跳到正文
只看观点#数据/训练
今天10月21日周三2 条
  1. 钛媒体40

    电商巨头的AI战略分野：阿里抢算力，京东抢扳手

    阿里与京东在AI上走向两条路：阿里三年3800亿元云和AI基建计划已过半，单季资本开支达677亿元，AI云与算力服务分部季度收入484亿元、同比增长45%；京东则宣布到2028年在机器人领域投入百亿资源、五年建80个RoboBase，并组织60万人采集1000万小时真实场景数据。文章认为阿里卖算力、京东卖物理劳动力，后者能否成为独立商业形态取决于物理运营能力能否对外按使用量收费。

  2. 雷锋网35

    一次抓取背后：Token经济学如何计量算力、模型与语料

    截至2026年6月，我国日均词元调用量已突破500万亿，Token正成为连接模型服务、推理算力与数据供给的计量接口。DeepSeek自8月起探索峰谷时段定价，9月10日调整Flash系列价格，闲时每百万词元缓存命中输入价0.02元。无问芯穹跨集群异构推理架构在特定测试中首词元等待时间降低51.5%、单词元成本降低37.5%。

10月20日周二
  1. MIT Technology Review · AI18

    企业 AI 的新经济学：从 token 成本转向有效成果成本

    HPE 提出企业 AI 的成本衡量应从 token 或 GPU 小时价格转向"每个有用成果的有效成本"，即完成可用工作所需的平台、模型、运维与算力总成本。企业可选自建栈、按需消费或集成私有 AI 平台三条路径，其中 HPE Private Cloud AI 通过调度、模型选择、GPU 分区和 KV-cache 管理提升利用率，并附 90 天前驻工程师服务。

10月2日周五
  1. Anthropic36

    哈佛物理学家 Matthew Schwartz 在 Anthropic Science Blog 客座文章中提出，LLM 与科学协作存在"阻抗失配"：LLM 能力很强，但像对待人类合作者那样使用它并非发挥其科学优势的最佳方式。为此他开发了一套用于定量科学精确计算的工具包，Claude 借此找到与生态学、群体遗传学等十几个领域的联系，Schwartz 再与领域专家合作把方向引向有价值的问题。

9月29日周二
7月7日周二
  1. Berkeley AI Research42

    智能免费之后怎么办？面向智能体、由智能体构建的数据系统

    UC Berkeley 团队提出，推理成本已从 GPT-4 级约 $30/百万 token 降至不足 $1，部分厂商低于 $0.10，基准测试中推理价格每年下降 9x 至 900x、中位数约 50x。作者据此提出三类数据系统挑战：为智能体设计的数据系统、支撑智能体群体运行的数据系统、以及由智能体合成的数据系统。

5月8日周五
  1. Berkeley AI Research36

    自适应并行推理：高效推理扩展的下一个范式

    伯克利 AI 研究团队综述并行推理进展，重点分析自适应并行推理——让模型自行决定何时分解并并行化子任务、开多少并发线程以及如何协调。现有方法如 self-consistency、Best-of-N、Tree of Thoughts、MCTS、ParaThinker、GroupThink 和 Hogwild! Inference 的并行结构多由外部设定，而非模型自主选择。

4月5日周日
  1. Andrej Karpathy60

    Karpathy 转发并点评 Farza 用 LLM 把 2,500 条日记、Apple Notes 和 iMessage 记录整理成含 400 篇文章的个人 Wiki（Farzapedia），认为这是个人化 AI 的好范例。

    引用Farza 🇵🇰🇺🇸@FarzaTV

    This is Farzapedia. I had an LLM take 2,500 entries from my diary, Apple Notes, and some iMessage convos to create a personal Wikipedia for me. It made 400 detailed articles for my friends, my startups, research areas, and even my favorite animes and their impact on me complete with backlinks. But, this Wiki was not built for me! I built it for my agent! The structure of the wiki files and how it's all backlinked is very easily crawlable by any agent + makes it a truly useful knowledge base. I can spin up Claude Code on the wiki and starting at index.md (a catalog of all my articles) the agent does a really good job at drilling into the specific pages on my wiki it needs context on when I have a query. For example, when trying to cook up a new landing page I may ask: "I'm trying to design this landing page for a new idea I have. Please look into the images and films that inspired me recently and give me ideas for new copy and aesthetics". In my diary I kept track of everything from: learnings, people, inspo, interesting links, images. So the agent reads my wiki and pulls up my "Philosophy" articles from notes on a Studio Ghibli documentary, "Competitor" articles with YC companies whose landing pages I screenshotted, and pics of 1970s Beatles merch I saved years ago. And it delivers a great answer. I built a similar system to this a year ago with RAG but it was ass. A knowledge base that lets an agent find what it needs via a file system it actually understands just works better. The most magical thing now is as I add new things to my wiki (articles, images of inspo, meeting notes) the system will likely update 2-3 different articles where it feels that context belongs, or, just creates a new article. It's like this super genius librarian for your brain that's always filing stuff for your perfectly and also let's you easily query the knowledge for tasks useful to you (ex. design, product, writing, etc) and it never gets tired. I might spend next week productizing this, if that's of interest to you DM me + tell me your usecase!