Core Viewpoint - The company MiroMind, founded by Chen Tianqiao, is shifting its focus from generic chatbot development to "discovery-based intelligence" and "general solvers," aiming to create a science-oriented AI model centered on causality [2][3]. Group 1: Technological Direction - MiroMind will concentrate its resources on scientific and causal subspaces, striving for AI that excels in long-chain reasoning and serious research scenarios, rather than merely being an advanced conversational tool [2]. - The company aims to develop a pre-trained model that emphasizes causality, with a focus on refining reasoning structures, causal modules, and a "research OS" to assist human scientists in exploring the unknown [2]. Group 2: Organizational Philosophy - Chen Tianqiao advocates for a "systematic innovation" organizational philosophy, moving away from reliance on individual brilliance to a framework based on clear rules and stable mechanisms [3]. - The company plans to implement a scientific review process and committee mechanisms to transform key technological breakthroughs from "personal intuition" into "verifiable and reproducible" engineering capabilities [3]. Group 3: Financial Commitment - Chen Tianqiao defines the investment from Shengda Group as "patient capital," emphasizing a long-term commitment without the pressure of short-term quarterly results [3]. - Shengda Group will act as a "bottom-line investor" for MiroMind, ensuring strategic stability and preventing the company from deviating due to short-term funding issues [3]. Group 4: Talent Incentives - MiroMind will allocate funds for employee stock buybacks in future financing rounds, providing a regular "liquidity window" for long-term contributors [4]. - The company invites talent who value long-term contributions and are willing to think about making a meaningful impact during the AI transformation to join its mission [4].

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]