Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Today's AI agents don't meet the definition of true agents. Key missing elements are reinforcement learning and complex memory. It will take at least five years to get AI agents where they need to be.

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

AgiBot announced a key milestone this week with the successful deployment of its Real-World Reinforcement Learning system in a manufacturing pilot with Longcheer Technology. The pilot project marks ...

How can a small model learn to solve tasks it currently fails at, without rote imitation or relying on a correct rollout? A team of researchers from Google Cloud AI Research and UCLA have released a ...

Reinforcement learning (RL) is machine learning (ML) in which the learning system adjusts its behavior to maximize the amount of reward and minimize the amount of punishment it receives over time ...

Abstract: In the digital realm, ensuring the security and reliability of systems and software is of paramount importance. Fuzzing has emerged as one of the most effective testing techniques for ...

What if the very techniques we rely on to make AI smarter are actually holding it back? A new study has sent shockwaves through the AI community by challenging the long-held belief that reinforcement ...