Computer science and technology

Artificial IntelligenceEnergy

New materials could boost the energy efficiency of microelectronics

By stacking multiple active components based on new materials on the back end of a computer chip, this new approach reduces the amount of energy wasted during computation.

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Artificial IntelligenceRobotics

MIT researchers “speak objects into existence” using AI and robotics

The speech-to-reality system combines 3D generative AI and robotic assembly to create objects on demand.

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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 LearningRobotics

New control system teaches soft robots the art of staying safe

MIT CSAIL and LIDS researchers developed a mathematically grounded system that lets soft robots deform, adapt, and interact with people and objects, without violating safety limits.

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Artificial IntelligenceMachine Learning

Researchers discover a shortcoming that makes LLMs less reliable

Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.

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Artificial IntelligenceMachine Learning

MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases

BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.

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Artificial IntelligenceMachine Learning

The cost of thinking

MIT neuroscientists find a surprising parallel in the ways humans and new AI models solve complex problems.

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Artificial IntelligenceMachine Learning

Understanding the nuances of human-like intelligence

Associate Professor Phillip Isola studies the ways in which intelligent machines “think,” in an effort to safely integrate AI into human society.

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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 IntelligenceMachine Learning

MIT researchers propose a new model for legible, modular software

The coding framework uses modular concepts and simple synchronization rules to make software clearer, safer, and easier for LLMs to generate.

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