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

Adoption of new tools and technologies occurs when users largely perceive them as reliable, accessible, and an improvement over the available methods and workflows for the cost. Five PhD students from the inaugural class of the MIT-IBM Watson AI Lab Summer Program are...

MIT researchers propose a new model for legible, modular software

Coding with large language models (LLMs) holds huge promise, but it also exposes some long-standing flaws in software: code that’s messy, hard to change safely, and often opaque about what’s really happening under the hood. Researchers at MIT’s Computer Science and...

Researchers discover a shortcoming that makes LLMs less reliable

Large language models (LLMs) sometimes learn the wrong lessons, according to an MIT study. Rather than answering a query based on domain knowledge, an LLM could respond by leveraging grammatical patterns it learned during training. This can cause a model to fail...

Teaching robots to map large environments

A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain. Researchers have recently started building powerful machine-learning...

How artificial intelligence can help achieve a clean energy future

There is growing attention on the links between artificial intelligence and increased energy demands. But while the power-hungry data centers being built to support AI could potentially stress electricity grids, increase customer prices and service interruptions, and...