人工智能辅助的战略规划场景生成

Matthew J. Spaniol, Nicholas J. Rowland
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引用次数: 2

摘要

这篇“实践的反思”文章探讨了新兴的人工智能工具对未来和远见实践界的一些影响。作者提供了这些新兴的人工智能工具的背景,并特别强调场景,探索了一种名为“聊天生成预训练转换器”(以下简称ChatGPT)的特定工具。作者研究了人工智能(AI)生成的场景的效用,并探讨未来和远见实践界是否应该有选择地接受人工智能的进步,以帮助生成场景。特别是,作者将考虑(1)使用完全由人工智能生成的场景的效用,(2)根据传统定义,生成的东西实际上是否构成场景,以及(3)评估使用人工智能辅助生成场景的效用。在这个时候,人工智能工具现在可以在用户基本上为零成本的情况下,就看似任何主题生成许多场景。尽管如此,作者坚持认为,这些场景的实用性在很大程度上取决于用户从人工智能机器人中获得适当“原材料”的能力,这意味着,作者认为,这些机器人可以为场景的开发提供有用的基础材料,但不太可能在场景的制作中完全超越场景制作者。此外,作者建议未来和远见实践界特别关注人工智能工具,以获得关于人类认知差异的新见解,在这种情况下,还包括大型语言模型输出的逻辑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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AI-assisted scenario generation for strategic planning

This “reflections from practice” piece explores some of the implications of emerging, artificially intelligent tools for the futures and foresight prac-ademic community. The authors provide background on these emerging, artificially intelligent tools, and explore, with special emphasis on scenarios, a specific tool named “Chat Generative Pre-trained Transformer” (hereafter, ChatGPT). The authors examine the utility of scenarios generated by artificial intelligence (AI) and explore whether or not the futures and foresight prac-ademic community should selectively embrace advances in AI to assist in the generation of scenarios. In particular, the authors will consider (1) the utility of using scenarios generated completely by AI, (2) whether what is produced, in fact, constitute scenarios, based on conventional definitions, and (3) assess the utility of using AI to assist in the production of scenarios. At this point in time, artificially intelligent tools can now generate numerous scenarios on seemingly any topic at essentially zero cost to the user. Still, the authors insist that the utility of those scenarios is largely predicated on the user's ability to coax the appropriate “raw material” from the artificially intelligent bot, which implicates, the authors contend, that such bots can usefully provide base material for the development of scenarios but are unlikely to fully eclipse scenarists in the production of scenarios. Additionally, the authors recommend that the futures and foresight prac-ademic community pay especially close attention to artificially intelligent tools for novel insights with regard to the differences in human cognition and, in this case, the logic of large language model outputs.

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Issue Information Issue Information Simplification errors in predictive models Don't push the wrong button. The concept of microperspective in futures research Science fiction in military planning—Case allied command transformation and visions of warfare 2036
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