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.
Read MoreAn AI-driven system lets users design and build simple, multicomponent objects by describing them with words.
Read MoreAssistant Professor Yunha Hwang utilizes microbial genomes to examine the language of biology. Her appointment reflects MIT’s commitment to exploring the intersection of genetics research and AI.
Read MoreThe approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease.
Read MoreThe “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.
Read MoreFounded by MIT alumni, the Pickle Robot Company has developed machines that can autonomously load and unload trucks inside warehouses and logistic centers.
Read MoreThis new technique enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question.
Read MoreWith insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.
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