论文标题

动态认知应用于人工智能中的价值学习

Dynamic Cognition Applied to Value Learning in Artificial Intelligence

论文作者

de Oliveira, Nythamar, Corrêa, Nicholas Kluge

论文摘要

人工智能(AI)开发专家预测,智能系统和代理商的发展进步将重塑我们社会中重要的领域。然而,如果没有审慎的进步,它可能会导致人类的负面结果。因此,该地区的一些研究人员正在试图发展一个强大,有益和安全的人工智能概念。目前,AI研究领域的一些开放问题是由于难以避免智能代理的不良行为,同时指定了我们想要这样的系统。最重要的是,人工智能代理人的价值观与人类价值观保持一致,因为我们不能指望AI仅仅因为它的智慧而发展我们的道德偏好,正如正交性论文中所讨论的那样。也许这个困难来自我们使用代表性认知方法来解决目标,价值和目的的问题的方式。解决这个问题的方法是Dreyfus提出的动态认知方法,其现象学哲学捍卫了人类在世界上的经验不能由象征或联系的认知方法来代表。解决此问题的一种可能的方法是使用理论模型,例如SED(位于体现动力学)来解决AI中的值学习问题。

Experts in Artificial Intelligence (AI) development predict that advances in the development of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance isn't done with prudence, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents, and at the same time specifying what we want such systems to do. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop our moral preferences simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic cognitive approach proposed by Dreyfus, whose phenomenological philosophy defends that the human experience of being-in-the-world cannot be represented by the symbolic or connectionist cognitive methods. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI.

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