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

For much of the last two years, multi-agent systems have been treated as the natural next step in artificial intelligence. If one large language model can reason, plan, and act, then several working ...

The deal arrives as Meta accelerates its AI investments to compete with Google, Microsoft, and OpenAI — and as the industry’s ...

Bold acquisitions in AI are no longer just about cutting-edge algorithms they’re about who controls the execution layer, and ...

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

Karthik Ramgopal and Daniel Hewlett discuss the evolution of AI at LinkedIn, from simple prompt chains to a sophisticated ...

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

AI coding agents are highly vulnerable to zero-click attacks hidden in simple prompts on websites and repositories, a ...

Step aside, LLMs. The next big step for AI is learning, reconstructing and simulating the dynamics of the real world.

Legacy metrics—uptime, latency, MTTR—no longer capture operational value in an AI-driven world. Mean time to prevention (MTTP ...

Researchers explored the nuanced dynamics of how people balance their desire to speak out vs their fear of punishment in a ...