LeCun团队新论文：模仿人类智能搞AI，照猫画虎死胡同

Core Viewpoint - The pursuit of Artificial General Intelligence (AGI) may have been misguided from the start, with a new focus proposed on Superhuman Adaptable Intelligence (SAI) instead of merely mimicking human intelligence [1][2]. Group 1: Key Changes in AI Development Goals - The development goals of AI are shifting towards three key changes, emphasizing the speed of adapting to new tasks rather than achieving human-like intelligence [3][5]. - SAI aims to surpass human capabilities in tasks humans can perform and tackle areas previously unexplored by humans [5][6]. - The focus is moving from the number of skills an AI can perform to the speed at which it can learn new skills [6][12]. Group 2: Critique of Human-Centric AI Development - The traditional approach of using humans as a benchmark for intelligence is seen as problematic, as it may limit AI's potential [10][11]. - The paper argues that if the goal is merely to reach human-level performance, it could hinder AI's development [11][16]. - The authors suggest that optimizing for the speed of adapting to new tasks is more beneficial than simply imitating human capabilities [12][13]. Group 3: Understanding Human Intelligence Limitations - Human intelligence is not as "general" as often perceived; it is primarily a survival tool shaped by evolution [18][20]. - Many abilities considered "general" are actually the result of evolutionary adaptations, and humans perform poorly in tasks like complex calculations compared to computers [22][23]. - The concept of AGI may be an illusion, as it overlooks the biological limitations of human intelligence [25][30]. Group 4: Emphasis on Specialization - Specialization is presented as the norm for intelligence evolution, both in biology and AI systems [31][32]. - AI systems face pressure to optimize for specific tasks, as general models may not meet the demands of critical applications [34][40]. - The success of AI algorithms often comes from their alignment with the structure of the problems they are designed to solve [38][39]. Group 5: Proposed Technical Pathways for SAI - The authors propose three key technical pathways for achieving SAI: self-supervised learning, world models, and modular systems [43]. - Self-supervised learning allows AI to learn from real-world data without human labeling [44]. - World models enable AI to simulate environments and predict outcomes, facilitating task completion without explicit training [45][46]. - A modular architecture is favored over a single "one-size-fits-all" model, promoting collaboration among specialized systems [47][48].