Deep learning, reinforcement learning, and world models

Глубокое обучение, обучение с подкреплением и модели мира
David Silver, Yann LeCun, Masashi Sugiyama, Doina Precup, Maneesh Sahani, Yutaka Matsuo, Eiji Uchibe, Jun Morimoto
2022-04-19

deep learninghuman-level intelligenceneuroscientific findingsreinforcement learningworld models
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.
1
DL and RL methods have strong connections with brain functions and neuroscientific findings, suggesting relevance for understanding biological intelligence.
2
Deep learning (DL) and reinforcement learning (RL) are indispensable factors for achieving human-level or super-human AI systems.
3
Speakers presented recent studies that are identified as potential key technologies toward achieving human-level intelligence.
4
The paper summarizes talks and discussions from a symposium session exploring whether recent DL and RL advances can lead to a comprehensive understanding of human intelligence.

Deep learning and reinforcement learning methods and world models as studied in relation to human-level intelligence

Their potential to achieve and explain human-level or super-human intelligence, including connections to brain functions and neuroscientific findings

Publication Details
Publication Date
2022-04-19
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Authors
David Silver
Yann LeCun
Masashi Sugiyama
Doina Precup
Maneesh Sahani
Yutaka Matsuo
Eiji Uchibe
Jun Morimoto
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