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.

This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

Meet NVIDIA Nitrogen, a generalist gaming agent trained on 40,000 hours of video, so you can understand how imitation learning scales.

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

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, ...

Nemotron-3 Nano (available now): A highly efficient and accurate model. Though it’s a 30 billion-parameter model, only 3 billion parameters are active at any time, allowing it to fit onto smaller form ...

In a first-of-its-kind study, scientists found that bumblebees can tell the difference between short and long light flashes, much like recognizing Morse code. The insects learned which signal led to a ...

Learn how to effectively read and understand deep learning code with this beginner-friendly guide. Break down complex scripts and get comfortable navigating AI projects step by step. #DeepLearning ...

Watch an AI agent learn how to balance a stick—completely from scratch—using reinforcement learning! This project walks you through how an algorithm interacts with an environment, learns through trial ...

* Cursor, the AI-native code editor, recently reported that it writes nearly a billion lines of code daily. That’s one billion lines of production-grade code accepted by users every single day. If we ...