Guided learning lets “untrainable” neural networks realize their potential
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
National Science Foundation (NSF)
Enabling small language models to solve complex reasoning tasks
The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.
Read MoreThe technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.
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MIT engineers design an aerial microrobot that can fly as fast as a bumblebee
With insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.
Read MoreResearchers 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.
Read MoreTeaching robots to map large environments
A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.
Read More3 Questions: How AI is helping us monitor and support vulnerable ecosystems
MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.
Read MoreImproving the workplace of the future
Economics doctoral student Whitney Zhang investigates how technologies and organizational decisions shape labor markets.
Read MoreWith SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.
Read MoreA new generative AI approach to predicting chemical reactions
System developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.
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