Core Insights - The article discusses the AI Accelerated Science Symposium held in San Francisco, where the concept of "Discoverative Intelligence" was introduced as a new paradigm for general artificial intelligence [1][3][4] - The speaker, Chen Tianqiao, emphasizes that AI should not merely replace human jobs but should aid in human evolution by helping discover the unknown [5][10] Group 1: Human Evolution and AI - Human evolution has not stopped; instead, it has transformed through scientific discoveries and technological inventions, extending human capabilities beyond biological limits [3][4] - The concept of "Discoverative Intelligence" is presented as a true form of general artificial intelligence, which can actively construct testable theoretical models and propose falsifiable hypotheses [5][10] Group 2: Paths to Discoverative Intelligence - Two main paths to achieving "Discoverative Intelligence" are identified: the "Scale Path," which relies on large models and data, and the "Structure Path," which focuses on cognitive mechanisms akin to human brain functions [6][12] - The "Scale Path" has achieved significant results in AI applications, while the "Structure Path" is emerging as a necessary approach to overcome the limitations of current AI systems [13][14] Group 3: Time Structure and Core Capabilities - The article outlines five core capabilities essential for managing information over time, which are necessary for achieving "time structure" in AI: neural dynamics, long-term memory, causal reasoning, world modeling, and metacognition [8][9][12] - These capabilities form a continuous and active loop, enabling a system to evolve over time and engage in scientific discovery [12] Group 4: Opportunities for Young Researchers - The article highlights the need for new theories, algorithms, and interdisciplinary approaches, positioning young researchers as key players in redefining intelligence through the "Structure Path" [13][14] - The company is investing over $1 billion in dedicated computing clusters to support young scientists in exploring new structures and validating cognitive mechanisms [16]

Core Insights - The AI-driven scientific symposium held in San Francisco gathered top scholars and industry leaders to discuss how AI can drive scientific discovery [1][3] - Chen Tianqiao, founder of the Tianqiao Brain Science Research Institute, introduced the concept of "Discoverative Intelligence," which he argues represents true general artificial intelligence [1][4] Group 1: Discoverative Intelligence - Discoverative Intelligence is defined as the ability to ask questions and understand principles, rather than merely predicting outcomes [6][7] - This form of intelligence is seen as essential for human evolution, emphasizing AI's role in helping humans discover the unknown [6][8] Group 2: Paths to Discoverative Intelligence - Two main paths to achieving Discoverative Intelligence are identified: the "scale path," which focuses on the size of models and data, and the "structure path," which emphasizes cognitive mechanisms akin to human brain functions [8][10] - The scale path has led to significant advancements in AI applications, while the structure path is emerging as a necessary complement to achieve deeper understanding and discovery [8][13] Group 3: Time Structure Analysis - The concept of "time structure" refers to the brain's ability to process information dynamically over time, contrasting with the static nature of current AI models [9][10] - Five core capabilities are essential for managing information over time: neural dynamics, long-term memory, causal reasoning, world modeling, and metacognition [10][11] Group 4: Opportunities for Young Researchers - The company plans to invest over $1 billion in dedicated computing clusters to support young scientists in exploring new theories and algorithms [13][14] - A new benchmark will be established to measure AI's ability to "discover," focusing on the five core capabilities necessary for true intelligence [13][14]