Fei-Fei Li Rejects 'Mother AI' Concept, Urges Focus on Human Agency

World Models, Spatial Intelligence, and Human-Centered AI With World Labs, CNN — Ai4

Ai4

Artificial intelligence must be engineered to augment human agency rather than replace it, as surging computational productivity will not automatically guarantee shared economic prosperity without deliberate governance.

AI-assisted summary of World Models, Spatial Intelligence, and Human-Centered AI With World Labs, CNN — original source. Check the original for full context.

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## Background This content was published on YouTube by the channel Ai4 on August 28, 2025. It features a conversation between Dr. Fei-Fei Li (Co-Founder & CEO of World Labs) and Matt Egan (Senior Reporter at CNN) exploring the intersection of spatial reasoning, the physical world, technical development, and human-centered AI design.

## TL;DR Dr. Fei-Fei Li discusses artificial intelligence as an essential civilizational technology comparable to the advent of electricity, urging society to look beyond competitive framing or fears of student cheating toward human empowerment. She highlights education, critical thinking, and spatial intelligence as key frontiers, introducing World Labs' vision of 3D spatial models and multi-universe creation. Pushing back against concepts like 'Mother AI' or doom scenarios, Li advocates for human agency, ethical governance, and addressing urgent realities like energy demands, information integrity, and shared economic prosperity.

## Atmosphere & style The dialogue carries an intellectually grounded, candid, and forward-looking tone, balancing deep academic insight with engaging accessibility. Dr. Li brings self-deprecating humor and warm, pragmatic authority as a professor and entrepreneur, respectfully challenging common media tropes like sports analogies or sci-fi tropes. The interaction feels like an inspiring and thoughtful keynote exchange that elevates the conversation from alarmism or corporate hype to human dignity and civilizational responsibility.

## Key takeaways - [00:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=4s) AI should be viewed as a foundational civilizational technology akin to electricity in the 1870s, rather than a competitive sports game with winners and losers. - [02:00](https://www.youtube.com/watch?v=yleXdrvFfaE&t=120s) Education across all levels—from K-12 to lifelong workforce upskilling—remains the most critical, under-resourced, and high-impact frontier for AI integration. - [04:54](https://www.youtube.com/watch?v=yleXdrvFfaE&t=294s) Educational frameworks should abandon the defensive fixation on AI 'cheating' and instead adopt a Socratic prompting model where AI provides a baseline that students build upon with critical thinking. - [07:55](https://www.youtube.com/watch?v=yleXdrvFfaE&t=475s) The industry obsession with 'AGI' is largely redundant since AI's founders always aimed for generalized cognition; governance should focus on Human-Centered AI built, used, and governed by people. - [10:48](https://www.youtube.com/watch?v=yleXdrvFfaE&t=648s) Spatial intelligence bridges computational models with physical 3D world reasoning, unlocking multi-universe applications spanning precision surgery, autonomous mobility, and interactive commerce. - [15:41](https://www.youtube.com/watch?v=yleXdrvFfaE&t=941s) For nearly one billion years, biological life has been confined to a single physical 3D universe; generative spatial AI allows humans to become creators and navigators of multiple digital universes. - [17:46](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1066s) A pragmatic 'boring middle' approach rejects both utopian hype and doomsday panic, recognizing AI as a double-edged tool that should dismantle rote standardized testing and absorb hazardous labor. - [22:06](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1326s) Relying on a protective 'Mother AI' as proposed by Geoffrey Hinton is a flawed premise; humans must never surrender individual dignity, agency, or governance to algorithmic tools. - [24:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1444s) The most urgent near-term AI threats stem from human weaponization—specifically synthetic media undermining democratic processes and rapid structural shifts in the labor market. - [26:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1564s) Frontier AI model training now demands nation-state levels of energy, with top clusters reaching roughly two gigawatts of power consumption. - [26:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1564s) Technological productivity gains do not automatically guarantee shared prosperity, requiring deliberate societal and policy efforts to ensure widespread economic benefit.

## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=4s) AI as a Civilizational Technology, Not a Baseball Game

CNN senior reporter Matt Egan opens the session by welcoming Dr. Fei-Fei Li, calling her "literally one of the most qualified people on the planet to discuss the AI revolution." Li responds with self-deprecating humor, noting that as a Stanford professor she normally dreads teaching 8:00 AM classes, praising the audience for filling the room so early.

