Core Viewpoint - The article introduces the "Generalized Agent Theory" (GAT), which proposes that all entities, including physical systems, life, and AI, can be viewed as "agents" and suggests a potential pathway towards a "Theory of Everything" [1][3][28]. Group 1: Theory of Everything - The "Theory of Everything" aims to create a unified framework that explains all phenomena in the universe using minimal foundational laws, from the Big Bang to the emergence of intelligence and self-awareness [2]. - The pursuit of this theory faces significant challenges, particularly the incompatibility between general relativity and quantum mechanics, as well as the lack of a unified theory for the four fundamental forces of physics [4][8]. Group 2: Generalized Agent Theory - The GAT is built on the exploration of the core concept of "agents" in AI, leading to the development of a unified structure that encompasses various systems, including physical, biological, and AI systems [3][6]. - The theory identifies three main goals: unifying the four fundamental forces, integrating general relativity with quantum mechanics, and consolidating physical, biological, and AI systems into a single theoretical model [28]. Group 3: Core Components of GAT - GAT consists of four core components: the standard agent model, agent classification system, extreme point intelligent field model, and multi-agent relationship system [10][19]. - The standard agent model defines agents as information processing systems with five essential functional modules: information input, output, dynamic storage, information creation, and a control module [12][18]. Group 4: Challenges and Hypotheses - The theory proposes that the four fundamental forces may be manifestations of a more fundamental "intelligent field" that drives the evolution of all agents [7][41]. - It suggests that the differences in classical mechanics, relativity, and quantum mechanics arise from the varying intelligence levels of observers, which can be adjusted as a parameter in theoretical scenarios [46][52]. Group 5: Implications and Future Directions - GAT opens new avenues for exploring the fundamental questions of the universe, emphasizing that it is not a closed theory but an exploratory framework that may lead to deeper scientific inquiries [54][57]. - The theory's potential to unify various scientific disciplines under the concept of agents could provide valuable insights into the nature of existence and intelligence [42][56].

Group 1 - The core viewpoint of the article is that AI is reshaping human scientific paradigms, and while it will become an important partner in exploring ultimate questions in mathematics and physics, it cannot replace human intuition and creativity [2][3]. - Terence Tao discusses the importance of collaboration in creating superior intelligent systems, suggesting that a collective human community is more likely to achieve breakthroughs in mathematics than individual mathematicians [3]. - The article highlights Tao's insights on various world-class mathematical problems, including the Kakeya conjecture and the Navier-Stokes regularity problem, emphasizing the interconnectedness of these problems with other mathematical fields [4][16]. Group 2 - Tao emphasizes that in undergraduate education, students encounter difficult problems like the Riemann hypothesis and twin prime conjecture, but the real challenge lies in solving the remaining 10% of the problem after existing techniques have addressed 90% [5]. - The Kakeya problem, which Tao has focused on, involves determining the minimum area required for a needle to change direction in a plane, illustrating the complexity and depth of mathematical inquiry [6][7]. - The article discusses the implications of the Kakeya conjecture and its connections to partial differential equations, number theory, geometry, topology, and combinatorics, showcasing the rich interrelations within mathematics [10][14]. Group 3 - The Navier-Stokes regularity problem is presented as a significant unsolved issue in fluid dynamics, questioning whether a smooth initial velocity field can lead to singularities in fluid flow [16][18]. - Tao explains the challenges in proving general conclusions for the Navier-Stokes equations, using the example of Maxwell's demon to illustrate statistical impossibilities in fluid dynamics [19][20]. - The article notes that understanding the Kakeya conjecture can aid in comprehending wave concentration issues, which may indirectly enhance the understanding of the Navier-Stokes problem [18][26]. Group 4 - Tao discusses the concept of self-similar explosions in fluid dynamics, where energy can be concentrated in smaller scales, leading to potential singularities in the Navier-Stokes equations [22][24]. - The article highlights the mathematical exploration of how energy can be manipulated within fluid systems, suggesting that controlling energy transfer could lead to significant breakthroughs in understanding fluid behavior [26][30]. - Tao's work aims to bridge the gap between theoretical mathematics and practical applications, indicating a future where AI could play a role in experimental mathematics [55][56].