图片来源： Tim Ferriss Z Highlights Fei-Fei Li （李飞飞）， ImageNet 数据集创建者、 "AI 教母 " 、斯坦福大学教授，以奠定现代 AI 基石的开创性工作与深刻的人文思考，持续引领着人工智 能在技术前沿与人类价值交汇处的探索。本访谈发布于 2025 年 12 月 10 日。 普林斯顿往事：一场错过的校园重逢 主持人： 李博士，很高兴见到你。谢谢你抽时间来。 李飞飞： 嗨， Tim 。很高兴来到这里，非常期待这次对话。 主持人： 在正式录音前我们聊了一下，说实话挺神奇、也有点遗憾的是 —— 我们竟然在同一个校园待了三年，却从来没有碰到过彼此。 李飞飞： 我知道！现在我都在想你当时在哪个书院、参加了哪些社团。 主持人： 我在 Forbes College 。 李飞飞： Tim ，我也在那家图书馆工作过。我真的不明白我们怎么会没遇到。 主持人： 这太有意思了。好吧，现在我们终于见面了。 李飞飞： 你是不是改过名字？也许我们见过。 李飞飞： 天呐，我也在 Forbes 。 主持人： 好吧，给不了解我们在说什么的听众解释一下：普林斯顿有住宿书院制度，新生会被分到不同书院。 ...

艾伦·图灵(1912-1954）英国计算机科学家、数学家、逻辑学家、密码分析学家和理论生物学家，被誉为 计算机科学与人工智能之父。 自从进入这一领域，对视觉与空间智能的探索始终是指引我前行的"北极星"。正因如此，我投入多年时 间构建了ImageNet——第一个大规模视觉学习与评测数据集。它与神经网络算法、以图形处理器 （GPUs）为代表的现代计算能力一道，构成了现代人工智能诞生的三大关键要素。也正因如此，过去 十年来，我在斯坦福大学的实验室持续将计算机视觉与机器人学习相结合。更因为如此，一年多以前， 我与联合创始人贾斯丁·约翰逊（Justin Johnson）、克里斯托弗·拉斯纳（Christoph Lassner）、本·米尔登 霍尔（Ben Mildenhall）一同创立了世界实验室（World Labs）——希望第一次真正、完整地把这种可能 性变为现实。 在这篇文章中，我将尝试解释什么是空间智能，它为何重要，以及我们正在如何构建能够释放这一能力 的世界模型。这种进展，将深刻重塑创造力、具身智能，以及人类社会的整体进步路径。 【文/李飞飞，翻译/鲸生】 1950年，当计算还主要停留在自动算术和简单逻辑层面时 ...

Core Insights - Fei-Fei Li was awarded the Queen Elizabeth Prize for Engineering in Spring 2025 for her foundational contributions to computer vision and deep learning, particularly as a core advocate of the ImageNet project [1][2] - Li emphasizes that engineering is not just about computational power and algorithms, but also about responsibility and empathy, warning against the dehumanization risks posed by AI [2][12] Group 1: Achievements and Contributions - Li's research has enabled machines to perceive the world in a way that closely resembles human vision, marking a significant milestone in AI development [1][8] - The ImageNet project, initiated in 2007, has been pivotal in shifting the paradigm towards data-driven deep learning methods, which became mainstream after the 2012 ImageNet competition [8][9] Group 2: Ethical Considerations and Social Impact - Li advocates for a human-centered approach to AI, stressing that technology must align with human values and needs, and warns against the over-commercialization and militarization of AI [10][12] - She has called for the establishment of ethical regulatory mechanisms for AI, emphasizing the urgency of integrating legal frameworks to ensure responsible AI development [17][20] Group 3: Personal Background and Perspective - Li's immigrant background and experiences as a woman in technology have shaped her unique perspective on the societal implications of AI, allowing her to recognize structural biases within the tech ecosystem [4][22] - Despite her significant contributions, Li expresses discomfort with being labeled as a "godmother of AI," advocating for a broader representation of women in the field [23][29] Group 4: Challenges and Controversies - The ImageNet dataset has faced criticism for potential racial biases, prompting discussions about the ethical implications of AI training data [26][28] - Li's position as a prominent figure in AI raises questions about the balance between human-centered values and the commercial pressures of the tech industry, highlighting the complexities of her role [31][34]

