson AI Lab

Artificial IntelligenceMachine Learning

Guided learning lets “untrainable” neural networks realize their potential

CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.

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Artificial IntelligenceMachine Learning

A new way to increase the capabilities of large language models

MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.

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Artificial IntelligenceMachine Learning

Enabling small language models to solve complex reasoning tasks

The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.

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Artificial IntelligenceMachine Learning

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

This new technique enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question.

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Artificial IntelligenceMachine Learning

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

MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.

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Artificial IntelligenceIndustryMachine Learning

Creating AI that matters

How the MIT-IBM Watson AI Lab is shaping AI-sociotechnical systems for the future.

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Artificial IntelligenceMachine Learning

Method teaches generative AI models to locate personalized objects

After being trained with this technique, vision-language models can better identify a unique item in a new scene.

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Artificial IntelligenceMachine Learning

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

MIT-IBM Watson AI Lab researchers have developed a universal guide for estimating how large language models will perform based on smaller models in the same family.

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Artificial Intelligence

MIT tool visualizes and edits “physically impossible” objects

By visualizing Escher-like optical illusions in 2.5 dimensions, the “Meschers” tool could help scientists understand physics-defying shapes and spark new designs.

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Artificial IntelligenceMachine Learning

The CodeSteer system could boost large language models’ accuracy when solving complex problems, such as scheduling shipments in a supply chain.

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