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.

A practical guide to the four strategies of agentic adaptation, from "plug-and-play" components to full model retraining.

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 ...

At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network ...

OpenAI says prompt injections will always be a risk for AI browsers with agentic capabilities, like Atlas. But the firm is ...

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, ...

Nvidia Corp. today announced the launch of Nemotron 3, a family of open models and data libraries aimed at powering the next generation of agentic artificial intelligence operations across industries.

In an effort to teach self-management to students identified as impulsive, Meichenbaum found that he could help students control and manage their impulsive behaviors.

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 ...

Abstract: Reinforcement learning (RL) is renowned for its proficiency in modeling sequential tasks and adaptively learning latent data patterns. Deep learning models have been extensively explored and ...

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 ...