- 對人工智慧的基本瞭解和 Machine Learning
- 熟練使用Python程式設計
- 自然語言處理概念的經驗
觀眾
- 數據科學家
- 機器學習工程師
- 人工智慧研究人員和開發人員
- 產品經理和用戶體驗設計師
小型語言模型 （SLM） 是緊湊而強大的工具，用於在各種應用程式（包括對話式 AI 和客戶服務機器人）中實現複雜的人機交互。
這種由講師指導的現場培訓（在線或現場）面向中級數據科學家、機器學習和 AI 研究人員，他們希望使用小型語言模型創建引人入勝且高效的 AI 驅動的對話體驗。
在培訓結束時，參與者將能夠：
- 了解對話式 AI 的基礎知識和 SLM 的作用。
- 設計和實現以使用者為中心的 AI 交互。
- 開發和訓練用於互動式應用程式的 SLM。
- 使用適當的指標評估和提高人機通信的有效性。
- 在實際場景中部署可擴展且符合道德規範的 AI 驅動的對話介面。
課程形式
- 互動講座和討論。
- 大量的練習和練習。
- 在現場實驗室環境中動手實施。
課程自定義選項
- 如需申請本課程的定製培訓，請聯繫我們進行安排。
Conversational AI 和小型語言模型 （SLM） 簡介
- 對話式 AI 的基礎知識
- SLM概述及其優勢
- SLM在互動式應用中的案例研究
設計會話流
- 人機交互設計原理
- 製作引人入勝且自然的對話
- 用戶體驗 （UX） 注意事項
構建客戶服務機器人
- 客戶服務機器人的用例
- 將 SLM 整合到客戶服務平臺中
- 使用 AI 處理常見的客戶查詢
用於交互的訓練 SLM
- 對話式 AI 的數據收集
- 對話系統中可持續土地地雷的培訓技術
- 針對特定交互場景微調模型
評估交互品質
- 用於評估對話式 AI 的指標
- 用戶測試和反饋收集
- 基於評估的反覆運算改進
語音交互和多模態交互
- 將語音辨識與 SLM 相結合
- 設計多模態互動（文字、語音、視覺）
- 語音助手和聊天機器人的案例研究
個人化和情境理解
- 個性化交互的技術
- 上下文感知對話處理
- 個人化 AI 中的隱私和數據安全
倫理考量和偏見緩解
- 對話式 AI 的道德框架
- 識別和減輕互動中的偏見
- 確保人工智慧通信的包容性和公平性
部署和擴展
- 部署對話式 AI 系統的策略
- 擴展 SLM 以廣泛使用
- 部署後監控和維護 AI 交互
頂點專案
- 確定所選領域對對話式 AI 的需求
- 使用 SLM 開發原型
- 測試和演示互動式應用程式
最終評估
- 提交頂點項目報告
- 功能性對話式 AI 系統演示
- 基於創新、用戶參與和技術執行的評估
摘要和後續步驟
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