When Egan asks where the AI revolution currently stands using a sports metaphor—asking what inning of a nine-inning baseball game we are in—Li immediately rejects the premise. She explains that AI is not a competitive sport where there are winners and losers at the final whistle: "AI is not a sports competition because I would not like to see losers at the end of this. Humanity needs to win." Rather than a game, she frames artificial intelligence as a collective journey and a profound "civilizational technology."

To contextualize where humanity stands, Li draws a direct parallel to the rise of electricity roughly 150 to 180 years ago. Around 1870, electricity was evolving from a pure scientific curiosity into a technology and commercial product. If someone had asked an 1870s inventor which inning electricity was in, any numerical answer would have missed the point entirely, given that electricity continues to power global civilization over a century and a half later. Li asserts that AI is at that exact nascent stage—the very beginning of a foundational technology that will power humanity's future for generations.

```mermaid flowchart LR accTitle: Evolution of Civilizational Technology subgraph S1["19th Century Benchmark"] E1["Scientific Discovery<br/>(Electricity ~1870)"] --> E2["Technology & Productization"] --> E3["Pervasive Civilizational Infrastructure"] end subgraph S2["21st Century AI Trajectory"] A1["Foundational Science<br/>(Turing, McCarthy, Minsky)"] --> A2["Deep Learning & GenAI<br/>(Present Day: Early Inception)"] --> A3["Human-Centered Civilizational Engine"] end ```

---

[02:00](https://www.youtube.com/watch?v=yleXdrvFfaE&t=120s) The Underappreciated Frontier: AI and Education

Reflecting on the evolution of AI, Li reminds the audience of its roots as an intellectual quest. It started when daring pioneers—from Alan Turing to her late Stanford colleague John McCarthy—asked the fundamental question: "Can machines think?" That question stemmed entirely from raw human curiosity. Li feels fortunate to belong to the generation that saw this academic curiosity scale into pivotal breakthroughs, moving from data-driven machine learning and deep learning to modern generative AI.

Despite AI dominating headlines, podcasts, boardrooms, and policy agendas in Washington, D.C., Li argues that the most critical, under-resourced, and underappreciated frontier is AI in education. This spans early childhood and K-12 schooling, higher education, and continuous lifelong workforce upskilling.

For Li, artificial intelligence must never be designed or deployed to replace human agency. Instead, using AI to transform learning paradigms for students and teachers represents "this decade's most important challenge and opportunity."

- **Foundational Pioneers Cited:** Alan Turing, John McCarthy, and Marvin Minsky. - **Key Milestones:** Pure scientific inquiry $\rightarrow$ data-driven machine learning $\rightarrow$ deep learning $\rightarrow$ generative AI. - **Core Stance:** Education and human agency empowerment remain severely under-resourced compared to commercial AI development.

---

[04:54](https://www.youtube.com/watch?v=yleXdrvFfaE&t=294s) Reframing Education: Moving Beyond "Cheating" to Socratic Prompting

Addressing the current educational landscape, Li shares that just the day prior, she hosted senior congressional staffers from both political parties and both houses of Congress at Stanford. Her conclusion from those discussions is clear: neither policymakers, school systems, parents, nor students have settled on a coherent approach to AI-powered education. Because public discourse is clouded by doomsday hype, the immediate, knee-jerk reaction in schools toward AI has centered on the fear of student "cheating."

Li strongly repudiates that mindset, calling cheating "absolutely the wrong word" to center the conversation around. Instead, educators and parents should focus on empowerment. She draws an analogy between modern prompt engineering and the Socratic method, asking: "You know who is humanity's first prompter? It's Socrates." More than 2,000 years ago, Socrates demonstrated that the essence of learning lies in asking the right questions.

```mermaid flowchart TD accTitle: Socratic Learning Framework with AI subgraph Defensive["Legacy Mindset (Defensive)"] D1["AI Output Seen as 'Cheating'"] D2["Passive Consumption / Bans"] end subgraph Socratic["Human-Centered Socratic Model"] S1["Socratic Prompting<br/>(Inquiry & Questioning)"] S2["AI as Baseline Generator<br/>(Math, Science, Writing)"] S3["Human Agency & Critical Reasoning<br/>(Pushing Beyond the Baseline)"] S1 --> S2 --> S3 end ```

Rather than treating AI outputs as illicit shortcuts, students should view generative models as a baseline for any math problem, scientific query, or essay draft. From that baseline, human agency, creativity, and critical thinking take over to explore territory that no supercomputer can reach on its own.

- **Primary Diagnostic:** Public debate suffers from a knee-jerk reaction fixated on academic dishonesty rather than pedagogy. - **The Socratic Analogy:** Prompting is fundamentally the ancient art of inquiry—interrogating a concept to deepen understanding. - **Actionable Framework:** Treat AI as the starting baseline, requiring the student's unique intellect to build upon, critique, and elevate the work.

