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Deep learning, reinforcement learning, and world models.
Matsuo, Yutaka; LeCun, Yann; Sahani, Maneesh; Precup, Doina; Silver, David; Sugiyama, Masashi; Uchibe, Eiji; Morimoto, Jun.
Afiliación
  • Matsuo Y; The University of Tokyo, Japan.
  • LeCun Y; New York University, Courant Institute & Center for Data Science, United States of America; Facebook AI Research, United States of America.
  • Sahani M; Gatsby Computational Neuroscience Unit, University College London, United Kingdom.
  • Precup D; DeepMind, United Kingdom; McGill University, Canada.
  • Silver D; DeepMind, United Kingdom.
  • Sugiyama M; RIKEN Center for Advanced Intelligence Project, Japan; The University of Tokyo, Japan.
  • Uchibe E; Advanced Telecommunication Research International (ATR), Japan.
  • Morimoto J; Advanced Telecommunication Research International (ATR), Japan; Kyoto University, Japan. Electronic address: xmorimo@atr.jp.
Neural Netw ; 152: 267-275, 2022 Aug.
Article en En | MEDLINE | ID: mdl-35569196
ABSTRACT
Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and with neuroscientific findings. In this review, we summarize talks and discussions in the "Deep Learning and Reinforcement Learning" session of the symposium, International Symposium on Artificial Intelligence and Brain Science. In this session, we discussed whether we can achieve comprehensive understanding of human intelligence based on the recent advances of deep learning and reinforcement learning algorithms. Speakers contributed to provide talks about their recent studies that can be key technologies to achieve human-level intelligence.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2022 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2022 Tipo del documento: Article