Core Insights - The rise of AI for Science is fundamentally changing the paradigm of scientific research, raising questions about the value of human scientific inquiry as AI can predict protein structures and execute thousands of experimental simulations [1][2] - The World Leading Scientists Summit (WLS) reached a consensus that foundational science remains a reliable force amid uncertainty, with significant breakthroughs often arising from the unknown, which algorithms struggle to reach [1][2] Group 1: The Role of AI in Science - AI's predictive capabilities are built on vast experimental data, exemplified by DeepMind's AlphaFold, which excels in predicting known molecular structures but struggles with entirely new ones [2][3] - Scientists believe that while AI can accelerate scientific processes, it cannot replace the unique role of scientists in conceptualizing ideas and solving problems, as discovery often requires transcending existing knowledge frameworks [3][4] Group 2: The OPENSCI Initiative - The OPENSCI Initiative aims to systematically reshape the value chain and collaboration model in research through AI and blockchain technology, ensuring that science serves humanity [5][7] - The initiative features a platform that establishes a value mechanism for scientific contributions and develops AI-driven tools to support researchers, enhancing efficiency and transparency in the scientific process [5][7] Group 3: Future of Scientific Research - The OPENSCI platform consists of a robust three-layer architecture, including the Artificial Scientific Intelligence (ASCI), which signifies a shift towards AI that can autonomously generate and validate scientific knowledge [7] - The future of science is seen as a proactive endeavor, with the potential for significant breakthroughs in foundational science driven by the OPENSCI initiative [7]