---

[07:55](https://www.youtube.com/watch?v=yleXdrvFfaE&t=475s) Human-Centered AI Versus the Race to AGI

When asked why she pushes back against the industry obsession with "Artificial General Intelligence" (AGI) in favor of "Human-Centered AI," Li sets the record straight: she does not believe AGI is inherently bad, but rather views the term as redundant. When Turing, McCarthy, and Minsky established the discipline, they never set out to build narrow or domain-restricted machines; their foundational goal was generalized machine cognition. As a scientist, she views "AI" and "AGI" as functional synonyms, playfully praising the conference organizers: "Kudos to Dan and friends. This is AI4, not AGI4."

Li explains the rationale behind penning her 2018 *New York Times* op-ed and co-founding the Stanford Institute for Human-Centered AI (HAI). Because AI is a civilizational technology impacting employment, corporate productivity, and family life, technology cannot sit at the center of the equation—humanity must.

Li's core governing philosophy rests on a tripartite pillar: 1. AI is **created by people**. 2. AI is **used by people**. 3. AI must be **governed by people**.

Human-centered AI means deliberately and responsibly deploying technology to augment, enhance, and elevate the human condition rather than disempower it, serving as a north star for corporate executives, researchers, and government regulators alike.

---

[10:48](https://www.youtube.com/watch?v=yleXdrvFfaE&t=648s) Spatial Intelligence and World Labs: Navigating Multiple Digital Universes

Shifting the conversation to her startup, World Labs, and the emerging domain of spatial intelligence, Egan asks about Li's vision where AI creates "infinite universes" and humans operate within a multiverse.

Li illustrates this concept through a concrete day-in-the-life narrative of a future surgeon: - **The Operating Room:** When preparing for a delicate procedure, the surgeon puts on augmented reality goggles or enters a digital interface. AI reconstructs the patient's internal anatomy in high-fidelity 3D space, allowing the surgeon to visualize and navigate microscopic blood vessels with far greater precision and safety than the naked eye could ever achieve. - **The Autonomous Commute:** After saving a life, the exhausted doctor steps into an autonomous vehicle. The vehicle operates inside its own digital universe—a real-time, spatial model of dynamic roadways, physical obstacles, and traffic conditions. - **Everyday Life:** Later, shifting from surgeon to parent, she enters an immersive virtual retail environment to select a prom dress for her child.

In this paradigm, spatial intelligence bridges raw computational models with physical, 3D world understanding, creating dedicated digital environments that augment human capability across work, mobility, and personal life.

[12:58](https://www.youtube.com/watch?v=yleXdrvFfaE&t=778s) The Vision of 3D Worlds and Spatial Intelligence

Fei-Fei Li paints a picture of a near future shaped by immersive 3D digital environments. Speaking from her perspective as a busy mother who has "no time for shopping," she describes an e-commerce universe where parents and children can digitally try on outfits in interactive virtual spaces before buying. Entertainment will undergo a parallel shift: rather than passively watching a movie on a flat display, audiences will use goggles, screens, or other interactive media to physically step inside the narrative—such as roaming through an alien outer-space landscape. On a deeply personal level, she envisions transatlantic family connections where digitally transported grandparents from Europe can join a living room gathering in full 3D to sing happy birthday together.

