Scientists at Massachusetts Institute of Technology have devised a way for large language models to keep learning on the fly—a step toward building AI that continually improves itself.

'Periodic table of machine learning' could fuel AI discovery Researchers have created a unifying framework that can help scientists combine existing ideas to improve AI models or create new ones ...

Google’s AI agent Big Sleep, first revealed last year, has recently uncovered a security flaw (CVE-2025-6965) in SQLite that ...

An artificial-intelligence model did something last month that no machine was ever supposed to do: It rewrote its own code to avoid being shut down. Nonprofit AI lab Palisade Research gave OpenAI ...

The era of predictive modeling enhanced with machine learning and artificial intelligence (AI) to aid clinical ...

Margaret Mitchell, an AI ethics researcher at Hugging Face, tells WIRED about a new dataset designed to test AI models for bias in multiple languages.

Just as people from different countries speak different languages, AI models also create various internal "languages"—a ...

Google's Big Sleep AI has advanced from finding bugs to proactively foiling an imminent exploit, a major leap in AI-driven ...

Hongwei Li’s team from China University of Geosciences published a research article titled “scSCC: A swapped contrastive ...

If data used to train artificial intelligence models for medical applications, such as hospitals across the Greater Toronto Area, differs from the real-world data, it could lead to patient harm. A ...

An analysis by Epoch AI, a nonprofit AI research institute, suggests that the AI industry may not be able to eke massive gains out of 'reasoning' AI models for much longer.