算力悖论：理论对了所需算力是可控的，理论错了再多算力也白搭

Core Viewpoint - The current AI boom is fundamentally misdirected, with an overemphasis on scaling and computational power rather than genuine research and innovation [1][2]. Group 1: Scaling and Its Limits - The era of scaling through increased computational power is coming to an end, as the industry faces diminishing returns on investment in data and computation [3][5]. - High-quality training data is becoming scarce, leading to a plateau in performance improvements from current scaling methods [3][5]. - Existing models lack true intelligence and generalization capabilities, indicating a fundamental flaw in the underlying architecture [6][8]. Group 2: Generalization Challenges - Current AI models excel in benchmark tests but fail in real-world applications, revealing significant weaknesses in their generalization abilities [6][8]. - The focus on narrow optimization for specific tasks leads to models that perform well in limited contexts but struggle with broader applications [7][8]. - Understanding reliable generalization mechanisms is crucial for addressing various AI challenges, including alignment and value learning [8]. Group 3: SSI's Research Focus - Safe Superintelligence Inc. (SSI) aims to prioritize research over product development, challenging the industry's default assumptions about resource allocation [9][10]. - SSI's structure is designed to eliminate distractions from research, focusing solely on validating theories related to generalization [10]. - Historical precedents show that significant breakthroughs in AI do not require massive computational resources but rather insightful approaches [10]. Group 4: AGI and Its Misconceptions - The concept of Artificial General Intelligence (AGI) may be overestimated, as human intelligence operates differently from the proposed models [12]. - Human learning involves mastering foundational skills before acquiring complex abilities, contrasting with the notion of a universally capable AI [12]. - This understanding influences deployment strategies, suggesting that AI should be viewed as a system capable of continuous learning rather than a fully formed entity at launch [12]. Group 5: Future Predictions - Systems with improved generalization capabilities are expected to emerge within 5 to 20 years, reflecting uncertainty about the path forward rather than doubt about solutions [13]. - As AI capabilities become more apparent, industry behaviors will shift, leading to increased collaboration on safety and deeper government involvement [13]. - The alignment goal should encompass all sentient AI, not just humans, based on the premise of shared understanding across species [13]. Group 6: Research Aesthetics - The pursuit of research is driven by a sense of aesthetic and simplicity, with promising directions often appearing elegant and inspired by biological intelligence [14][15]. - A strong belief in the validity of certain research paths is essential for overcoming challenges and failures in the development process [15]. - The shift away from reliance on scaling as a substitute for belief in research direction emphasizes the need for genuine innovation and insight [15].