Tamami Nakano<\/a><\/p>\n\n\n\nAbstract:
Humans have formed the current language (symbol) system through the adaptation to the real-world environment. In recent years, large language models that achieve language understanding and generation by massively learning linguistic corpora through predictive learning have gained attention. However, the computational theory of intelligence that forms language itself as an extension of real-world environmental adaptation is not well understood.\u00a0 The speaker proposed the concept of a symbol emergence system and has been developing research in symbol emergence in robotics as a constructive approach. In this discussion, the speaker propose a framework called collective predictive coding, interpreting symbol emergence as decentralized Bayesian inference in a multi-agent system. This lecture will introduce the concepts of symbol emergence systems and collective predictive coding, discuss recent research achievements related to symbol emergence, and explore the possibilities of theoretical construction for a future society where humans and AI robots coexist.<\/p>\n","protected":false},"featured_media":0,"template":"","acf":[],"_links":{"self":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/english\/wp-json\/wp\/v2\/event\/3383"}],"collection":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/english\/wp-json\/wp\/v2\/event"}],"about":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/english\/wp-json\/wp\/v2\/types\/event"}],"wp:attachment":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/english\/wp-json\/wp\/v2\/media?parent=3383"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}