Core Insights - The discussion emphasizes the dual nature of AI as both a powerful tool and a potential risk, highlighting the need for responsible management and governance of technology [1][3][29] - The next frontier in AI is identified as "spatial intelligence," which involves AI's ability to understand, perceive, reason, and interact with the three-dimensional world [1][25] - The importance of democratizing AI technology is stressed, advocating for broader access and responsible usage rather than monopolization by a few large tech companies [1][3][24] Group 1: AI's Impact and Future - AI is described as a civilization-level technology that profoundly affects various aspects of life, work, and well-being [2][28] - The potential for job displacement due to AI is acknowledged, with historical parallels drawn to past technological advancements that reshaped labor markets [28] - The need for continuous learning and adaptation by individuals, businesses, and society in response to technological changes is emphasized [28] Group 2: Governance and Responsibility - Concerns regarding the governance of superintelligent AI are raised, questioning how humanity can prevent potential crises stemming from advanced AI systems [29][30] - The necessity for international cooperation and responsible development of AI technologies is highlighted, with a call for a global awareness of the implications of AI [30][31] - The role of educators in integrating AI responsibly into learning environments is underscored, stressing the importance of preparing future generations [32][34] Group 3: Environmental Considerations - The environmental impact of AI, particularly regarding energy consumption and the need for renewable energy sources, is discussed [31][32] - The potential for innovation in energy policies to support sustainable AI development is recognized as crucial [31][32] Group 4: Personal Insights and Experiences - The speaker's journey from a challenging upbringing to becoming a leader in AI research illustrates the importance of resilience and curiosity in scientific pursuits [17][18][19] - The influence of mentors and the significance of traditional values in education and personal development are acknowledged [19][34]

Core Viewpoint - The development of AI requires fundamental technological innovation beyond just scaling laws, and the concept of Artificial General Intelligence (AGI) is seen more as a marketing term than a scientific one [1][7][9]. Group 1: AI Development Insights - The combination of neural networks, big data, and GPUs is identified as the "golden formula" for modern AI, which remains relevant today with the success of ChatGPT [4][5]. - Current AI systems struggle with tasks that are easy for humans, indicating a significant gap in achieving true creativity, abstract thinking, and emotional intelligence [8][9]. - The concept of "world models" is proposed as a key direction for future AI development, enabling better understanding and interaction with three-dimensional environments [10][17]. Group 2: Challenges in Robotics - The challenges in robotics are highlighted, particularly the difficulty in data acquisition and the complexity of operating in three-dimensional spaces, which is more challenging than autonomous driving [15][16]. - The "bitter lesson" of using simple models with vast data does not apply straightforwardly to robotics due to the unique nature of action data required for training [15][16]. Group 3: AI's Role in Society - The potential of AI to enhance human capabilities rather than replace them is emphasized, with a focus on ensuring that technology development respects human dignity and agency [18][19]. - The belief is expressed that in the AI era, everyone will have a place, highlighting the importance of inclusivity in the technological landscape [19].

Core Insights - The interview with Fei-Fei Li highlights the emergence of "world models" as the next frontier in AI over the next decade, emphasizing the importance of spatial intelligence in AI development [1][28]. Group 1: Historical Context of AI - Two decades ago, AI was in a "winter" phase, with limited public interest and funding, often referred to as "machine learning" [10][14]. - Fei-Fei Li entered the AI field during this period, focusing on visual intelligence and the need for large datasets to train models effectively [11][20]. - The creation of ImageNet, which involved collecting 15 million images across 22,000 categories, marked a pivotal moment in AI, leading to the rise of deep learning [23][24]. Group 2: The Concept of World Models - "World models" are defined as systems that can generate an infinite 3D world based on input, allowing for reasoning and interaction [37]. - The Marble platform exemplifies this concept, significantly reducing production time in various industries, including film and gaming, by allowing creators to generate navigable worlds from simple descriptions [40][43]. - The integration of spatial intelligence into AI is seen as crucial for enhancing both robotic capabilities and human understanding [39][32]. Group 3: Challenges in Robotics - The primary challenge in robotics lies in data acquisition, as robots require extensive real-world interaction data, which is difficult to obtain [44][45]. - Unlike language models that operate on text, robots must navigate and interact within a 3D environment, complicating their training [45]. - The historical context of autonomous vehicles illustrates the complexities involved in developing effective robotic systems [46]. Group 4: Fei-Fei Li's Career and Vision - Fei-Fei Li's career trajectory reflects a commitment to addressing significant problems in AI, transitioning from academia to industry and now to entrepreneurship with World Labs [47]. - Her focus on collaboration and team dynamics underscores the importance of human roles in the evolving landscape of AI [47]. - Li emphasizes that every individual has a vital role in the future of AI, regardless of their profession [47].

