Instructor: Yang Yuan

This course is a core course in IIIS Zhi Class, which aims for letting students solve interdisciplinary problems using AI techniques, assuming that the students have already taken systematic AI courses. This course contains multiple themes, where each theme contains a few different projects. Students will form teams of size 1-2 people. Each team will pick one project, and solve the specific problems using AI techniques. The goal of this course is to let students finish one AI project from the beginning to the end, understand the potentials and limitations of AI techniques, as well as understand what kind of human/data support are necessary for making AI work. This course assumes that the students have already taken Machine Learning and other related AI course, and also familiar with basic tools (including Python, GitHub, SSH and so on).

Instructor: Luming Duan

This course will organize students to discuss fundamental concepts and big open questions in AI and quantum computer science, including the free will, consciousness, and their relation and implementation with AI and quantum computer, emotion and social intelligence, quantum computing and its relation with brain and AI, creativity and its implementation with AI, how life and intelligence influence each other, and the future directions of intelligence. Through a series of heuristic discussions of fundamental questions, this course will stimulate the students’ interest in research on intelligence and related interdisciplinary subjects and motivate the students to think deeply on unusual fundamental questions to bring conceptual breakthroughs to the filed. Some heuristic questions include: Is free will real or an illusion? What leads to free will (or free-will illusion)? Does machine have free will? What is consciousness/mind? How consciousness helps in intelligence? How to implement consciousness in AI? Does emotion help for intelligence? How to implement emotion and social intelligence in AI? Is consciousness required for social intelligence? Is our brain quantum? How can quantum computing help for intelligence? What are the key factors for creativity? How to make a computer be creative and have imagination? How to implement Inductive, Deductive, Analog, and Probabilistic reasonings with a computer? How life and intelligence mutually influence each other? How to make a computer alive and intelligent? What are the advantages of biologically-based and computer-based intelligence? What are the possible directions for future intelligent beings?

Instructor: Jianyu Chen

This course introduces both the theoretical foundations and advanced techniques in the fields of intelligent systems and robotics, from a unified algorithmic view of both the traditional robotic control perspective and the learning perspective. The contents range from robotic system modeling and problem formulation, planning and control, estimation and perception, to adaptive behaviors using both the indirect (model-based learning) methods and direct (model-free learning) methods. The course concludes with an introduction to industrial robotic arms, autonomous vehicles, and other areas.