李飞飞博士，常被誉为“人工智能之母”，在最近的一次播客访谈中探讨了人工智能的演变、影响和未来。她强调，人工智能尽管名称如此，却以人为本，它的灵感来源于人，由人创造，并影响着人。李飞飞倡导以负责任和合乎伦理的方式开发和部署人工智能，强调其最终影响取决于人类的选择。 她追溯了人工智能的历史，从20世纪50年代的早期，强调了像艾伦·图灵这样的先驱人物，到20世纪末机器学习的兴起。她解释说，一个关键的转折点是人们意识到机器需要从数据中学习模式，从而超越纯粹基于规则的程序。这一认识促成了她自己对ImageNet的开创性工作，该项目涉及整理一个包含1500万张带有标记对象的图像的大型数据集。 李飞飞强调了“大数据”在训练人工智能模型中的关键作用，将其与人类的学习和进化进行了类比。事实证明，ImageNet与神经网络的进步以及GPU的使用相结合，是一种变革性的组合。2012年ImageNet挑战赛由一个使用深度学习方法的团队赢得，标志着人工智能复兴的一个关键时刻。大数据、神经网络和GPU的这种融合仍然是当前像ChatGPT这样的人工智能突破的核心基础。 李飞飞承认，尽管人工智能取得了重大进展，但远未完成。她认为“AGI”（通用人工智能）更多的是一个营销术语，而不是一个科学术语，并指出当前的人工智能系统仍然缺乏创造性外推、抽象推理和情商等关键能力。虽然用更多的数据和算力来扩大现有模型仍然重要，但李飞飞认为，进一步的创新至关重要。 她对“世界模型”的潜力表示兴奋，这些模型旨在让人工智能系统更深入地了解物理世界。她认为，这种理解对于使机器人能够与环境互动、增强人类能力以及促进科学发现至关重要。李飞飞的公司World Labs推出了“Marble”，这是第一个围绕这个愿景构建的产品，允许用户通过简单的提示创建和交互无限探索的3D世界。 李飞飞将世界模型与视频生成AI区分开来，强调它专注于空间智能、推理以及在更完整的虚拟模拟中进行交互的能力。这不仅仅是被动地观看视频，而是积极地在模拟现实中创造和参与。 她详细阐述了Marble的潜在应用，从电影制作的虚拟制作、机器人仿真和游戏开发到心理学研究中的意外用途，例如帮助患者控制恐惧症。这突显了尽早发布人工智能产品以从用户反馈中学习并发现意想不到的应用的重要性。 关于作为创始人所吸取的教训，李飞飞强调了智力上的无畏精神和对使命的深刻承诺的重要性。她建议人工智能领域的年轻人才专注于自己的热情，与一项使命保持一致，并对自己的团队充满信心，而不是过度分析工作的每一个细枝末节。人工智能领域竞争激烈，但专注于影响力和建立强大的团队是最重要的。 最后，李飞飞提到了她在斯坦福大学以人为本的人工智能研究院（HAI）正在进行的工作。HAI旨在以以人为本的框架指导人工智能的开发和应用，包括研究、教育、生态系统拓展和政策影响。她倡导弥合硅谷和政策制定者之间的差距，促进对人工智能更广泛的理解和负责任的治理。她最后强调，人工智能不仅仅是技术人员的事，而是每个人的事。至关重要的是找到一条技术永远不会剥夺人类尊严的道路，并且人类能动性在发展中占据核心位置。

Dr. Fei-Fei Li, often hailed as the "Godmother of AI," discusses the evolution, impact, and future of artificial intelligence in a recent podcast interview. She emphasizes that AI, despite its name, is deeply human-centric, inspired by people, created by people, and impacting people. Li advocates for a responsible and ethical approach to AI development and deployment, stressing that its ultimate impact rests on humanity's choices. She traces the history of AI from its early days in the 1950s, highlighting figures like Alan Turing, to the rise of machine learning in the late 20th century. A key turning point, she explains, was the realization that machines needed to learn patterns from data, moving beyond purely rule-based programs. This realization led to her own groundbreaking work on ImageNet, a project that involved curating a massive dataset of 15 million images with labeled objects. Li underscores the crucial role of "big data" in training AI models, drawing a parallel to human learning and evolution. ImageNet, coupled with advancements in neural networks and the use of GPUs, proved to be a transformative combination. The 2012 ImageNet Challenge, won by a team using a deep learning approach, marked a pivotal moment in the resurgence of AI. This convergence of big data, neural networks, and GPUs continues to be the core foundation of current AI breakthroughs like ChatGPT. Li acknowledges that while AI has made significant strides, it is far from complete. She dismisses the term "AGI" (Artificial General Intelligence) as more of a marketing term than a scientific one, noting that current AI systems still lack crucial capabilities like creative extrapolation, abstract reasoning, and emotional intelligence. While scaling up existing models with more data and compute power remains important, Li believes further innovation is essential. She expresses excitement about the potential of "world models," which aim to give AI systems a deeper understanding of the physical world. This understanding, she argues, is critical for enabling robots to interact with their environments, augmenting human abilities, and facilitating scientific discovery. Li's company, World Labs, has launched "Marble," the first product built around this vision, allowing users to create and interact with infinitely explorable 3D worlds by simply prompting. Li distinguishes world models from video generation AI, emphasizing that it focuses on spatial intelligence, reasoning, and the capability of interaction in a more complete virtual simulation. This is more than just passively watching videos, but actively creating and engaging within a simulated reality. She elaborates on Marble's potential applications, ranging from virtual production in filmmaking, robotic simulation, and game development to unexpected uses in psychology research, like helping patients manage phobias. This highlights the importance of releasing AI products early to learn from user feedback and uncover unforeseen applications. Regarding lessons learned as a founder, Li highlights the importance of intellectual fearlessness and a deep commitment to a mission. She advises young talents in AI to focus on their passion, align with a mission, and have faith in their team, rather than over-analyzing every minute aspect of a job. The AI landscape is intensely competitive, but focusing on impact and building a strong team is most important. Lastly, Li touches upon her ongoing work at Stanford's Human-Centered AI Institute (HAI). HAI aims to guide the development and application of AI with a human-centered framework, encompassing research, education, ecosystem outreach, and policy impact. She advocates for bridging the gap between Silicon Valley and policymakers, fostering a broader understanding and responsible governance of AI. She concludes by emphasizing that AI is not just for technologists but for everyone. It is crucial to find a path where technology never takes away human dignity, and human agency has a place at the heart of the development.