When RL is paired with human oversight, teams can shape how systems learn, correct course when context changes, and ensure ...

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...

Learn how to build a digit recognition model from scratch using PyTorch! This beginner-friendly deep learning project walks you through loading the MNIST dataset, creating a neural network, training ...

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.

We introduce DeepQuantum, an open-source, PyTorch-based software platform for quantum machine learning and photonic quantum computing. This AI-enhanced framework enables efficient design and execution ...

Abstract: End-to-end navigation strategies using reinforcement learning (RL) can improve the adaptability and autonomy of Autonomous ground vehicles (AGVs) in complex environments. However, RL still ...

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 ...