Matsuo Yutaka, LeCun Yann, Sahani Maneesh, Precup Doina, Silver David, Sugiyama Masashi, Uchibe Eiji, Morimoto Jun
The University of Tokyo, Japan.
New York University, Courant Institute & Center for Data Science, United States of America; Facebook AI Research, United States of America.
Neural Netw. 2022 Aug;152:267-275. doi: 10.1016/j.neunet.2022.03.037. Epub 2022 Apr 19.
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.
深度学习(DL)和强化学习(RL)方法似乎是实现人类水平或超人类人工智能系统不可或缺的因素。另一方面,DL和RL都与我们的大脑功能以及神经科学研究结果有着紧密的联系。在本综述中,我们总结了人工智能与脑科学国际研讨会“深度学习与强化学习”环节中的演讲和讨论。在这个环节中,我们探讨了基于深度学习和强化学习算法的最新进展,我们是否能够全面理解人类智能。演讲者们发表了关于他们近期研究的演讲,这些研究可能是实现人类水平智能的关键技术。