Challenges, evaluation and opportunities for open-world learning

IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Nature Machine Intelligence Pub Date : 2024-06-24 DOI:10.1038/s42256-024-00852-4
Mayank Kejriwal, Eric Kildebeck, Robert Steininger, Abhinav Shrivastava
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Abstract

Environmental changes can profoundly impact the performance of artificial intelligence systems operating in the real world, with effects ranging from overt catastrophic failures to non-robust behaviours that do not take changing context into account. Here we argue that designing machine intelligence that can operate in open worlds, including detecting, characterizing and adapting to structurally unexpected environmental changes, is a critical goal on the path to building systems that can solve complex and relatively under-determined problems. We present and distinguish between three forms of open-world learning (OWL)—weak, semi-strong and strong—and argue that a fully developed OWL system should be antifragile, rather than merely robust. An antifragile system, an example of which is the immune system, is not only robust to adverse events, but adapts to them quickly and becomes better at handling them in subsequent encounters. We also argue that, because OWL approaches must be capable of handling the unexpected, their practical evaluation can pose an interesting conceptual problem. AI systems operating in the real world unavoidably encounter unexpected environmental changes and need a built-in robustness and capability to learn fast, making use of advances such as lifelong and few-shot learning. Kejriwal et al. discuss three categories of such open-world learning and discuss applications such as self-driving cars and robotic inspection.

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开放世界学习的挑战、评估和机遇
环境变化会对在现实世界中运行的人工智能系统的性能产生深远影响,其影响范围从明显的灾难性故障到不考虑环境变化的非稳健行为。在这里,我们认为,设计能在开放世界中运行的机器智能,包括检测、描述和适应结构上意想不到的环境变化,是建立能解决复杂和相对不确定问题的系统的关键目标。我们介绍并区分了三种形式的开放世界学习(OWL)--弱型、半强型和强型--并认为,一个全面开发的开放世界学习系统应该是反脆弱的,而不仅仅是稳健的。反脆弱系统的一个例子是免疫系统,它不仅对不利事件具有鲁棒性,而且能迅速适应这些事件,并在以后的遭遇中更好地处理这些事件。我们还认为,由于 OWL 方法必须能够处理意外事件,因此对它们的实际评估可能会带来一个有趣的概念问题。
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来源期刊
CiteScore
36.90
自引率
2.10%
发文量
127
期刊介绍: Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.
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