重要的建模:人工智能和国防学习的未来

IF 1 Q3 ENGINEERING, MULTIDISCIPLINARY
S. Schatz, J. Walcutt
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引用次数: 0

摘要

老实说,人工智能(AI)将会改变——或者更确切地说,已经在改变——如此之多。这将是很容易的,如果没有灵感,填满这篇文章的洗衣清单。但是,我们不会再增加已有的一连串预测(其中许多你可以在本期特别版的章节中读到),我们将更狭隘地关注。首先,我们将这个问题与国防领域的学习联系在一起,其次,我们挑战自己,以一个单一的概念为目标——命名最有可能产生深远的、改变范式的影响的关键。为了透露点睛之笔,我们选择了“衡量和评估的方式”。在我们展示我们的工作之前,考虑一下这些定义。衡量和评价是指同一事物的两面。正式地说,测量是“基于一个或多个观测的定量表达的不确定性的减少”(第23页)。换句话说,它指的是收集到的观察结果(无论多么模糊或不完整),这些观察结果可以帮助我们以克劳德·香农(Claude Shannon)的“信息论”的方式填补(但不一定消除)不确定性。测量与评估是相辅相成的。评估是解释从度量中收集的数据的过程,出于我们的目的,我们认为它涵盖了所有相关的聚合、转换、分析和其他有效使用度量数据所需的活动。学习作为一个正式的概念,与培训和教育相关,但又明显不同。后两个词,特别是在国防背景下,充满了内涵。“训练和教育”指的是经验的组织性方面,例如,学校或训练部门提供的课程或兵棋。它们是以输入为中心的术语,更重要的是,它们往往暗示了一种正式的学习环境。相反,“学习”一词关注的是等式的个人(或团队)方面——结果方面。它描述的是任何影响知识、技能或行为的长期记忆的变化,它不区分获得这些变化的过程。1. 操作视角
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling what matters: AI and the future of defense learning
Let’s be honest, artificial intelligence (AI) will change— or, rather, is already changing—so much. It would be easy, if uninspired, to fill this article with a laundry list. But rather than add to the existing litany of forecasts (many of which you can read in the chapters of this special edition), we’ll focus more narrowly. First, we’ve bound the question to learning in the defense domain, and second, we’ve challenged ourselves to target a single concept—to name the linchpin with greatest potential to have profound, paradigm-changing impacts. To give away the punchline, we’ve selected ‘‘the way we measure and evaluate.’’ Before we show our work, consider these definitions. Measure and evaluate refer to two sides of the same coin. Formally, measurement is the ‘‘quantitatively expressed reduction of uncertainty based on one or more observations’’ (p. 23). In other words, it refers to collected observations (no matter how fuzzy or incomplete) that help us fill-in (but not necessarily eliminate) uncertainty in a Claude Shannon ‘‘information theory’’ sort of way. Measurement goes hand-in-hand with evaluation. Evaluation is the process of interpreting the data collected from measurements, and for our purposes, we’ll say it covers all of the associated aggregation, transformation, analysis, and other activities needed to effectively use the measured data. Learning, as a formal concept, is related to—but notably distinct from—training and education. Those latter two terms, particularly in a defense context, are laden with connotations. ‘‘Training and education’’ refer to the organizational side of the experience, for instance, to the curriculum or the wargame delivered by a schoolhouse or training branch. They’re input-focused terms, and more than that, they tend to imply a formal learning context. In contrast, the term ‘‘learning’’ focuses on the individual (or team) side of the equation—the outcomes side. It describes any change in long-term memory that affects knowledge, skills, or behaviors, and it makes no distinction for the process through which it was acquired. 1. An operational perspective
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CiteScore
2.80
自引率
12.50%
发文量
40
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