- 对人工智能的基本了解和 Machine Learning
- 熟练使用Python编程
- 自然语言处理概念的经验
观众
- 数据科学家
- 机器学习工程师
- 人工智能研究人员和开发人员
- 产品经理和用户体验设计师
小型语言模型 （SLM） 是紧凑而强大的工具，用于在各种应用程序（包括对话式 AI 和客户服务机器人）中实现复杂的人机交互。
这种由讲师指导的现场培训（在线或现场）面向中级数据科学家、机器学习和 AI 研究人员，他们希望使用小型语言模型创建引人入胜且高效的 AI 驱动的对话体验。
在培训结束时，参与者将能够：
- 了解对话式 AI 的基础知识和 SLM 的作用。
- 设计和实现以用户为中心的 AI 交互。
- 开发和训练用于交互式应用程序的 SLM。
- 使用适当的指标评估和提高人机通信的有效性。
- 在实际场景中部署可扩展且符合道德规范的 AI 驱动的对话界面。
课程形式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在现场实验室环境中动手实施。
课程自定义选项
- 如需申请本课程的定制培训，请联系我们进行安排。
Conversational AI 和小型语言模型 （SLM） 简介
- 对话式 AI 的基础知识
- SLM概述及其优势
- SLM在交互式应用中的案例研究
设计会话流
- 人机交互设计原理
- 制作引人入胜且自然的对话
- 用户体验 （UX） 注意事项
构建客户服务机器人
- 客户服务机器人的用例
- 将 SLM 集成到客户服务平台中
- 使用 AI 处理常见的客户查询
用于交互的训练 SLM
- 对话式 AI 的数据收集
- 对话系统中可持续土地地雷的培训技术
- 针对特定交互场景微调模型
评估交互质量
- 用于评估对话式 AI 的指标
- 用户测试和反馈收集
- 基于评估的迭代改进
语音交互和多模态交互
- 将语音识别与 SLM 相结合
- 设计多模态交互（文本、语音、视觉）
- 语音助手和聊天机器人的案例研究
个性化和情境理解
- 个性化交互的技术
- 上下文感知对话处理
- 个性化 AI 中的隐私和数据安全
伦理考量和偏见缓解
- 对话式 AI 的道德框架
- 识别和减轻互动中的偏见
- 确保人工智能通信的包容性和公平性
部署和扩展
- 部署对话式 AI 系统的策略
- 扩展 SLM 以广泛使用
- 部署后监控和维护 AI 交互
顶点项目
- 确定所选领域对对话式 AI 的需求
- 使用 SLM 开发原型
- 测试和演示交互式应用程序
最终评估
- 提交顶点项目报告
- 功能性对话式 AI 系统演示
- 基于创新、用户参与和技术执行的评估
摘要和后续步骤
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