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2026-10-27

腾讯新闻 · AI频道18:08

How can developers capitalize on the foundational benefits of the AI era? The CEIC × 1024 Shenzhen event unveils the new infrastructure for AI and HarmonyOS development.

During the 2026 Changsha · 1024 Programmers’ Day event, the CEIC Consumer Electronics Innovation Conference and CSDN joined forces in Shenzhen to present a cutting-edge tech extravaganza for developers. As Yang Tao, Executive Deputy Director of the CEIC Organizing Committee, noted in an interview: “Ultimately, every experience innovation in the consumer electronics field is brought to life by the code written by our programmers.” At this pivotal moment in technological advancement, the evolution has moved from plain hand-written code to leveraging intelligent agents (Agents) and powerful on-device APs...

开发者如何接住AI时代的底座红利？CEIC×1024深圳专场解密AI与鸿蒙开发新基建

AI 腾讯新闻

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2026-10-22

NVIDIA Blog13:00

NVIDIA drives the transformation of Europe’s retail sector: Open models and physical intelligence are being implemented in e-commerce, warehousing, and virtual try-on.

European retailers are leveraging NVIDIA-accelerated open large models and physics‑AI technologies to achieve large‑scale deployment across scenarios such as digital product discovery, intelligent warehouse simulation, virtual try‑on, and accelerated R&D. Companies including Shopify, Zooplus, L’Oréal, and the Otto Group have used tools like the Nemotron series of models, Omniverse digital twins, and ALCHEMI molecular simulations to significantly reduce inference costs, improve search accuracy, shorten R&D cycles, and validate improvements in conversion rates and sales growth.

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2026-10-21

Hugging Face 官方博客2026-10-21

VocabX: Reduces the inference cost of domain-specific small language models by 77% through cost-weighted vocabulary specialization.

Dharma-AI has introduced the VocabX method, which reconfigures the vocabulary of language models for specific tasks—such as automated scoring of Portuguese essays—by optimizing for cost rather than word frequency. Without modifying the model architecture, parameters, or training pipeline, empirical results show a 77% reduction in inference costs, while the evaluation score (bench score) improved from 0.703 to 0.810.

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2026-07-07

Berkeley AI Research17:00

Intelligence is Free, Now What? Data Systems for, of, and by Agents

... government of the people, by the people, for the people ... — Abraham Lincoln, Gettysburg Address (1863) The cost of AI is dropping rapidly. GPT-4-class capabilities cost roughly $30 per million tokens in early 2023; today the same runs under $1, and some providers are pushing costs below $0.10. Across benchmarks, inference prices have fallen between 9x and 900x per year, with a median decline near 50x. Even frontier models are getting dramatically cheaper each generation, with open-source models following closely behind. And crucially, even if “Nobel-Prize-winning genius-level” intelligence isn’t here yet, the intelligence that suffices for the vast majority of knowledge work is here today, and getting cheaper by the month. At this rate, we are soon entering the era of virtually free

Research

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