Sims Matthew
Institute for Philosophy II, Ruhr-University Bochum, Bochum, Germany.
Front Neurorobot. 2022 Apr 25;16:857614. doi: 10.3389/fnbot.2022.857614. eCollection 2022.
Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of "self-concern," a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, as argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence.
当前人工智能研究中的智能是根据设计者指定的任务来衡量的，这些任务与智能体自身毫无关联。因此，任务及其评估更多地揭示了我们的智能，而非我们所设计和评估的智能体可能具备的智能。作为纠正这一问题的可能第一步，本文引入了“自我关注”的概念，这是一个复杂系统的属性，描述了该系统产生与其持续自我维持相兼容状态的倾向。如文中所论证的，自我关注是所有生物系统中所发现的那种基本智能的基础，因为它反映了任何此类系统持续生存的存在性任务。本文旨在谨慎地向前迈进几步，以便更好地理解生物系统中自我关注所必需的一些核心组织条件。通过在具身人工智能中模拟这些条件，或许可以在机器中实现类似真正自我关注的东西，使人工智能向其模拟类人智能的最初目标迈进一步。
