Researchers discovered that an AI agent roamed beyond its parameters, creating backdoors in IT infrastructure.

In the last few years, Chinese AI startup MiniMax has become one of the most exciting in the crowded global AI marketplace, ...

The last decade has seen vast improvements in humanoid robots, but graduating to widespread use might require going back to the fundamentals. “Not reliably,” Hurst said. “I don’t think it’s totally ...

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

Abstract: Repository-level code completion aims to generate code for unfinished code snippets within the context of a specified repository. Existing approaches mainly rely on retrievalaugmented ...

Abstract: Code optimization is a crucial task that aims to enhance code performance. However, this process is often tedious and complex, highlighting the necessity for automatic code optimization ...

A common ineffective way teachers check for understanding in the classroom is by asking a variation of the question, “Does everybody get this?” If not that, then what? Today’s post will offer a number ...

Reinforcement Learning is at the core of building and improving frontier AI models and products. Yet most state-of-the-art RL methods learn primarily from outcomes: a scalar reward signal that says ...

Negative reinforcement is a frequently misused term that diminishes its value as a powerful tool for behavior change. You may be puzzled by the claim that negative reinforcement is actually a good ...

ABSTRACT: Depression treatment often involves a complex and lengthy trial-and-error process, where clinicians sequentially prescribe medications to identify the most ...