Core Insights - The essence of AI is not "artificial" but an extension of "intelligence," enhancing human understanding of the world [3][11] - The concept of "world models" is crucial for advancing AI, particularly in spatial and visual understanding beyond language models [4][39] - The development of AI has transitioned from skepticism to widespread acceptance, with companies now identifying as AI firms [9][30] Group 1: AI Development and Historical Context - AI's evolution has been marked by significant milestones, including the creation of ImageNet, which provided a vast dataset for training models [6][23] - The combination of big data, neural networks, and GPUs has been pivotal in the modern AI landscape, leading to breakthroughs like ChatGPT [24][25] - The early days of AI were characterized by limited public interest and funding, with a resurgence occurring in the last decade [9][19] Group 2: World Models and Their Importance - World models are foundational capabilities that enable reasoning, interaction, and world creation, essential for both AI and robotics [40][41] - The development of world models aims to bridge the gap between language understanding and spatial intelligence, enhancing AI's ability to operate in real-world scenarios [39][43] - The recent launch of Marble, a product that generates navigable 3D worlds, exemplifies the application of world models in various fields, including gaming and virtual production [53][60] Group 3: Challenges in Robotics - The "Bitter Lesson" suggests that simple models with large datasets outperform complex models with limited data, but this principle faces challenges in robotics due to data scarcity [45][47] - Robotics requires not only advanced algorithms but also physical systems and real-world applications, complicating the training process [48][49] - The current state of robotics is still experimental, with significant hurdles remaining before achieving desired capabilities [47][50] Group 4: Future Directions and Innovations - Continuous innovation is necessary for AI to reach new heights, as current models still lack capabilities like abstract reasoning and emotional intelligence [35][36] - The focus on spatial intelligence and world modeling is expected to drive future advancements in AI, particularly in enhancing human-machine collaboration [39][44] - The integration of AI into various sectors, including psychology and design, highlights its potential to transform industries and improve efficiency [60][61]

Core Insights - The article emphasizes that spatial intelligence is the next frontier for AI, which can revolutionize creativity, robotics, scientific discovery, and more [6][10][14] - It outlines the three core capabilities that a world model must possess: generative, multimodal, and interactive [4][18][19] Group 1: Importance of Spatial Intelligence - Spatial intelligence is foundational to human cognition and influences how individuals interact with the physical world [11][14] - Historical examples illustrate how spatial intelligence has driven significant advancements in civilization, such as Eratosthenes' calculation of the Earth's circumference and Watson and Crick's discovery of DNA structure [12][13] Group 2: Current Limitations of AI - Current AI models, particularly large language models (LLMs), lack the spatial reasoning capabilities that humans possess, limiting their effectiveness in understanding and interacting with the physical world [15][16] - Despite advancements, AI struggles with tasks like estimating distances and navigating environments, indicating a fundamental gap in spatial understanding [15][16] Group 3: Future Directions for AI Development - The development of world models is essential for creating AI that can understand and interact with the world in a human-like manner [18][24] - World models should be capable of generating consistent virtual worlds, processing multimodal inputs, and predicting future states based on actions [18][19][20] Group 4: Applications of Spatial Intelligence - The potential applications of spatial intelligence span various fields, including creativity, robotics, science, medicine, and education [34][35] - In creative industries, tools like World Labs' Marble platform enable creators to build immersive experiences without traditional design constraints [28][29] - In robotics, spatial intelligence can enhance machine learning and human-robot collaboration, making robots more effective in various environments [30][31] Group 5: Vision for the Future - The article envisions a future where AI enhances human capabilities rather than replacing them, emphasizing the importance of aligning AI development with human needs [26][36] - The ultimate goal is to create machines that can understand and interact with the physical world, thereby improving human welfare and addressing significant challenges [38]

