AI agents have emerged from the lab, bringing promise and peril. A Carnegie Mellon University researcher explains what's likely next for 2026.

What happens when the growth of computing demand becomes so rapid that even the best systems become unable to match it? This became a pressing question in the year 2025, as the areas of quantum computing,

At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network trained on vast amounts of text data, including lots of programming code. It’s a pattern-matching machine that uses a prompt to “extract” compressed statistical representations of data it saw during training and provide a plausible continuation of that pattern as an output.

AI agents will reshape 2026: they’ll feed on synthetic/structured data, remake the web, swarm unpredictably, and empower prosumers.

The AI developer claims the system, set to launch in January, allows agents to complete payments with credit cards, stablecoins, and FET tokens.

The promise of artificial intelligence has moved far beyond theoretical discussions and into practical business applications. According to Emergn research, a staggering 94% of new digital products and services are projected to be created using AI within ...

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

The tool rush taught us what's possible. Now it's time to build the systems that make those possibilities scalable, & transformative.

Simultaneously, “shadow AI” is spreading as employees experiment with publicly available tools on sensitive data, often outside corporate oversight. A recent IBM report found that most enterprises still lack formal policies governing AI use, creating significant blind spots for attackers to exploit.