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

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, ...

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

In the ever-evolving landscape of artificial intelligence, there is a growing interest in leveraging insights from neuroscience to create more ...

The paper challenges the dominant “mirror metaphor” that defines digital human twins as faithful replicas of human behavior ...

In multi-tier supply chains, trust is often the first casualty of complexity. Blockchain is helping to restore it by creating ...

High performance computing has entered a new phase, one where the chips inside a machine can reshape themselves around the code they are running. Instead of simply stacking more processors and drawing ...

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.

Levi Patterson lifts the lid on the sophisticated 3D-to-factory platform he's built that enables personalized designs for ...

Demonstrating and applying control theory has long presented a complex challenge, encompassing a broad range of engineering ...

Traditional water management approaches are increasingly unfit for modern pressures. Periodic manual measurements, delayed ...