Research talk: Numerical weak AI and symbolic strong AI
Currently, bottom-up DNNs are making breakthroughs and achieving tremendous success in all aspects of Computer Science. Here, the researchers weakly solve the problems by collecting large data sets and DNNs are solving problems numerically. On the other hand, AI systems need to have the top-down knowledge derived from human common-sense to understand human intentions and human emotions for effective co-working with human. Thus, common-sense needs to be formalized symbolically, under strong guidance by the researchers, to solve the problems. In fact, we believe that the effective and smooth integration of this top-down strategy given by human common-sense and bottom-up tactics given by DNNs will be the key to the next generation of AI systems.
In this session, we will take up the robotics field as an example of human co-working AI systems, and researchers working on this integration in the robotics area will provide a summary of their latest research results and discuss prospects.
- 轨迹:
- Amplifying Human Productivity and Creativity
- 日期：
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Katsushi Ikeuchi
Sr. Principal Research Manager
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Yasuo Kuniyoshi
Director of AI Center, Professor at School of Info Sci & Tech
The University of Tokyo
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Jun Takamatsu
Sr Research Scientist
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Jaime Teevan
Chief Scientist & Technical Fellow
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