Deep learning, reinforcement learning, and world models
Глубокое обучение, обучение с подкреплением и модели мира
2022-04-19
SCID: 54.1/8tc4z7y7
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deep learninghuman-level intelligenceneuroscientific findingsreinforcement learningworld models
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Abstract (AI)
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.
Key Findings
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.
Research Object
Deep learning and reinforcement learning methods and world models as studied in relation to human-level intelligence
Research Subject
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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