Hinton加入Scaling Law论战，他不站学生Ilya

Core Viewpoint - The article discusses the ongoing debate surrounding the "Scaling Law" in AI, highlighting contrasting perspectives from key figures in the field, particularly Ilya Sutskever and Geoffrey Hinton, regarding the future and limitations of scaling AI models [1][8][21]. Group 1: Perspectives on Scaling Law - Ilya Sutskever expresses skepticism about the continued effectiveness of Scaling Law, suggesting that merely increasing model size may not yield significant improvements in AI performance [23][40]. - Geoffrey Hinton, on the other hand, maintains that Scaling Laws are still valid but face challenges, particularly due to data scarcity, which he believes can be addressed by AI generating its own training data [10][21]. - Demis Hassabis, CEO of DeepMind, supports Hinton's view, emphasizing the importance of scaling for achieving advanced AI systems and the potential for self-evolving AI through data generation [15][19]. Group 2: The Debate on Data and Model Scaling - The article outlines the historical context of Scaling Law, which posits that increasing model parameters, training data, and computational resources leads to predictable improvements in AI performance [26][27]. - Recent discussions have shifted towards concerns about data limitations, with Ilya arguing that the era of pre-training is coming to an end due to diminishing returns from scaling [32][41]. - Yann LeCun also shares skepticism about the assumption that more data and computational power will automatically lead to smarter AI, indicating a broader questioning of the Scaling Law's applicability [46][48]. Group 3: Future Directions and Research Focus - The article suggests that while current paradigms may still yield significant economic and social impacts, achieving Artificial General Intelligence (AGI) or Artificial Superintelligence (ASI) will likely require further research breakthroughs [53]. - There is a consensus among leading researchers that while AGI is not a distant fantasy, the nature and speed of necessary breakthroughs remain uncertain [53].