LeCun和哈萨比斯「吵」起来了：「通用智能」到底存不存在？

Core Viewpoint - The article discusses a heated debate between Yann LeCun and Demis Hassabis regarding the concept of "general intelligence," with LeCun arguing that it does not exist and is a misconception, while Hassabis defends the idea of human and AI adaptability as a form of general intelligence [1][10][38]. Group 1: LeCun's Perspective - LeCun asserts that "general intelligence" is a flawed concept, arguing that human intelligence is highly specialized rather than general [4][24]. - He emphasizes that the tasks humans excel at are limited to those we can conceive, while there are countless tasks beyond our understanding where other animals outperform us [4][23]. - LeCun provides mathematical reasoning to support his view, stating that the vast majority of possible functions that could be understood by the human brain are beyond our comprehension [25][27]. Group 2: Hassabis's Counterargument - Hassabis argues that LeCun confuses "general intelligence" with "universal intelligence," suggesting that human brains and AI models are akin to Turing machines capable of learning anything given sufficient time, memory, and data [12][14]. - He highlights the remarkable adaptability of the human brain, which, despite being evolved for specific tasks, has achieved extraordinary feats such as inventing chess and building complex machines like the Boeing 747 [12][14]. - Hassabis acknowledges the limitations imposed by the "no free lunch" theorem but maintains that the theoretical framework of a general system allows for learning any computable task [14][38]. Group 3: Broader Implications and Reactions - The debate has sparked widespread discussion, with supporters of LeCun praising his realism, while critics accuse him of exaggeration for attention [6][8]. - Various experts have weighed in, with some aligning with LeCun's view on human intelligence being overly self-centered, while others support Hassabis's perspective on the brain's adaptability [29][36]. - The disagreement reflects differing research paradigms: one focusing on the potential of general architectures and the other on practical mechanisms for learning and generalization in real-world environments [38][39].