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.
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MIT Schwarzman College of Computing
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.
Read More3 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.
Read MoreNew MIT class uses anthropology to improve chatbots
MIT computer science students design AI chatbots to help young users become more social, and socially confident.
Read MoreA 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.
Read MoreImproving 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.
Read MoreA “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.
Read MoreNew method could increase LLM training efficiency
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
Read MoreMixing 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.
Read MoreAI 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.
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