These scenarios are not isolated gimmicks; they represent a fundamental blend of the physical and digital domains into distinct, interactive universes where people can live, work, and socialize either collaboratively or solo. The technological foundation making this possible is generative 3D world creation paired with the capacity to understand, reason about, and interact with three-dimensional space—a discipline she terms "spatial intelligence." Her venture, World Labs, is built from the ground up on basic science to deliver these generative spatial capabilities. Li emphasizes that physical robotics represents an extension of this same technology, noting that robots do not need to mimic the large, menacing humanoid forms common in science fiction; rather, they can take any functional form, from autonomous vehicles to helpful home assistants.

```mermaid flowchart TD accTitle: Architecture of Spatial Intelligence and Multi-Universe Applications subgraph Core["Foundational Layer"] A["Spatial Intelligence & 3D World Models"] end subgraph Virtual["Digital & Mixed Universes"] B["Interactive E-Commerce & Virtual Try-Ons"] C["Immersive Cinema & Spatial Storytelling"] D["Telepresence & Shared Social Spaces"] end subgraph Physical["Physical Embodiment"] E["Autonomous Vehicles"] F["Assistive & Industrial Robotics"] end

A --> Virtual A --> Physical Virtual --> B Virtual --> C Virtual --> D Physical --> E Physical --> F ```

[15:41](https://www.youtube.com/watch?v=yleXdrvFfaE&t=941s) Moving from Single-Universe Species to Multi-Universe Creators

When asked how far humanity is from realizing this multi-universe future, Li quips about recurring baseball-inning analogies before explaining that the transition is already underway. Her decision to step outside the academic "ivory tower" as a Stanford professor to become an entrepreneur alongside her former students and colleagues stems from the conviction that spatial intelligence is at the cusp of turning into commercial products, accelerating creative workflows, and powering high-fidelity robotic simulations.

Reflecting on the deeper evolutionary significance of this milestone, Li highlights a staggering historical contrast: for nearly one billion years of biological life on Earth, every living species has been confined to experiencing a single, tangible 3D universe. Through the digital revolution and spatial AI, humanity is set to become the first species capable of creating and inhabiting multiple 3D universes, opening unbounded horizons for human augmentation and exploration.

- **Evolutionary context**: For approximately one billion years, living organisms have interacted solely within one physical 3D world. - **Mission**: Digital multi-universes are engineered not to replace reality, but to augment human agency, creativity, and connection.

[17:46](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1066s) The "Boring Middle": Education, Human Creativity, and Robotics

Addressing the broader societal impacts of artificial intelligence, Li distances herself from both "doomsaying" catastrophists and "delusional utopian" optimists, positioning herself firmly in the "boring middle." She notes that throughout history, every human innovation—from fire and the stone axe to advanced AI—has functioned as a double-edged sword. As a civilizational technology, AI holds enormous potential to revamp foundational social institutions, chief among them education. Li expresses frustration with the current state of schooling, arguing that society should not squander its most precious human capital—children—by training them for years to pass memory-based, standardized exams that contemporary AI can already ace. Instead, 21st-century education must be systematically redesigned around critical thinking and human potential.

``` Extreme Rhetoric vs. Pragmatic Middle ┌────────────────────┐ ┌────────────────────┐ ┌────────────────────┐ │ Delusional Utopian │ │ The Pragmatic │ │ AI Doomsaying │ │ "AI solves all │ ──│ "Boring Middle" │── │ "AI destroys │ │ problems without │ │ Civilizational tool│ │ humanity and │ │ friction" │ │ with dual edges │ │ agency" │ └────────────────────┘ └────────────────────┘ └────────────────────┘ ```

Li also connects AI directly to the essence of human creativity. Reflecting humorously on a recent vacation to Africa, she remarks that purely as biological animals, humans "kind of suck" and are "kind of useless" compared to wildlife: humans cannot run fast, fly, or swim proficiently. However, our defining superpower is creative imagination and curiosity. AI should therefore serve as a force multiplier across every domain—empowering scientists, artists, writers, musicians, doctors, and entrepreneurs. In industry, spatial intelligence will enable robots to absorb hazardous, physically damaging, or mind-numbing repetitive tasks, freeing people to focus on higher-order creative work while expanding the global economic pie.

- **Education reform**: Calls for historians looking back in 50 to 100 years to recognize this era as the moment humanity dismantled rote, standardized testing in favor of deep human skills. - **Labor allocation**: Machines should handle dangerous, repetitive, or uncreative work, rather than stripping away functions humans uniquely excel at.

[22:06](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1326s) Pushing Back on "Mother AI": Preserving Human Dignity and Agency

Matt Egan brings up comments made on the same stage the previous day by Geoffrey Hinton—often hailed as the "Godfather of AI"—who suggested that humanity's best chance of avoiding subjugation by superintelligent systems is to endow them with a "maternal instinct" or create a "mother AI" that protects us. While affirming her deep personal respect for Hinton as a longtime mentor and friend of more than two or three decades, Li explicitly and respectfully pushes back against this framing.

She rejects the notion of relying on a superior, protective AI entity, arguing that the entire discussion must be anchored in "human-centered AI." In Li's view, technology must preserve and elevate human dignity and agency across the individual, community, and societal tiers. Humans must never abdicate their responsibility to govern tools ethically, nor should any person surrender their self-determination simply because an algorithmic tool exhibits immense computational power.

> "At no moment, not a single human should be asked or should choose to let go our dignity and agency just because a tool is powerful. And as a mother, as a educator, and as a inventor, I really believe this is the core of what AI is is it should be human-centered."

