Core Insights - The first AI-driven scientific symposium organized by the Tianqiao Brain Science Institute and UC Berkeley gathered over 20 top scholars and industry leaders to discuss how AI can drive scientific discovery [1][2] Group 1: Investment and Support for AI Research - Tianqiao Brain Science Institute founder Chen Tianqiao announced a $1 billion investment in computational power to support innovative AI research globally [1] - Chen emphasized that the ultimate value of AI lies in discovery, proposing that AI should evolve human capabilities rather than replace them [1][2] Group 2: Development of Discovery-Oriented Intelligence - Chen outlined five essential capabilities for building discovery-oriented intelligence: neural dynamic structure, long-term memory, causal reasoning mechanisms, world models, and metacognition with intrinsic motivation [2] - He introduced various support initiatives for young scientists, including benchmark testing and structural computational resources [2] Group 3: AI Innovations in Scientific Research - Omar Yaki, a 2025 Nobel Prize winner, presented a portable water extraction device designed by AI, showcasing a new paradigm of "from molecules to society" driven by generative AI and self-learning agents [5][8] - David Baker, a 2024 Nobel Prize winner, discussed how AI can reverse-engineer protein design, achieving breakthroughs in neurodegenerative disease research and enzyme engineering [8] - Jennifer Doudna highlighted that 40% of basic gene functions remain mysteries, advocating for the synergy of CRISPR technology and machine learning to enhance data collection and analysis [9][10] Group 4: Ethical Considerations and Challenges in AI - John Hennessy emphasized the need for human oversight in AI decision-making, stressing the importance of transparency and verification of AI-generated content [10] - He raised concerns about data quality and energy efficiency in the context of rapidly advancing AI technologies [10]

Core Insights - The first "AI-Driven Scientific Symposium" was held in San Francisco, featuring Nobel laureates and industry leaders discussing how AI can drive scientific discovery [1][2] - Chen Tianqiao announced a $1 billion investment in computational power to support global scientists in "discovery-driven intelligence" research [1] - The symposium highlighted the importance of AI's role in constructing verifiable world models and enhancing human capabilities rather than replacing them [1] Group 1: AI in Scientific Research - Chen Tianqiao emphasized the need for "discovery-driven intelligence" to possess five key capabilities: neural dynamic structure, long-term memory, causal reasoning mechanisms, world models, and metacognitive systems [1] - Omar Yaghi showcased AI's application in materials science, demonstrating a portable device that extracts water from the atmosphere in low humidity conditions using ChatGPT for molecular optimization [1][2] - David Baker presented the RFDiffusion3 model, which enables reverse design of proteins, providing new pathways for research on diseases like Alzheimer's [2] Group 2: AI and Genetic Research - Jennifer Doudna discussed the integration of AI with CRISPR technology, highlighting its potential to enhance understanding of unknown gene functions and advance personalized gene therapy [2] - The symposium concluded with the "AI-Driven Science Prize," recognizing young scientists for their cutting-edge research, indicating a shift towards AI-driven paradigms across multiple disciplines [3] Group 3: Societal Implications of AI - John Hennessy reflected on the rapid adoption of AI, stressing the need for humans to retain key decision-making authority and ensure transparency in AI-generated content [2] - He warned about the potential depletion of global data for AI training in the coming years, noting that improvements in computational energy efficiency have not kept pace with growth [2]