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.
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.
Nuclear waste continues to be a bottleneck in the widespread use of nuclear energy, so doctoral student Dauren Sarsenbayev is developing models to address the problem.
The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.
Postdoc Zongyi Li, Associate Professor Tess Smidt, and seven additional alumni will be supported in the development of AI against difficult problems.
This new technique enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question.
MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.
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.
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.
The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.