Over the past decades, computer scientists have introduced numerous artificial intelligence (AI) systems designed to emulate the organization and functioning of networks of neurons in the brain.

A team at the University of Cambridge has built a memristor chip that operates on switching currents about a million times ...

Neuro-symbolic AI — combining data-driven learning with rule-based reasoning — could accelerate safe, transparent use of autonomous equipment on complex jobsites.

A new hybrid AI approach may drastically cut energy use while improving reliability. Artificial intelligence is not just changing software. It is also driving a sharp rise in electricity use. In the ...

Inside a giant autonomous warehouse, hundreds of robots dart down aisles as they collect and distribute items to fulfill a ...

This article discusses that applying principles from neuroscience, specifically neuroplasticity, can significantly enhance ...

The unpredictability of AI could lead to a future where humans lose control over AI systems. Neural networks differ ...

A machine learning approach shows promise in helping astronomers infer the internal structure of stellar nurseries from ...

Brain-inspired AI-hardware mimics neural efficiency to cut energy use, enabling autonomous devices to navigate, adapt and make real-time decisions independently.

Large language models lack grounding in physical causality — a gap world models are designed to fill. Here's how three ...

Abstract: In this paper, a generalizable Decentralized sequential multitask Assignment Learning Spiking neural network (DeALS) approach is presented for solving a Perimeter Defense Problem (PDP). In ...

More than a billion people are now using artificial intelligence (AI) models regularly, for purposes ranging from work to advice about personal relationships. This trend began with the introduction of ...