[24:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1444s) Real Risks: Information Warfare, Democracy, and Workforce Disruption

Asked what truly keeps her awake at night regarding superintelligent systems, Li pivots away from abstract science fiction toward the real-world vulnerabilities created by the "dark underbelly" of human nature. Throughout history, human messiness and malice have weaponized tools against communities, and AI is susceptible to the exact same abuse.

Her foremost concerns center on the manipulation of public information and the disruption of democratic processes, emphasizing that safeguarding democratic institutions in the face of synthetic media and weaponized narratives is urgent. Additionally, she warns of massive structural growing pains across the global labor market. Pointing directly to Silicon Valley, she observes that software engineering is currently undergoing a "seismic change," cautioning that navigating this displacement is far more complex than simply telling prospective workers to avoid the field.

[26:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1564s) Human Capital, Energy Demands, and the Distinction Between Productivity and Shared Prosperity

Addressing the broader workforce shifts, Fei-Fei Li pushes back on simplistic narratives about AI replacing workers, noting that within her own company, top software engineers are instead "superpowered by AI." The deeper challenge lies in structural adaptation: determining how people learn, how workers upskill, and how organizations approach human capital investment over the long term. Alongside workforce considerations, she underscores the massive resource footprint of cutting-edge artificial intelligence. Recounting a conversation from the previous day with OpenAI's CFO, Li highlights that the latest state-of-the-art models require roughly two gigawatts of power to train—a staggering figure considering that an entire medium-sized European nation operates on single-digit gigawatts, pushing AI infrastructure into nation-state levels of energy consumption.

Looking at the broader macroeconomic horizon, Li reflects on the historical trajectory of technological innovation over the past three decades. While Silicon Valley heavily celebrates technology's ability to drive massive global productivity gains, she cautions that higher output does not automatically translate into broad societal well-being. She emphasizes that "increasing productivity does not guarantee shared prosperity," urging technologists, business leaders, and policymakers to treat these as two distinct objectives that must both be thoughtfully pursued and implemented in the era of AI. Following her remarks, Matt Egan wraps up the keynote discussion, thanking Dr. Li for sharing her insights during such a pivotal moment for the field.

- **Human capital & engineering:** AI currently acts as a force multiplier that superpowers skilled software engineers, placing a spotlight on workforce upskilling and long-term training. - **Nation-state energy demands:** Frontier AI model training clusters consume up to 2 gigawatts of power, rivaling the entire grid demand of mid-sized European countries. - **Productivity vs. prosperity:** A critical distinction must be maintained between technological productivity gains and equitable, shared prosperity across society.

## Notable quotes > [00:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=4s) literally one of the most qualified people on the planet to discuss the AI revolution. — Matt Egan

> [00:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=4s) AI is not a sports competition because I would not like to see losers at the end of this. Humanity needs to win. — Fei-Fei Li

> [02:00](https://www.youtube.com/watch?v=yleXdrvFfaE&t=120s) Can machines think? — Alan Turing

> [02:00](https://www.youtube.com/watch?v=yleXdrvFfaE&t=120s) this decade's most important challenge and opportunity. — Fei-Fei Li

> [04:54](https://www.youtube.com/watch?v=yleXdrvFfaE&t=294s) You know who is humanity's first prompter? It's Socrates. — Fei-Fei Li

> [07:55](https://www.youtube.com/watch?v=yleXdrvFfaE&t=475s) Kudos to Dan and friends. This is AI4, not AGI4. — Fei-Fei Li

> [17:46](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1066s) kind of suck — Fei-Fei Li

> [17:46](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1066s) kind of useless — Fei-Fei Li

> [22:06](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1326s) At no moment, not a single human should be asked or should choose to let go our dignity and agency just because a tool is powerful. And as a mother, as a educator, and as a inventor, I really believe this is the core of what AI is is it should be human-centered. — Fei-Fei Li

> [24:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1444s) seismic change — Fei-Fei Li

> [26:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1564s) increasing productivity does not guarantee shared prosperity — Fei-Fei Li

## Mentioned Ai4, Alan Turing, CNN, Geoffrey Hinton, John McCarthy, Marvin Minsky, Matt Egan, New York Times, OpenAI, Socrates, Stanford Institute for Human-Centered AI (HAI), Stanford University, World Labs

## Video description Fei-Fei Li, Co-Founder & CEO, World Labs Matt Egan, Senior Reporter, CNN

Abstracts Join Fei-Fei Li for a forward-looking conversation exploring the intersection of AI’s understanding of the physical world, spatial reasoning, and the pursuit of human-centered design. This broad discussion will touch on technical, ethical, and societal dimensions shaping AI’s role in the world. Keynotes, Meals, Networking, & More"

