Computer science and technology
Computer science and technology

MIT-IBM Watson AI Lab seed to signal: Amplifying early-career faculty impact

Academia-industry relationship is an early-stage accelerator, supporting professional progress and research.

Can AI help predict which heart-failure patients will worsen within a year?

Researchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient’s heart failure prognosis up to a year in advance.

3 Questions: On the future of AI and the mathematical and physical sciences

Professor Jesse Thaler describes a vision for a two-way bridge between artificial intelligence and the mathematical and physical sciences — one that promises to advance both.

A better method for planning complex visual tasks

A new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.

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.

A “ChatGPT for spreadsheets” helps solve difficult engineering challenges faster

The approach could help engineers tackle extremely complex design problems, from power grid optimization to vehicle design.

New method could increase LLM training efficiency

By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.

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.

AI to help researchers see the bigger picture in cell biology

By providing holistic information on a cell, an AI-driven method could help scientists better understand disease mechanisms and plan experiments.

Exposing biases, moods, personalities, and abstract concepts hidden in large language models

A new method developed at MIT could root out vulnerabilities and improve LLM safety and performance.