Human-computer interaction
Human-computer interaction

New MIT class uses anthropology to improve chatbots

MIT computer science students design AI chatbots to help young users become more social, and socially confident.

Improving AI models’ ability to explain their predictions

A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.

Mixing generative AI with physics to create personal items that work in the real world

To help generative AI models create durable, real-world accessories and decor, the PhysiOpt system runs physics simulations and makes subtle tweaks to its 3D blueprints.

Personalization features can make LLMs more agreeable

The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.

Helping AI agents search to get the best results out of large language models

EnCompass executes AI agent programs by backtracking and making multiple attempts, finding the best set of outputs generated by an LLM. It could help coders work with AI agents more efficiently.

Counter intelligence

Architecture students bring new forms of human-machine interaction into the kitchen.

MIT scientists investigate memorization risk in the age of clinical AI

New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.

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.

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.

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.