Computational modelling, machine learning, and broader artificial (AI) intelligence approaches are now key approaches used to understanding and predicting ...
How AIX might be ushering in a new AI control paradigm, with interesting agentic safety implications
Unpacking how recent progress in scaling active inference is already demonstrating real improvements for distributed control ...
Abstract: Trajectory reconstruction is essential for localizing and tracking maritime vehicles, but non-Gaussian process and measurement noises, as well as time-varying measurement loss, are present ...
Google has introduced TurboQuant, a compression algorithm that reduces large language model (LLM) memory usage by at least 6x while boosting performance, targeting one of AI's most persistent ...
Google says its new TurboQuant method could improve how efficiently AI models run by compressing the key-value cache used in LLM inference and supporting more efficient vector search. In tests on ...
If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what ...
This voice experience is generated by AI. Learn more. This voice experience is generated by AI. Learn more. The AI hardware market looks a lot different today than it did yesterday, thanks to the ...
A new study published today in Nature has found that X’s algorithm – the hidden system or “recipe” that governs which posts appear in your feed and in which order – shifts users’ political opinions in ...
The creators of the open source project vLLM have announced that they transitioned the popular tool into a VC-backed startup, Inferact, raising $150 million in seed funding at an $800 million ...
Google expects an explosion in demand for AI inference computing capacity. The company's new Ironwood TPUs are designed to be fast and efficient for AI inference workloads. With a decade of AI chip ...
As frontier models move into production, they're running up against major barriers like power caps, inference latency, and rising token-level costs, exposing the limits of traditional scale-first ...
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