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所有知识型岗都要被AI “吞了!清华大学教授刘嘉:未来大学分化猛烈,软件公司靠 “几人 + Agent” 就够
AI前线· 2025-09-29 04:28
作者 | 华卫 人类与 AI 间的对决,自 2016 年的 AlphaGo 打赢世界围棋冠军李世石起,就开始不断出现在大众视线中,出圈的例子更是不少。 曾担任《最强大脑》节目首席科学家的刘嘉,也亲眼见证过这样一场比赛。当时,还是百度大脑首席科学家的吴恩达带着搭载百度大脑的智能机器人小 度上了舞台,与人类组选手比拼起"看照片认脸"。面对多轮挑战,最终人类最顶尖的面孔识别选手不敌 AI。 这个结果,好似当头一棒重重敲向了此时正往北京师范大学副校长一职奔赴的刘嘉。他火速向学校递交辞呈,重新钻进实验室,将全部心思转投到 了脑科学与 AI 的交叉研究中。 回到 2025 年的今天,我们更是已置身于一个几乎被 AI 包围的时代。去年,诺贝尔物理学奖和图灵奖双双花落 AI 领域。今年年初爆火的 DeepSeek 让"无所不知"的大模型遍布朋友圈,随后 Manus 的横空出现又将 AI 完全自主的蓝图放到大众眼前。AI 真的将超越人类吗?身处于现在的时代,这个话 题已被推至现实议程,越来越多的人能够感觉到一种深切的危机感。 在今年 6 月出版的新书《通用人工智能:认知、教育与生存方式的重构》中,刘嘉用"近乎疯狂"几个字来形容 ...
通往 AGI 之路的苦涩教训
AI科技大本营· 2025-06-26 11:10
Core Viewpoint - The article discusses the rapid advancement of AI and the potential for achieving Artificial General Intelligence (AGI) within the next 5 to 10 years, as predicted by Google DeepMind CEO Demis Hassabis, who estimates a 50% probability of this achievement [1] Group 1: AI Development and Challenges - The AI wave is accelerating at an unprecedented pace, but there have been numerous missteps along the way, as highlighted by Richard Sutton's 2019 article "The Bitter Lesson," which emphasizes the pitfalls of relying too heavily on human knowledge and intuition [2][4] - Sutton argues that computational power and data are the fundamental engines driving AI forward, rather than human intelligence [3] - The article suggests that many previously held beliefs about the paths to intelligence are becoming obstacles in this new era [4] Group 2: Paths to AGI - The article introduces a discussion on the "bitter lessons" learned on the road to AGI, featuring a dialogue with Liu Jia, a professor at Tsinghua University, who has explored the intersection of AI, brain science, and cognitive science [5][11] - Liu Jia identifies three paths to AGI: reinforcement learning, brain simulation, and natural language processing (NLP), but warns that each path has its own hidden risks [9] - The article emphasizes that language does not equate to cognition, and models do not represent true thought, indicating that while NLP is progressing rapidly, it is not the ultimate destination [9][14] Group 3: Technical Insights - The article discusses the Scaling Law and the illusion of intelligence associated with large models, questioning whether the success of these models is genuine evolution or merely an illusion [15] - It raises concerns about the limitations of brain simulation due to computational bottlenecks and theoretical blind spots, as well as the boundaries of language in relation to understanding the world [14]