On October 27, the Second Intelligent Scientist Ecosystem Alliance Conference and Forum was held at the University of Science and Technology of China, themed “Building Infrastructure for Intelligent Scientists to Ignite a Paradigm Revolution.”
The Intelligent Scientist Ecosystem Alliance, established by the University of Science and Technology of China in collaboration with leading domestic institutions in the field of intelligent material sciences, aims to promote the sharing of research robot command sets, experimental template libraries, and frameworks for scientific intelligent agents. The goal is to advance the standardization and infrastructure development for intelligent scientists, ultimately transforming the research paradigm in data-driven scientific inquiries.
During the event, notable attendees, including Academician Chang Jin (President of the University of Science and Technology of China), Academician Asao Ming from Zhejiang University, Academician Zhang Tongyi from Hong Kong University of Science and Technology (Guangzhou) and Shanghai University, Academician Xie Yi from the University of Science and Technology of China, and Vice President Academician Yang Jinlong, collectively ignited the “flame of paradigm revolution.”
The conference began with the announcement of 45 new alliance members, followed by Professor Luo Yi from the University of Science and Technology of China presenting the alliance’s action program — the “Declaration of the Intelligent Scientist Ecosystem Alliance.” This alliance aims to establish a national infrastructure for intelligent scientists, providing always-accessible capabilities in scientific cognition, precise experimentation and computation, and data analysis. This initiative seeks to help humanity overcome the individual limitations of physical strength, computational power, intellectual capacity, and disciplinary understanding. Much like how electrical infrastructure allows for immediate access to power, the intelligent scientist infrastructure will serve as a foundational “grid” for new productive forces of intelligence.
Professor Jiang Jun from the University of Science and Technology of China unveiled the “Machine Chemist System 1.2” during the conference. This version integrates a “chemical brain” and includes four major research bases: literature reading, experimental design, robotic operation, and intelligent simulation, covering the full spectrum of scientific research methodologies. He highlighted the latest achievements of the alliance in intelligent research paradigms and foundational construction for intelligent science.
Jiang elaborated that the machine chemist operating system combines cognitive and action intelligence, establishing a data-driven paradigm of “theoretical-practical integration” for scientific research. He also introduced the intelligent scientist platform website, which will serve as the alliance’s data center, providing up-to-date chemical template libraries, command sets, automated calculation templates, and a forum for engagement, creating a vibrant community for AI4S innovation.
Subsequently, attendees engaged in deep discussions on four themes: “Intelligent Scientific Foundations and the Flame of Paradigm Revolution,” “Talent Development in the Era of Data Intelligence,” “Intelligent Foundations Driving New Productive Forces,” and “Standardization and Promotion of Intelligent Scientists.” They collaboratively analyzed the latest advancements and future trends of intelligent technology in chemical synthesis, material innovation, and scientific methodologies, exchanging ideas on the integration of theory and practice as well as strategies for talent development in the data intelligence era.
Shen Yi, Deputy Director of the Frontiers of Science and Basic Research Bureau of the Chinese Academy of Sciences, emphasized that building infrastructure for intelligent scientists is a crucial step towards achieving paradigm transformation. He remarked, “We need to strengthen the application of artificial intelligence across various disciplines, innovate research paradigms, improve research efficiency and quality, and promote significant original outcomes. Additionally, we must enhance talent development and recruitment, unite related strengths both within and outside our institutions, and build an interdisciplinary research team with a spirit of innovation and practical capabilities for AI in Science.”
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