[Source](https://www.youtube.com/watch?v=yleXdrvFfaE)

中文简报

李飞飞驳斥“AI之母”概念，呼吁聚焦人类能动性 人工智能的设计必须旨在增强而非取代人类能动性，因为若缺乏审慎治理，激增的计算生产力无法自动带来普惠的经济繁荣。 李飞飞博士将人工智能视为堪比电力问世的关键文明技术，呼吁社会跳出零和竞争思维及对学生作弊的担忧，转向赋能人类。她强调教育、批判性思维和空间智能是关键前沿，并介绍了World Labs在3D空间模型与多重宇宙构建方面的愿景。在反驳“AI之母”标签与末日论调的同时，李飞飞极力倡导人类能动性与伦理治理，并呼吁应对能源需求、信息真实性以及经济繁荣共享等紧迫现实议题。 李飞飞指出 AI 不应被视为有输赢之分的体育比赛，而是一项堪比1870年代电力的基础性“文明技术”，目前全人类正处于其长期发展的初级阶段。 (0:04) 在教育领域，李飞飞反对将 AI 简单定性为“作弊”的防御性视角，主张建立苏格拉底式的提问框架，由 AI 生成基准内容，再由学生通过批判性思维进行拓展。 (4:54) 空间智能将计算模型与 3D 物理世界推理深度结合，使人类有望从近十亿年来仅局限于单一物理世界的物种，跨越为探索并构建多重数字宇宙的创造者。 (10:48) 针对杰弗里·辛顿（Geoffrey Hinton）提出的“母性 AI（Mother AI）”庇护设想，李飞飞明确提出异议，强调人类绝不能向任何强大的算法工具让渡个体的尊严与治理能动性。 (22:06) 前沿模型训练集群能耗已逼近中型欧洲国家规模的 2 吉瓦，李飞飞警示技术带来的生产力激增绝不等于全社会的共同繁荣，治理者必须审慎处理两者之间的脱节。 (26:04)

Reading summary

Dr. Fei-Fei Li frames artificial intelligence as a foundational, civilizational technology akin to early electricity, arguing that systems must be governed strictly to augment human dignity and agency rather than replace it. Civilizational Scale and the Human-Centered Paradigm (0:04) Rejecting zero-sum competition metaphors such as baseball innings, Li contends that AI is at an inception stage comparable to electricity around 1870. She views artificial general intelligence (AGI) as a redundant label, noting that pioneers like Alan Turing, John McCarthy, and Marvin Minsky always aimed for generalized machine cognition. Because AI is a civilizational technology rather than a sport with winners and losers, Li argues that humanity as a whole must benefit. Her human-centered framework relies on three principles: AI is created by people, used by people, and must be governed by people. Reforming Education and Socratic Inquiry (2:00) Li identifies education as the most critical and underfunded frontier in AI, spanning early childhood schooling to continuous workforce training. Following discussions with congressional staffers, she observes that public debate remains trapped in defensive reactions centered on student cheating. Li rejects this framing, comparing modern prompt engineering to the Socratic method established over 2,000 years ago. She argues that educational institutions should stop training students for standardized tests that AI can easily pass, and instead treat generative AI outputs as a baseline for students to critique, elevate, and expand through critical reasoning. Spatial Intelligence and Generative 3D Worlds (10:48) Through her venture World Labs, Li is pursuing spatial intelligence—the capacity for AI models to reason about, generate, and interact with physical 3D spaces. While living organisms have experienced only a single physical world for nearly one billion years, Li suggests that spatial AI enables humans to create and navigate multiple digital universes. Practical applications include high-fidelity surgical navigation of blood vessels in augmented reality, dynamic spatial models for autonomous driving, interactive 3D commerce, and shared social environments. This spatial reasoning also provides the algorithmic foundation for physical robotics, ranging from assistive home devices to autonomous vehicles. Agency, Governance, and Immediate Structural Risks (17:46) Positioning herself in a pragmatic "boring middle" between doom-laden catastrophism and uncritical utopianism, Li explicitly rejects the idea of relying on a protective "mother AI" or maternal instinct to safeguard humanity, as suggested by Geoffrey Hinton. She insists that humans must never surrender self-determination or ethical oversight to powerful computational tools. Instead of distant speculative threats, Li highlights immediate dangers rooted in human misuse: synthetic media, information warfare, threats to democratic institutions, and seismic disruption in fields like software engineering. Energy Consumption and Macroeconomic Equity (26:04) The rapid scale of frontier AI carries unprecedented infrastructure requirements. Citing figures discussed with OpenAI's CFO, Li notes that training state-of-the-art models now requires roughly two gigawatts of electrical power—an energy footprint comparable to the grid consumption of entire mid-sized European nations. On an economic level, she cautions that while technology drives efficiency, increased productivity does not automatically guarantee shared prosperity. Tech leaders and policymakers must deliberately decouple the two concepts and ensure technological gains result in broad societal well-being. Li concludes that navigating the current technological transformation requires massive investment in workforce upskilling, responsible environmental resource management, and deliberate policy choices that align algorithmic productivity with equitable economic outcomes.

