创建安全开放式AI的开放性问题：控制与创造力之间的紧张关系
Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity
摘要：
人工生命起源并长期研究开放式进化的主题，该主题寻求人工系统的基本原理，这些系统受到生物进化的启发，不断创新。最近，在更广泛的人工智能领域中，人们对开放式进化的泛化越来越感兴趣，这里称为开放式搜索，其中探索开放式问题以推进人工智能，无论底层搜索算法的性质如何（例如，进化或基于梯度）。例如，开放式搜索可能会为神经网络设计新的架构，新的强化学习算法，或者最雄心勃勃的目标是设计人工通用智能。本文提出，开放式进化和人工生命对理解开放式人工智能有很大贡献，这里特别关注开放式搜索的安全性。这个想法是，人工智能系统越来越多地应用于现实世界，往往在这个过程中产生意想不到的伤害，这推动了人工智能安全领域的不断发展。本文认为，开放式人工智能有其自身的安全挑战，特别是开放式系统的创造力是否可以有效和可预测地控制。本文解释了开放式搜索中独特的安全问题是如何体现的，并提出了具体的贡献和研究问题来探索它们。希望能激发创造性的，有用的，安全的开放式搜索算法的进步。
英文摘要：
Artificial life originated and has long studied the topic of open-ended evolution, which seeks the principles underlying artificial systems that innovate continually, inspired by biological evolution. Recently, interest has grown within the broader field of AI in a generalization of open-ended evolution, here called open-ended search, wherein such questions of open-endedness are explored for advancing AI, whatever the nature of the underlying search algorithm (e.g. evolutionary or gradient-based). For example, open-ended search might design new architectures for neural networks, new reinforcement learning algorithms, or most ambitiously, aim at designing artificial general intelligence. This paper proposes that open-ended evolution and artificial life have much to contribute towards the understanding of open-ended AI, focusing here in particular on the safety of open-ended search. The idea is that AI systems are increasingly applied in the real world, often producing unintended harms in the process, which motivates the growing field of AI safety. This paper argues that open-ended AI has its own safety challenges, in particular, whether the creativity of open-ended systems can be productively and predictably controlled. This paper explains how unique safety problems manifest in open-ended search, and suggests concrete contributions and research questions to explore them. The hope is to inspire progress towards creative, useful, and safe open-ended search algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
