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Research Alumni Education Prospective Students Current Students UTokyo FOCUS Access and Campus Maps Contact Make a donation 日本語 中文 한국어 Prospective Students Home > Prospective Students > Special and Short-term Programs > UTokyo Global Unit Courses (GUC) > GUC24S131C | AI for Understanding Human Intelligence GUC24S131C | AI for Understanding Human Intelligence About the lecturer Yukie Nagai is a Project Professor at the International Research Center for Neurointelligence at the University of Tokyo. She earned her Ph.D. in Engineering from Osaka University in 2004 and subsequently held positions at the National Institute of Information and Communications Technology, Bielefeld University, and Osaka University. Since 2019, she has been leading the Cognitive Developmental Robotics Lab at the University of Tokyo. Her research encompasses cognitive developmental robotics, computational neuroscience, and assistive technologies for developmental disorders. Dr. Nagai’s pioneering work centers on the role of predictive processing in the brain, explaining temporal continuity and individual diversity in cognitive development. In acknowledgment of her work, she received the titles of “World’s 50 Most Renowned Women in Robotics” in 2020 and “35 Women in Robotics Engineering and Science” in 2022, among other recognitions. Prof. Yukie NAGAI Top Introduction video AI for Understanding Human Intelligence Top Syllabus 1 Subject AI for Understanding Human Intelligence 2 Field Computer science, robotics, developmental psychology 3 Key words Cognitive developmental robotics; neural network; predictive processing; cognitive development; neurodiversity 4 Global Unit 1 5 Lecturer Yukie NAGAI 6 Period June 17 - 28, 2024 7 Time 13:00-14:30 [June 17-21] 13:00-18:30 [June 24-28] (Japan Standard Time) 8 Lecture style In-person (on Hongo Campus) 9 Evaluation Criteria Excellent (S) 90–100％; Very good (A) 80–89％; Good (B) 70–79%; Pass (C) 60–69%; Fail (D) 0–59％ 10 Evaluation methods Lecture: 25% Hands-on project: 25% Presentation: 25% Final report: 25% 11 Prerequisites No prior knowledge about artificial intelligence or robotics is required for the lectures. However, basic computational skills for programming and/or data analysis are preferred for the hands-on projects. 12 Contents Purpose This course consists of lectures and hands-on projects, through which students learn how to use AI and robots for investigating human intelligence. Students who successfully complete this course will have: learned how to use artificial neural networks to investigate human intelligence learned cognitive and neuroscience theories about human intelligence acquired skills to program/run artificial neural networks acquired skills to design and conduct robot/VR (virtual reality) experiments acquired skills to computationally analyze behavioral and physiological data Description Human infants acquire various cognitive abilities in the first few years of life. Although the developmental dynamics of their behaviors have been closely analyzed, what neural, bodily, and social mechanisms guide the development remain a mystery. In this course, I will introduce AI and robotics approaches to understanding the underlying mechanisms for infant development. The approach called cognitive developmental robotics aims to elucidate the principle of human intelligence by designing artificial systems that learn and develop like infants. In contrast to the analytical approach in neuroscience, cognitive science, and psychology, this constructive approach has the potential to uncover a unified principle of intelligence. The course consists of three parts: lectures, hands-on projects, and presentations. In the first week (Sessions 1-5), I will give lectures on how AI and robotics technologies can be used for investigating cognitive development. Computational studies using neural networks and humanoid robots will be introduced to explain how neural, bodily, and social mechanisms interact to guide cognitive development. In the second week (Sessions 6-17), students will work on hands-on projects to learn practical challenges in pursuing the above studies. Students divided into groups will address one of the following topics: (a) programming of neural networks to test a computational theory of cognitive development, and (b) robot/VR experiments to examine neurodiversity in cognitive development. At the end of the second week (Sessions 18-20), students will give a presentation about their hands-on projects. Students will discuss how the theories of cognitive development can be tested using neural networks and/or a robot/VR and what they have achieved and learned from their projects.   Schedule • Sessions 1-5: Lecture    1. Introduction to cognitive developmental robotics    2. Development of sensorimotor abilities    3. Emergence of social abilities    4. Neurodiversity in development    5. Roles of embodied social interaction • Sessions 6-17: Hands-on project    (a) Programming of neural networks to test a computational theory of cognitive development    (b) Robot/VR experiments to examine neurodiversity in cognitive development    (c) Computational analysis of behavioral and physiological signals in social interactions  • Sessions 18-20: Presentation Assignments • Hands-on project: Students divided into groups will address one of the following projects:    (a) Programming of neural networks to test a computational theory of cognitive development    (b) Robot/VR experiments to examine neurodiversity in cognitive development    (c) Computational analysis of behavioral and physiological signals in social interactions • Presentation: Students will give a 10-20 min presentation about their hands-on projects. • Final report: Students will submit a 5-6 page final report summarizing the lectures and hands-on projects. 13 Required readings • Yukie Nagai, "Predictive learning: its key role in early cognitive development," Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1771):20180030, 2019. • Karl Friston, Rosalyn J. Moran, Yukie Nagai, Tadahiro Taniguchi, Hiroaki Gomi, and Josh Tenenbaum, "World model learning and inference," Neural Networks, 144:573-590, 2021. • Yukie Nagai, “Social Cognition,” Cognitive Robotics, A. Cangelosi and M. Asada (Eds.), MIT Press, 2022. 14 Reference readings • Angelo Cangelosi and Matthew Schlesinger, “Developmental Robotics,” The MIT Press, 2015. • Jun Tani, "Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena”, Oxford University Press, 2016. • Angelo Cangelosi and Minoru Asada (Eds), “Cognitive Robotics,” The MIT Press, 2022. 15 Notes on Taking the Course N/A UTokyo Global Unit Courses (GUC) International Education Promotion Group, Education and Student Support Department The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8652 JAPAN For inquiries regarding GUC, kindly direct them to the following email address: utokyo-guc.adm(at)gs.mail.u-tokyo.ac.jp *Please change (at) to @ Top--> Inquiries about the content of this page: International Education Promotion GroupSend inquiry Social media directory Disseminating information for UTokyo during disasters Site map Site policy Privacy policy Jobs UTokyo Portal utelecon Frequently asked questions This website is managed by the Public Relations Office. 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