A new large-scale effort to map and evaluate AI-powered learning tools finds some areas where the technology has promise for ...

Learn how physical AI deployment concerns like unpredictability, training data quality, compute limitations, safety and cost overruns can be resolved.

AI startup Magic published a pretraining recipe on September 8, 2026, that matched DeepSeek V4 Pro base model quality using ...

OpenAI has appointed AI alignment pioneer Paul Christiano to its Foundation board, adding RLHF creator and former NIST ...

Beijing University of Technology scientists have improved maneuverability algorithms to conduct cold war in space-style ...

One of the realities of leadership development is that some things can only be learned through experience. That’s becoming an ...

People have been talking to each other for at least 100,000 years, as best we can tell. And in all that time, there has been only one thing in the world that could learn a human language to perfect ...

Teaching an AI to write code that works is one thing. Teaching it to write code that works fast is, apparently, a completely different beast. A new paper from Meta AI’s FAIR team, published July 29, ...

European Commission Executive Vice-President Henna Virkkunen discusses the European Union's approach to AI policy at the Stanford University's Institute for Human-Centered AI in Stanford, California, ...

Real-world robotic manipulation in homes and factories demands reliability, efficiency, and robustness that approach or surpass skilled human operators. We present a real-world reinforcement learning ...

Dexterous manipulation is a crucial yet highly complex challenge in humanoid robotics, demanding precise, adaptable, and sample-efficient learning methods. As humanoid robots are usually designed to ...

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...