2026年10月23日 星期五 7 分钟前更新 711 篇报道 来源 RSS

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人工智能 — 711 篇报道

Google 人工智能 全文

Co-creating the future of fashion with Google

Google worked side-by-side with designers Jane Wade and Sergio Hudson to custom-design Google Flow tools to prep for NYFW.

Co-creating the future of fashion with Google

Towards Data Science 人工智能

Your Six AI Models Are Really Two: The Math of Correlated Mistakes

Ask several models the same question, take a vote, and you get fewer independent opinions than you paid for. One formula says how many, and I checked it on six open models and 1,500 exam questions. The post Your Six AI…

MarkTechPost 人工智能

Liquid AI Launches RIPL, a Real-Time Inference Engine for Sub-50 ms LFMs

Liquid AI released RIPL, a real-time inference engine for its LFM models. On one B200, LFM2.5-1.2B on RIPL showed 14.4 to 28.4x lower p95 latency than Qwen3.5-0.8B on vLLM. The post Liquid AI Launches RIPL, a Real-Time…

KDnuggets 人工智能 全文

Google Open-Sources Harness²: Self-Improving AI Agents Without Retraining

Google Research has open-sourced Harness² (Harness2), a framework that lets AI agents improve themselves without retraining the underlying model. Instead of touching model weights, Harness² rewrites the harness around the model: its…

Google Open-Sources Harness²: Self-Improving AI Agents Without Retraining

MIT News 人工智能 全文

Giving AI a “crystal clear” view of the materials world

MIT researchers created an AI model that can identify optimal crystalline materials for various applications. The model combines 3D atomic structures with natural language descriptions to characterize crystal structures from research…

Giving AI a “crystal clear” view of the materials world

Meta Engineering 人工智能 全文

How Meta Built Forum With AI-Native Engineering

Meta’s newest app, Forum, is for anyone who wants to go deeper into the conversations in their Facebook groups. To build Forum, Meta engineers adopted a whole new way of working by bringing a…

How Meta Built Forum With AI-Native Engineering

Microsoft Research 人工智能 全文

Proof-based general-purpose computing with Magnetar

Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and…

Proof-based general-purpose computing with Magnetar
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