Richard Socher's new $650 million startup wants to build an AI that can research and improve itself indefinitely — and he ...

Recursive Superintelligence, founded by former Google, Meta and OpenAI researchers, is part of a growing effort to automate ...

Weekly cybersecurity recap covering zero-days, malware, phishing, supply chain attacks, cloud threats, AI security risks, and ...

On April 30, two releases of one of the most popular machine learning libraries on the Python Package Index were caught ...

A single line of Python code was all it took. Developers who ran import lightning after installing versions 2.6.2 or 2.6.3 of ...

Self-evolving AI agents are reshaping how artificial intelligence systems learn and adapt, allowing them to autonomously refine their skills and performance over time. AI Jason explores the mechanisms ...

The Palo Alto startup, spun out of Ohio State University by Yu Su, argues that current agents complete tasks as intended only half the time, a reliability gap it plans to close by giving agents a ...

Plus: An unauthorized group has reportedly accessed Anthropic’s Mythos. This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of ...

NeoCognition, a startup developing self-learning artificial-intelligence agents, emerged from stealth with $40 million in seed funding and backing from Intel's chief executive officer. The round was ...

Investors are aggressively courting AI researchers to build startups that can make AI more reliable and efficient. Yu Su, an Ohio State professor leading an AI agent lab, said he initially resisted ...

If it feels like AI is developing too fast to keep up with, a group of Chinese researchers have some bad news – because they've developed a model that "evolves" on its own, creating better versions of ...

Creating self-improving AI systems is an important step toward deploying agents in dynamic environments, especially in enterprise production environments, where tasks are not always predictable, nor ...