Group 1 - The Future Science Prize 10th Anniversary Celebration highlighted discussions on disruptive scientific changes over the next 20 years and breakthrough potentials over the next 50 years [1] - Zhang Jie from Shanghai Jiao Tong University emphasized that the achievement of net energy gain from inertial confinement nuclear fusion in December 2022 marks a significant milestone for controllable nuclear fusion technology, which could transform society towards non-carbon-based energy [1] - Ding Hong, also from Shanghai Jiao Tong University, identified general quantum computing as the most disruptive technology in the next 20 years, while AI for Science will be a key focus in the next 50 years [1] Group 2 - Xue Qikun, President of Southern University of Science and Technology, stated that controlled nuclear fusion could permanently solve energy issues and support industrial revolutions in the next 20 years, while room-temperature superconductivity could lead to major scientific and technological changes in the next 50 years [2] - Chen Xianhui from the University of Science and Technology of China highlighted that core key materials could drive significant human transformations in the next 20 years, with room-temperature superconductivity breaking cost barriers in fields like medical MRI and quantum computing cooling in the next 50 years [2] - Shi Yigong from Westlake University discussed how AI technologies like AlphaFold have revolutionized traditional biological research, urging researchers to embrace AI to expand scientific boundaries while maintaining critical thinking and interdisciplinary collaboration [2] Group 3 - Shen Xiangyang, Chairman of the Board of Hong Kong University of Science and Technology, described large models as encompassing technology, business, and governance, with multimodal development being a crucial milestone involving computation, algorithms, and data [3] - Yang Yaodong from Peking University emphasized the importance of alignment technology for large models to comply with human instructions, noting current weaknesses in reinforcement learning-based alignment and suggesting enhancements through computer science and cryptography [3]