遥遥无期的AGI是画大饼吗？两位教授「吵起来了」

Group 1 - The core argument of the article is that while current AI models are becoming more powerful, the realization of Artificial General Intelligence (AGI) remains distant due to physical and resource limitations [3][22][24] - Tim Dettmers' blog post titled "Why AGI Will Not Happen" argues that due to physical constraints, meaningful superintelligence cannot be achieved [3][6][22] - The article discusses the limitations of hardware improvements and the challenges in achieving efficient computation, emphasizing that the current AI architectures are bound by physical realities [8][10][11] Group 2 - The blog highlights that the efficiency of current AI systems is far from optimal, with significant room for improvement in both training and inference processes [35][37][56] - It points out that the current models are lagging indicators of hardware development, suggesting that advancements in hardware will lead to better model performance [43][57] - The article proposes multiple pathways for enhancing AI capabilities, including better model-hardware co-design and exploring new hardware features [40][46][55] Group 3 - The article contrasts the AI development philosophies of the US and China, noting that the US focuses on achieving superintelligence while China emphasizes practical applications and productivity improvements [20][21] - It suggests that the pursuit of superintelligence may lead to difficulties, as organizations focusing solely on this goal may be outpaced by those driving practical AI applications [26][28] - The discussion includes the potential for smaller players in the AI space to innovate beyond scale, leveraging efficiency and practical applications [17][18][19]