Overview: Clear problem definitions prevent wasted effort and keep machine learning work focused.Clean, well-understood data ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

Artificial intelligence (AI) is transforming our world, but within this broad domain, two distinct technologies often confuse people: machine learning (ML) and generative AI. While both are ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...

Adam M. Root argues businesses must anchor ML in customer problems, not technology. He details a strategy using ...

Overview: Machine learning failures usually start before modeling, with poor data understanding and preparation.Clean data, ...

TrialTranslator uncovers the survival gap for high-risk patients and offers a path to better cancer research. Study: Evaluating generalizability of oncology trial results to real-world patients using ...