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    --- GA: UA-34467841-15 --- # 為AI 開發注入金融級穩定性:從平台建構到流程自動化的實戰經驗 - 彭士爵 ###### tags: `HelloWorld2025` `HWDC2025` `2025` `BC 會議室` `後端技術與架構` <blockquote> 從 AI 與系統整合設計、AI 工具鏈串接,到 AI 開發工具的實戰應用,本場演講將分享在銀行業內部打造 AI 平台的實務經驗。透過具體案例,我們將探討如何在高度合規與安全要求的金融環境中,引入生成式 AI、模型部署框架與自動化開發流程,讓 AI 能夠真正落地,並融入產品規劃與 DevOps 流程之中。希望能為有志於企業內部推動 AI 工程化的技術人員,帶來可借鏡的實作策略與反思。 聽眾收穫: 希望能透過互相交流一起成長 </blockquote> {%hackmd @HWDC/announcement-2025 %} ## 會議資訊 **時間:** 14:20 ~ 15:05 **地點:** BC 會議室 **日期:** 2025年10月14日 **語言:** 中文 **難度:** 中階 **相關連結:** - [Hello World Dev Conference 2025 官方網站](https://hwdc.ithome.com.tw/2025) [target=_blank] - [Hello World 2025 議程表](https://hwdc.ithome.com.tw/2025/agenda) [target=_blank] ## 筆記區 > 請從這裡開始記錄你的筆記 ### 金融業的AI挑戰 - 金融業導入與使用AI的比例不斷創新高,尤其是銀行體系 - 但AI落地專案並沒有如預期爆炸成長 - 對消費者而言,金融業對AI有「零容錯」壓力 - AI無法像WEB服務一樣犯錯後再補救 - 金融機構特有的監理要求 - 對開發者而言,AI平台化在金融業難以推動 - 金融業裡數據的封閉性導致數據孤島現象 - 種種問題造就從模型到功能的「開發地獄」現象 - 模型成功不等於可以上線 - 沒有開發資源,每個專案只能「單打獨鬥」 - 缺乏可觀測性與治理 - 我們需要一個兼顧開發與落地的 AI Platform - 「我們往往會高估技術的短期影響而又低估了它的長期影響」 ### 從自建模型走向平台能力 - 自建模型 VS. LLM/LMM - 自建模型 - 可控性高、客製化強、資料主權 - 成本高、迭代慢、知識廣度不足 - LLM - 快速上手、廣泛知識、持續更新 - 可控性低、隱私顧慮、成本不確定 - 相對於自建模型, LLM 速度比較慢, 故最後還是選自建模型 - 解離資料與模型,利用RAG降低Overfitting - 後自建模型時代的衝擊 - 在少樣本、零樣本場景下 - 大語言模型可取得接近乃至達到傳統監督學習方式 - 大語言模型帶來的挑戰與機會 - 新AI平台的核心設計思路 - 多模型開發下的GPU隊列 - 多模態的知識圖譜 - 基於 GraphRAF 之上導入 MMKG (Multi-Modal Knowledge Graphs) - 用 graph 來解決 overfitting 問題 - 多腦協同合作 / AI Agent - A2A(Agent to Agent) & MCP protocols - 每一個 Agent 都是一個 Node, 確保可以監控. - AI Framework:B.R.A.I.N. - Bussiness Retrieval Augmented Intelligence Network - 新AI平台的核心功能成型 - GPU資源池 - 多模態能力的導入 - Mixture of Recursions (MoR) 導入 - MoR的概念與用途 - 不同任務使用不同的 Sub-Chain,不是單一prompt處理一切 - 每一層可以遞迴調用 - MoR在平台上的實作架構 - 每個任務由專責的 Module 處理(Retrieval、理解、合成、驗證、記錄) - 實際應用場景 - 長文件閱讀、多欄位表格解析、合約比對分析 - 拆解成 micro skill,每段遞迴處理再組合 - MoE v.s. MoR: Structure - MoE scales width with experts - MoR scales depth with recursions - MoR 方法論形成 BRAIN 決策流程自動化 - MoR 強調多層遞迴、調度、混合策略 ### 平台架構與設計實踐詳解 - 平台設計架構堆疊 - BRAIN分層設計 - 硬替資源層 - 資料圖譜層 - 認知引擎層 - 任務編排層 - 智能算力流動池 - 算力單元適配器 - 優先隊列與算力池適配器 - 混合思維中樞 - 知識建構圖解 - 知識重組 - 認知決策中樞 - 建構方法論 - 將「業務邏輯」與「API適配邏輯」徹底分離 ### 金融業AI最終的未來 - 結論:假的,一切都是業障 ## 討論區 > 歡迎在此進行討論與 Q&A ## 相關資源 - 投影片連結:(待講者提供) - 相關文件:(待更新)

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