著者

池上 高志

出版者

日本認知科学会

雑誌

認知科学 (ISSN:13417924)

巻号頁・発行日

vol.28, no.2, pp.198-210, 2021

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<p>Ordinary neural networks represent objects in a dimensionally compressed latent (Z) space. In this latent space, a grammatical structure emerges if the input is a language, or a finite state automaton that makes predictions if the input is a time series. But the living system perceives and experiences the object without contracting it. The philosopher Meillassoux said, "A discontinuous ring consisting of multiple interruptions." While discussing the interpretation of this idea, I will analyze and report on an experiment using the android Alter3, which mimics human poses. What is important to make a robot anti-contractive is one's interaction with a human being, and the other is the autonomous rewriting of memories that is created because of its inability to learn.</p>