AGI最新判断。 作者 | 闻乐 不圆 来源 | 量子位 （ID：QbitAI） 在我看来，没有空间智能，通用人工智能就不完整。 这是"AI教母"李飞飞在最新访谈中对AGI的判断——是的，李飞飞也开始谈论AGI了。 不过她有自己的表述，从进入人工智能领域开始，她就确定了她终身奋斗的梦想： 让智能体能够讲述世界的故事 。 而这，离不开 空间智能 。 正如她本人所说： 我整个职业生涯都在追逐那些极其困难、近乎疯狂的问题。 李飞飞如今聚焦于空间智能领域——这个人工智能最艰难的领域之一。 她认为 3D世界建模 对于实现AGI至关重要，并表示： 理解三维世界、生成三维世界、推理三维世界、在三维世界中做事，是人工智能的基本问题。 她的目标是创建一个超越平面像素、跨越语言障碍、能够真正捕捉三维世界结构和空间智能的 世界模型 。 在这次对话中，她从ImageNet的起源和影响说起，讲述了AI范式转变与关键突破，并提到了3D建模面临的挑战以及空间智能的数据 缺失问题。 量子位翻译并总结了全文，让我们一起来学习李飞飞的最新认知和分享。 ImageNet为现代计算机视觉搭建数据骨架 Q：你最早创建的项目之一是2009年的Image ...

Core Viewpoint - The article emphasizes the importance of spatial intelligence in achieving Artificial General Intelligence (AGI), as articulated by AI expert Fei-Fei Li, who believes that understanding and interacting with the 3D world is fundamental to AI development [1][4][29]. Group 1: Spatial Intelligence and AGI - Fei-Fei Li asserts that without spatial intelligence, AGI is incomplete, highlighting the necessity of creating world models that capture the structure and dynamics of the 3D world [29]. - She identifies 3D world modeling as a critical challenge for AI, stating that understanding, generating, reasoning, and acting within a 3D environment are essential problems for AI [7][29]. - The pursuit of spatial intelligence is framed as a lifelong goal for Li, who aims to develop algorithms that can narrate the stories of the world by understanding complex scenes [20][29]. Group 2: Historical Context and Breakthroughs - The article discusses the inception of ImageNet, a pivotal project initiated by Li, which aimed to create a vast dataset for training AI in visual recognition, addressing the data scarcity issue in the early days of AI [11][14]. - The success of ImageNet led to significant advancements in computer vision, particularly with the introduction of AlexNet, which utilized convolutional neural networks and marked a turning point in AI capabilities [19][22]. - Li reflects on the evolution of AI from object recognition to scene understanding, emphasizing the importance of integrating natural language with visual signals to enable AI to describe complex environments [15][20]. Group 3: Future Directions and Applications - Li expresses excitement about the potential applications of spatial intelligence in various fields, including design, architecture, gaming, and robotics, indicating a broad utility for world models [35]. - The article mentions the challenges of data acquisition for spatial intelligence, noting that while language data is abundant online, spatial data is less accessible and often resides within human cognition [33][50]. - Li's new venture, World Labs, aims to tackle these challenges by developing innovative solutions for understanding and generating 3D environments, indicating a commitment to advancing the field of AI [29][35].