Seedance 2.0 today announced the release of its upgraded AI video generation system, introducing expanded multi-input capabilities that allow creators to generate cinematic video using text, images, ...

A machine learning (ML) model might retrain or drift between quarterly operational syncs. This means that, by the time an ...

A duplex speech-to-speech model changes the premise: The intelligence layer consumes audio and produces audio directly. The model can attend to what was said and how it was said—content and delivery ...

AI didn't just create new attack surfaces. It fundamentally changed who—and what—is requesting access in your environment. Zero Trust needs an upgrade for a world where autonomous agents outnumber ...

MIT researchers developed Attention Matching, a KV cache compaction technique that compresses LLM memory by 50x in seconds — ...

Traditional machine learning emphasized predictive accuracy. Generative systems required attention to hallucination mitigation and grounding. Agentic systems shift the challenge again. They do not ...

Explore the future of embedded systems development with Claude Code. Learn how AI tools could deliver high-quality code faster.

Advances in instrumentation, modeling and control are more fully understood and utilized when assisted by first-principle, ...

ByteDance's latest Seedance 2.0 release produces hyper-real outputs from fairly simple text and image outputs that blur the line between real and AI generated.

Agentic AI rarely crashes; it quietly changes its behavior, and if you’re not measuring that drift, you won’t see trouble coming.