[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-ibm":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"988fa724-e819-4d24-8b25-2d1c1e989afe","IBM","ibm",6,"IBM 這個標籤聚焦企業 AI、向量檢索、RAG 與量子運算等技術動向。從單機百億向量索引到多代理系統與治理議題，都在看 IBM 如何把研究成果推向可部署的企業場景。","IBM here covers the company’s work on enterprise AI infrastructure, including high-scale vector indexing, RAG orchestration, agentic systems, and quantum computing. The common thread is making advanced AI practical, governable, and efficient in real deployments.",[12,21,28,36,44,51],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"8fc8bba8-e928-4e64-843e-179161c84453","ibm-think-2026-control-over-ai-zh","為什麼 IBM Think 2026 其實是在賣控制，不是在賣 AI","IBM Think 2026 的重點不是模型新鮮度，而是用治理、整合與控制，把企業 AI 變成可落地的系統。","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778213446734-4vqz.png","zh","2026-05-08T04:10:26.473593+00:00",{"id":22,"slug":23,"title":24,"summary":25,"category":17,"image_url":26,"cover_image":26,"language":19,"created_at":27},"13153bc9-0e9b-4b2a-b8e9-b6a6d60545ce","ibm-bob-enterprise-ai-harder-test-zh","為什麼 IBM 的 Bob 證明企業 AI 需要更 سخت的考題","IBM 的 Bob 說明企業 AI 不能只看 demo 與內部效率，還得通過真實流程、安全審查與成本壓力。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777861847379-yj1h.png","2026-05-04T02:30:20.318787+00:00",{"id":29,"slug":30,"title":31,"summary":32,"category":33,"image_url":34,"cover_image":34,"language":19,"created_at":35},"6510a804-74fd-4073-9c73-a1b4d3dc491c","ibm-100b-vector-database-single-server-zh","IBM 單機塞進 1000 億向量","IBM 宣稱 CAS 原型在單一伺服器上索引 1000 億向量，平均延遲 694 毫秒、召回率超過 90%。這篇拆解它怎麼做、跟一般向量資料庫差在哪、以及對企業 RAG 架構的影響。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776125936277-ct7n.png","2026-04-14T00:18:35.333469+00:00",{"id":37,"slug":38,"title":39,"summary":40,"category":41,"image_url":42,"cover_image":42,"language":19,"created_at":43},"7f3d9a39-815e-49c4-aced-a5454e7b4afa","what-openrag-does-for-enterprise-ai-zh","OpenRAG 在企業 AI 的用途","IBM OpenRAG 把檢索、索引和模型協調包成一套。適合用公司內部資料做 RAG，讓回答更貼近文件，也更好追查來源。","tools","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775164554067-i4oe.png","2026-04-02T21:15:38.707251+00:00",{"id":45,"slug":46,"title":47,"summary":48,"category":17,"image_url":49,"cover_image":49,"language":19,"created_at":50},"fe2c4a1c-15e0-48e1-93db-1a818730bf54","what-big-tech-expects-from-ai-in-2026-zh","2026 大科技怎麼看 AI","2026 年 AI 的主線越來越清楚：Agent 進入正式環境、資安與治理變成產品本體，醫療與製造先吃到紅利，基礎設施與量子運算也開始搶戲。從 Anthropic、Google 到 Microsoft，八份預測其實都在講同一件事。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1774519831083-avgd.png","2026-03-26T08:24:22.74605+00:00",{"id":52,"slug":53,"title":54,"summary":55,"category":17,"image_url":56,"cover_image":56,"language":19,"created_at":57},"b17b5b59-66b9-422e-9e36-99599aa614b5","ai-and-tech-trends-to-watch-in-2026-zh","2026 科技趨勢：AI 進入實戰","IBM 對 2026 的觀察很直接：多代理系統會開始進入正式環境，AI 硬體焦點從堆算力轉向效率，量子運算也要面對一次可驗證的實際考驗。重點不再是最大模型，而是能不能在企業裡穩定、便宜、可治理地跑起來。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1774517182966-phe7.png","2026-03-26T07:59:50.711356+00:00"]