Journal
NEURAL NETWORKS
Volume 152, Issue -, Pages 267-275Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2022.03.037
Keywords
Deep learning; Reinforcement learning; World models; Machine learning; Artificial intelligence
Funding
- JSPS KAKENHI, Japan [JP16H06562, JP16H06565, JP19H05001]
- Gatsby Charitable Foundation, United Kingdom
- Simons Foundation, United States [SCGB 543039]
- International Research Center for Neurointelligence, Japan (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study
Ask authors/readers for more resources
This review summarizes the talks and discussions on deep learning and reinforcement learning at the International Symposium on Artificial Intelligence and Brain Science. The review highlights the strong connections of DL and RL with brain functions and neuroscientific findings. The focus of the discussions is on whether comprehensive understanding of human intelligence can be achieved based on the recent advances in DL and RL algorithms, with speakers presenting their recent studies that could be key technologies for achieving human-level intelligence.
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 Learningsession 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. (c) 2022 Published by Elsevier Ltd.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available