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MIT researchers designed a computationally efficient algorithm for machine learning with symmetric data that also requires fewer data for training than conventional approaches. Their work could inform the design of faster, more accurate machine-learning models for tasks like discovering new drugs or identifying astronomical phenomena.
Recent Stories
Jan 19, 2026
AI helps reveal global surge in floating algae
For the first time and with help from artificial intelligence, researchers have conducted a comprehensive study of global floating algae and found that blooms are expanding across the ocean. These trends ...
Jan 19, 2026Agent Lightning: Train ANY AI Agents with Reinforcement Learning
We present Agent Lightning, a flexible and extensible framework that enables Reinforcement Learning (RL)-based training of Large Language Models (LLMs) for any AI agent. Unlike existing methods...
Jan 19, 2026NEURA Robotics joins forces with Bosch to deploy German humanoid creations
The current CTO of NEURA formerly served in a leading position at Bosch