精炼阅读摘要

斯坦福大学教授、World Labs联合创始人李飞飞（Fei-Fei Li）指出，人工智能并非以输赢定局的体育赛事，而是一项类似19世纪电力的深远“文明级技术”；她主张超越通用人工智能（AGI）的概念炒作，将重心聚焦于以人为本的自主权、空间智能创新以及教育体系的深层重构。 文明级技术定位与以人为本的治理哲学 (0:04) 针对将AI发展比作棒球赛程第几局的流行隐喻，李飞飞明确否定了这种零和竞争框架，强调AI关乎人类整体文明走向，最终目标必须是全人类共同获益。她将当下AI的发展阶段与约150至180年前（约1870年）的电力革命相类比：彼时电力正由纯粹的基础科学探索跨入技术与商业产品化阶段，若向当时的先驱追问赛程局数便毫无意义，因为电力至今仍在驱动全球文明运转。从图灵（Alan Turing）、麦卡锡（John McCarthy）等先驱探索“机器能否思考”，到机器学习、深度学习及当代生成式AI的突破，这项技术刚刚处于漫长文明进程的起点。 在这一背景下，李飞飞认为行业当前对“通用人工智能”（AGI）的狂热追求在概念上属于多余冗余，因为创立人工智能学科的初衷即是通用机器认知能力。她重申创办斯坦福以人为本人工智能研究院（HAI）的核心哲学：AI必须由人创造、由人使用、由人治理。李飞飞同时明确反驳了杰弗里·辛顿（Geoffrey Hinton）前一日提出的通过赋予系统“母性本能”（Mother AI）以守护人类免遭奴役的假说；她强调，无论算法工具的计算能力多么强大，人类在任何时刻都不应放弃自身的尊严与能动性，治理责任绝不能托付给外在的超级实体。 教育范式重塑：苏格拉底式提问与能力释放 (4:54) 李飞飞指出，尽管AI占据了商业资本、科技媒体与国会立法的焦点，但教育领域（涵盖幼儿与K-12教育、高等教育及终身职业再培训）依然是当前资源投入最匮乏、被严重低估的关键前沿。在与美国两党国会高级幕僚的闭门研讨中，她发现决策层、教育系统及家长普遍缺乏一致连贯的AI教育方案，往往受末日论等极端情绪干扰，陷入将AI视作学生“作弊”工具的防御性思维。李飞飞批评这一视角严重窄化了技术潜力，指出苏格拉底早在两千年前就通过提问证明了学习的核心在于提出正确的问题，堪称人类历史上首位“提示词工程专家”。 她提出一种以人为本的教学模式：学生应将生成式AI的运算输出视为各类数学解答、科学探索或写作草稿的“基准线”（baseline），而非走捷径的终点。教育的核心在于引导学生立足于基准线之上，施展人类独有的批判性思维、能动性与创造力，深入超级计算机无法企及的领域。李飞飞呼吁教育体制从根本上摆脱死记硬背与标准化测试，停止将最宝贵的年轻人才消耗在现有AI早已能轻易通过的记忆型考试上，转而全面培养深层的人文与创造潜能。 空间智能与多重数字宇宙的物理延伸 (12:58) 围绕其创立的初创公司World Labs，李飞飞详细阐释了“空间智能”（Spatial Intelligence）的愿景——即赋予AI理解、推理并生成三维物理与数字空间的能力。李飞飞从进化生物学视角提出，在地球生命诞生以来的近十亿年中，所有生物都受限于单一的三维物理现实；而空间智能与三维生成模型的成熟，将使人类成为历史上首个能够创造并穿梭于多重三维数字宇宙的物种。走出学术象牙塔投身创业，正是源于她确信空间智能已到达赋能创意工作流和推动商业产品化的关键拐点。 李飞飞描绘了空间智能在多领域的应用前景：外科医生佩戴增强现实设备，利用AI高精度重构的人体微观三维血管网络进行导航手术，突破肉眼极限；在个人与家庭生活层面，空间智能支持沉浸式虚拟试衣、跨大西洋亲属以三维全息形式参与起居室庆生，或让观众身临其境地步入数字叙事空间。在实体世界中，物理机器人并非科幻作品中冷酷的庞大人形机械，而是空间智能的物理延伸，涵盖自动驾驶汽车与各类功能性家用助手，旨在接管危险、有害及重复枯燥的体力劳动，释放人类专注更高阶的创造性活动。 现实风险防御、算力能耗与共享繁荣挑战 (24:04) 在极端末日论与无摩擦乌托邦论之间，李飞飞将自己定位在务实的“无聊中道”（boring middle）。她指出，火与石斧等一切历史工具皆为双刃剑，真正令人担忧的风险并非虚构的超级智能叛变，而是人性阴暗面对技术的恶意武器化。当前最紧迫的威胁在于合成媒体对公共信息的操纵，进而对民主制度构成系统性侵蚀；与此同时，全球劳动力市场正面临剧烈震荡，例如硅谷顶尖软件工程师的工作形态正经历地震般的变化，单纯告诫年轻劳动力避开特定行业无法解决长期的结构性失业隐患。 李飞飞进一步揭示了尖端AI训练面临的严峻资源壁垒。援引与OpenAI首席财务官的交流数据，她指出当前顶尖前沿模型的训练集群功耗已达约2吉瓦（GW），相当于一个欧洲中等国家的电网总需求，将AI基础设施推向了国家级能源消耗水平。在宏观经济层面，李飞飞反思了过去三十年硅谷对生产力增长的单一崇拜，强调提高生产力绝不自动等同于实现社会各阶层的共享繁荣（shared prosperity），政策制定者与商业领袖必须将技术产出与财富分配作为两个独立议题审慎推进。 李飞飞阐明了以人为本发展人工智能的务实路径：将AI定位于辅助而非取代人类能动性的文明级基础设施。然而，如何化解前沿大模型高达国家级电网负荷的能源瓶颈、遏制数字信息战对民主制度的现实冲击，以及构建能将单点生产力有效转化为全社会共享繁荣的分配与再培训机制，仍是当前技术界与政策层尚未解决的核心挑战。

