Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...

Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.

Humans possess a remarkable balance between stability and flexibility, enabling them to quickly establish new plans and ...

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. In today’s column, I will identify and discuss an important AI ...

Reinforcement Learning from Human Feedback (RLHF) has emerged as a crucial technique for enhancing the performance and alignment of AI systems, particularly large language models (LLMs). By ...

Two trailblazing computer scientists have won the 2024 Turing Award for their work in reinforcement learning, a discipline in which machines learn through a reward ...

Artificial Intelligence (AI) has achieved remarkable successes in recent years. It can defeat human champions in games like Go, predict protein structures with high accuracy, and perform complex tasks ...

Patronus AI unveiled “Generative Simulators,” adaptive “practice worlds” that replace static benchmarks with dynamic reinforcement-learning environments to train more reliable AI agents for complex, ...

Reinforcement Learning does NOT make the base model more intelligent and limits the world of the base model in exchange for early pass performances. Graphs show that after pass 1000 the reasoning ...

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AI methods are increasingly being used to improve grid reliability. Physics-informed neural networks are highlighted as a ...