FW-HTF-P/Collaborative Research: Exploring Tools to Help Workers and Organizations Adapt to AI-enabled Robots

FW-HTF-P/协作研究：探索帮助工人和组织适应人工智能机器人的工具

• 批准号: 2114791
• 负责人: Erik Brynjolfsson
• 金额: $ 7.72万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114791&HistoricalAwards=false
• 关键词: FW; HTF; Collaborative; Research; Exploring

This project will promote exploration of scalable tools to aid workers and organizations adapt to artificially-intelligent robots. In a sharp departure from current robotic systems that have to be programmed for a single manipulation task in a very tightly constrained set of conditions, venture-funded firms are designing and beginning to test qualitatively new robotic technologies that promise to flexibly automate entire classes of embodied tasks in widely divergent conditions. In this likely future, robots will adapt as readily to new repetitive manual tasks as a modern microprocessor adapts to new computational tasks. Such "learning" robots would clearly have profound implications for workers and organizations, but previous research on automation offers only limited guidance on how they will adapt. The researchers have recently begun a nationwide, four-year field study that will identify edge cases in which organizations and low-skill workers achieve unlikely yet systematic success, given the introduction of this disruptive technology. This will allow deriving design constraints for potential solutions from grounded theory, centering on the hard-won, demonstrably successful innovations of a suitably-diverse pool of informants. While existing research stands to unveil the mechanisms behind rare, in vivo learning successes to the world, this FW-HTF-P (Future of Work at the Human-Technology Frontier - Planning) award will assemble a world-class team of researchers who are committed to trying to expand and capitalize upon these mechanisms via new tools. This research has high-impact potential for organizations, lower-skilled workers and policy makers on how to expand and enrich work involving increasingly intelligent systems in the 21st century.With AI in robotics as the technology, humans collaborating with robots as the workers, and organizations employing both the robots and the workers as the context of work, the team of researchers will specifically contact and convene a group of top experts in diverse technical domains including social media, massive open online courseware, crowdsourced knowledge repositories, peer assessment and coaching, user experience design and platforms for on-demand labor, crowdsourcing and innovation challenge execution. Beyond these technical disciplines, the researchers will invite policymakers and technologists, as the pathways to local success will likely be deeply intertwined with legal and commercialization processes. The researchers will begin by sharing very preliminary findings, research questions and objectives from the current study with a select group of such researchers who may have interest in a potential collaboration. The researchers will then extend formal invitations to a workshop to no more than ten potential collaborators. This workshop will be one day in length and will be described as an opportunity to explore and decide upon potential collaborative opportunities related to helping workers and organizations adapt more productively to general-purpose robots. The researchers will explore potentially new organizational theories that take perspectives such as: (a) accounting for success as a learning problem in which robots, workers and organizations learn from each other; (b) the character of learning infrastructures evident in various practices for adapting to learning machines acting as co-workers; (c) how the organization of such learning practices impacts skill changes, role transformations, as well as workers and organizations. The researchers will then solicit participants' input and commitment for tools to scale the successes inherent in the findings and select the tool likely to have the greatest benefit for the most Americans. The researchers will then jointly craft an FW-HTF-R (Future of Work at the Human-Technology Frontier - Research) proposal with interested collaborators that reflects a rigorous test of this tool in real-world settings. The ultimate goal of this project is to develop the necessary research personnel, research infrastructure, and foundational work to expand the opportunities for studying future technology, future workers, and future work at the level of a FW-HTF full research proposal.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将促进对可扩展工具的探索，以帮助工人和组织适应人工智能机器人。与目前必须在非常严格的条件限制下为单一操作任务编程的机器人系统截然不同，风险投资公司正在设计并开始测试新的机器人技术，这些技术有望在广泛不同的条件下灵活地自动化整个类别的具体任务。在这个可能的未来，机器人将像现代微处理器适应新的计算任务一样容易适应新的重复性手动任务。这种“学习”机器人显然会对工人和组织产生深远的影响，但以前关于自动化的研究只提供了有限的指导，说明它们将如何适应。研究人员最近开始了一项为期四年的全国性实地研究，该研究将确定一些边缘案例，在这些案例中，组织和低技能工人在引入这种颠覆性技术后取得了不太可能但系统性的成功。这将允许从扎根理论中推导出潜在解决方案的设计约束，集中在来之不易的，证明成功的创新的一个适当多样化的信息池。虽然现有的研究将向世界揭示罕见的体内学习成功背后的机制，但这个FW-HTF-P（人类技术前沿规划工作的未来）奖将汇集一个世界级的研究人员团队，他们致力于通过新工具扩展和利用这些机制。这项研究对组织、低技能工人和政策制定者在21世纪如何扩大和丰富涉及日益智能化的系统的工作具有很大的影响潜力。以机器人中的人工智能为技术，人类与机器人合作作为工人，以及同时使用机器人和工人作为工作背景的组织，研究人员团队将专门联系并召集一组不同技术领域的顶级专家，包括社交媒体，大规模开放式在线课件，众包知识库，同行评估和指导，用户体验设计和按需劳动力平台，众包和创新挑战执行力。除了这些技术学科之外，研究人员还将邀请政策制定者和技术专家，因为地方成功的途径可能与法律的和商业化进程密切相关。研究人员将开始分享非常初步的发现，研究问题和目标，从目前的研究与选定的一组这样的研究人员谁可能有兴趣在一个潜在的合作。然后，研究人员将正式邀请不超过10名潜在的合作者参加研讨会。该研讨会为期一天，将被描述为一个探索和决定与帮助工人和组织更有效地适应通用机器人相关的潜在合作机会的机会。研究人员将探索潜在的新的组织理论，这些理论的视角包括：（a）将成功解释为一个学习问题，在这个问题中，机器人、工人和组织相互学习;（B）学习基础设施的特征在各种实践中很明显，以适应学习机器作为同事的行为;（c）组织这种学习做法如何影响技能变化、角色转变以及工人和组织。然后，研究人员将征求参与者对工具的投入和承诺，以扩大研究结果中固有的成功，并选择可能对大多数美国人产生最大利益的工具。然后，研究人员将与感兴趣的合作者共同制定FW-HTF-R（人类技术前沿研究的未来工作）提案，该提案反映了该工具在现实世界中的严格测试。该项目的最终目标是培养必要的研究人员、研究基础设施和基础工作，以扩大在FW-HTF完整研究提案水平上研究未来技术、未来工作人员和未来工作的机会。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Working With Robots in a Post-Pandemic World

在大流行后的世界中与机器人一起工作

• 发表时间: 2021
• 期刊: MIT Sloan Management Review
• 影响因子: 4.2
• 作者: Beane, Matthew;Brynjolfsson, Erik
• 通讯作者: Brynjolfsson, Erik