Core Insights - The article emphasizes that "spatial intelligence" is the next frontier for AI, enabling machines to transform perception into action and imagination into creation [2][7] - The concept of a "world model" is identified as essential for unlocking spatial intelligence, requiring AI to generate consistent worlds that adhere to physical laws and can process multimodal inputs [3][5] Group 1: Definition and Importance of Spatial Intelligence - Spatial intelligence is described as a foundational capability for human cognition, influencing how individuals interact with the physical world [15][19] - The evolution of spatial intelligence is linked to significant historical advancements, showcasing its role in shaping civilization [21][22] Group 2: Current Limitations of AI - Despite advancements in AI, current models lack the spatial reasoning capabilities that humans possess, particularly in tasks involving distance estimation and physical interactions [22][25] - The limitations of existing AI models hinder their ability to effectively engage with the physical world, impacting their application in various fields [25][26] Group 3: Building a World Model - Constructing a world model requires three core capabilities: generative, multimodal, and interactive, allowing AI to create and manipulate virtual or real environments [27][29][30] - The development of a world model is seen as a significant challenge for the next decade, necessitating innovative approaches and methodologies [31][32] Group 4: Applications of Spatial Intelligence - The potential applications of spatial intelligence span various domains, including creative industries, robotics, and scientific research, promising to enhance human capabilities [38][48] - Specific use cases include revolutionizing storytelling, improving robotic interactions, and transforming educational experiences through immersive learning [40][44][49] Group 5: Future Vision - The article envisions a future where AI, equipped with spatial intelligence, can serve as a partner in addressing complex challenges, enhancing human creativity, and improving quality of life [51] - The collaborative effort of the entire AI ecosystem is deemed essential for realizing this vision, highlighting the need for collective innovation and development [39][50]

Core Insights - The article emphasizes the importance of spatial intelligence as the next frontier in AI, which will fundamentally change how humans interact with both the real and virtual worlds [5][8][16] - It outlines the need for a new type of generative model, termed "world models," that can understand, reason, generate, and interact within complex environments [17][18][22] Summary by Sections Definition and Importance of Spatial Intelligence - Spatial intelligence is described as a foundational aspect of human cognition, enabling interaction with the physical world and driving creativity and imagination [10][13] - The article highlights historical examples where spatial intelligence has led to significant advancements in civilization, such as Eratosthenes' calculation of the Earth's circumference and Watson and Crick's discovery of DNA's structure [11][12] Current State of AI and Limitations - Despite advancements in AI, particularly in generative models, there remains a significant gap in AI's spatial capabilities compared to human intelligence [14][15] - Current AI models struggle with tasks involving physical interactions and spatial reasoning, limiting their effectiveness in real-world applications [15][21] Vision for Future AI Development - The article proposes that achieving spatial intelligence in AI requires developing world models with three core capabilities: generative, multimodal, and interactive [18][19][20] - It stresses the need for innovative training methods, large-scale data, and new model architectures to overcome existing limitations [23][24][25] Applications of Spatial Intelligence - The potential applications of spatial intelligence span various fields, including creativity, robotics, science, healthcare, and education [29][38] - In creativity, tools like World Labs' Marble platform empower creators to build immersive narratives and experiences [32] - In robotics, spatial intelligence is essential for robots to effectively interact with their environments and assist humans [34][36] - In science and healthcare, spatial intelligence can enhance research capabilities and improve patient care through advanced modeling and simulation [39][40] Conclusion - The article concludes with a vision of a future where machines equipped with spatial intelligence can significantly enhance human capabilities and address complex challenges [41]