MIT-IBM Watson AI Lab
MIT-IBM Watson AI Lab

Mapping the brain pathways of visual memorability

For the first time, researchers use a combination of MEG and fMRI to map the spatio-temporal human brain dynamics of a visual image being recognized.

This tiny chip can safeguard user data while enabling efficient computing on a smartphone

Researchers have developed a security solution for power-hungry AI models that offers protection against two common attacks.

3 Questions: Enhancing last-mile logistics with machine learning

MIT Center for Transportation and Logistics Director Matthias Winkenbach uses AI to make vehicle routing more efficient and adaptable for unexpected events.

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.

A faster, better way to prevent an AI chatbot from giving toxic responses

Researchers create a curious machine-learning model that finds a wider variety of prompts for training a chatbot to avoid hateful or harmful output.

A new way to let AI chatbots converse all day without crashing

Researchers developed a simple yet effective solution for a puzzling problem that can worsen the performance of large language models such as ChatGPT.

Reasoning and reliability in AI

PhD students interning with the MIT-IBM Watson AI Lab look to improve natural language usage.

Multiple AI models help robots execute complex plans more transparently

A multimodal system uses models trained on language, vision, and action data to help robots develop and execute plans for household, construction, and manufacturing tasks.

Technique could efficiently solve partial differential equations for numerous applications

MIT researchers propose “PEDS” method for developing models of complex physical systems in mechanics, optics, thermal transport, fluid dynamics, physical chemistry, climate, and more.

AI agents help explain other AI systems

MIT researchers introduce a method that uses artificial intelligence to automate the explanation of complex neural networks.