Quest for Intelligence
Quest for Intelligence

Making it easier to verify an AI model’s responses

By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.

Helping robots practice skills independently to adapt to unfamiliar environments

New algorithm helps robots practice skills like sweeping and placing objects, potentially helping them improve at important tasks in houses, hospitals, and factories.

Reasoning skills of large language models are often overestimated

New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.

Natural language boosts LLM performance in coding, planning, and robotics

Three neurosymbolic methods help language models find better abstractions within natural language, then use those representations to execute complex tasks.

A crossroads for computing at MIT

The MIT Schwarzman College of Computing building will form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence.

New technique helps robots pack objects into a tight space

Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.

When computer vision works more like a brain, it sees more like people do

Training artificial neural networks with data from real brains can make computer vision more robust.

Automating the math for decision-making under uncertainty

A new tool brings the benefits of AI programming to a much broader class of problems.