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.
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TipRanks Advanced Micro Devices (AMD) may no longer be categorized as a traditional value investment; however, it remains a compelling […]
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Viktoria Bosak Artificial intelligence (AI) in health care is rapidly advancing beyond traditional applications. Autonomous AI agents are gaining significant […]
