System helps humans predict when self-driving cars will make mistakes
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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Research
MIT Quantum Initiative launches postdoctoral fellowship program
The Institute welcomes its first cohort of QMIT Fellows this fall to advance interdisciplinary quantum research.
Read MoreLooking beyond natural sequences
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
Read MoreAI helps design new materials that work in the real world
The “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.
Read MoreGenerating scenarios for extreme events, without extreme data
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
Read MorePaving the way for greener ammonia production
New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that’s essential to fertilizer and other products.
Read MoreWhen AI art has no author: Study finds generated images often can’t be traced to training data
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
Read MoreWith a feel for physics, AI models simulate a wider range of real-world scenarios
“GeoPT” helps AI models understand the basics of physics so they can simulate how objects respond to things like wind and water more efficiently and accurately.
Read MoreSolving the solvent problem
By focusing on electrolytes, MIT scientists are making sodium-metal batteries a more practical energy storage option.
Read MoreThe benefits of medical AI assistance vary based on user expertise
Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.
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