Working with AI: Real Stories of Human-Machine Collaboration

Book Description

Two management and technology experts show that AI is not a job destroyer, exploring worker-AI collaboration in real-world work settings.

This book breaks through both the hype and the doom-and-gloom surrounding automation and the deployment of artificial intelligence-enabled–“smart”–systems at work. Management and technology experts Thomas Davenport and Steven Miller show that, contrary to widespread predictions, prescriptions, and denunciations, AI is not primarily a job destroyer. Rather, AI changes the way we work–by taking over some tasks but not entire jobs, freeing people to do other, more important and more challenging work. By offering detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor, Davenport and Miller also show that AI in the workplace is not the stuff of futuristic speculation. It is happening now to many companies and workers.

These cases include a digital system for life insurance underwriting that analyzes applications and third-party data in real time, allowing human underwriters to focus on more complex cases; an intelligent telemedicine platform with a chat-based interface; a machine learning-system that identifies impending train maintenance issues by analyzing diesel fuel samples; and Flippy, a robotic assistant for fast food preparation. For each one, Davenport and Miller describe in detail the work context for the system, interviewing job incumbents, managers, and technology vendors. Short “insight” chapters draw out common themes and consider the implications of human collaboration with smart systems.

中文：

书名：与人工智能合作：人机协作的真实故事

两位管理和技术专家展示了人工智能并不是工作破坏者，他们探索了现实世界工作环境中的工人-人工智能协作。

这本书打破了围绕自动化和在工作中部署人工intelligence-enabled&#8211；&#8220；smart&#8221；&#8211；systems的炒作和悲观情绪。管理和技术专家托马斯·达文波特和史蒂文·米勒表明，与普遍的预测、处方和谴责相反，人工智能主要并不是一个就业破坏者。相反，人工智能通过接管一些任务而不是全部工作来改变我们的工作方式，解放人们去做其他更重要、更具挑战性的工作 工作。通过提供从金融到工厂车间等各种环境下人工智能增强工作的详细、真实世界的案例研究，达文波特和米勒还表明，工作场所的人工智能并不是未来主义猜测的东西。现在，许多公司和员工都遇到了这种情况。

这些案例包括一个用于寿险承保的数字系统，它可以实时分析应用程序和第三方数据，使人类承保人能够专注于更复杂的病例；一个具有基于聊天的界面的智能远程医疗平台；一个通过分析柴油燃料样本来识别迫在眉睫的列车维护问题的机器学习系统；以及一个用于准备快餐的机器人助手Flippy。对于每一个，Davenport和Miller详细描述了系统的工作环境，采访了工作现任者、经理和技术供应商。简短的章节勾勒出共同的主题，并考虑人类与智能系统协作的影响。