Wisdom by information
JA | EN
| Term | 2020/Second semester |
| Number of Credits | 2 credits |
| Course Type | Lecture |
| Target Year | From 1st to 3rd year students |
| Target Student | Graduate |
| Language | Japanese |
| Day/Period | Mon.2 |
| Instructor(s) |
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| Outline and Purpose of the Course | What is life? This lecture studies this question with the latest achievements from science and technology including Biology, Physics, Anthropology, Neuroscience, Cognitive Science and Artificial Intelligence. It shows a common phenomenon in the nature of life and the prosperity of Human (i.e., Homo Sapiens), fragmentation and integration of science, disruption and organization of society, research, education, innovation and future life, i.e., information and the reducing information entropy. It is discussed that wisdom is better defined as information processing to reduce entropy and it evolves by learning and random selecting. |
| Course Goals | 1. Learn fundamental knowledge of Neuroscience, Cognitive Science and Artificial Intelligence and understand the challenges of human society from the past to the future. 2. Can consider the universe, life, evolution, wisdom and learning, etc from the aspect of information. 3. Can study life, future and other matter with this concept of wisdom. |
| Schedule and Contents | 1. Cosmos, life and evolution theory 2. Secret behind the life of human (Home Sapiens) 3. Challenge from Artificial Intelligence (Deep Blue, IBM Watson, AlphaGo/AlphaGo Zero) 4. What is life? Thinking of Schrodinger 5. Dissipative structure and related 6. Demon of Maxwell, information = energy 7. What is wisdom 8. Creation and learning, random selecting 9. Free energy principle - frontier in brain science 10. AI ethics and the Trolley problem 11. (Technology) Singularity 12. SuperIntelligence 13. Life 3.0 14. Future wisdom 15. Student presentation Remark: the above content may change according to learning attainment |
| Grading Policy | participant 30%, presentation 30%, report 40% |
| Prerequisites | It is not required to know Information Science before the lecture but self-learning is strongly recommended. |
| Preparation and Review | Off-campus report may be given. |
