Why it’s critical to move beyond overly aggregated machine-learning metrics
New research detects hidden evidence of mistaken correlations — and provides a method to improve accuracy.
New research detects hidden evidence of mistaken correlations — and provides a method to improve accuracy.
While the growing energy demands of AI are worrying, some techniques can also help make power grids cleaner and more efficient.
Read MoreNew research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
Read MoreThe technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.
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.
Read MoreMIT CSAIL and LIDS researchers developed a mathematically grounded system that lets soft robots deform, adapt, and interact with people and objects, without violating safety limits.
Read MoreLarge language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
Read MoreMIT 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.
Read MoreA 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.
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