Detailed notes — zh

## Background 本内容由 Ai4 于 2025 年 8 月 28 日发布在 YouTube 平台上。页面描述显示，这是 CNN 资深记者 Matt Egan 与 World Labs 联合创始人兼 CEO、斯坦福大学教授李飞飞展开的一场前瞻性对话，重点探讨 AI 对物理世界的理解、空间智能以及以人为本的设计原则，并涵盖塑造 AI 未来角色的技术、伦理与社会维度。 ## TL;DR 在对话中，李飞飞反驳了将 AI 竞争视为零和博弈的论调，将其定义为类似电力崛起的“文明级技术”，并呼吁社会高度重视最被低估的“AI+教育”范式重塑。她阐述了从图灵时代一脉相承的“以人为本的 AI”理念，坚决维护人类尊严与能动性，驳斥了依赖“母性 AI”庇护的设想。同时，她拆解了 World Labs 致力于突破的“空间智能”如何让人类历史上首次跨入并交互于“多重 3D 宇宙”，赋能医疗、具身机器人与数字生活。最后，她直面信息操纵、软件工程剧变以及前沿大模型高达国家级的 2 吉瓦能耗挑战，警示科技界生产力提升绝不直接等同于共同繁荣。 ## Atmosphere & style 对谈在轻松幽默与严谨深邃之间自如切换，李飞飞既有大学教授娓娓道来的亲和力与自嘲智慧，又展现出顶级科学家兼创业者的清醒与决断。面对主持人关于赛程局数或科幻隐喻的追问，她始终以宏大的演化历史与哲学定力破除极化叙事，呈现出理性的“中间派”沉稳。整场对话既充满对空间智能无限宇宙的壮阔想象，又在触及劳动力阵痛、民主侵蚀与能源危机时流露出深切的现实关怀与警醒感。 ## Key takeaways - [00:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=4s) AI 并非零和博弈的体育竞技，而是一项如同 1870 年代电力一般的“文明级技术”，人类必须全员获胜，且当前正处于发展的最初起点。 - [02:02](https://www.youtube.com/watch?v=yleXdrvFfaE&t=122s) 在当前的 AI 叙事中，最关键却被严重低估的领域是“AI 与教育”，重塑教育体系以赋能师生是这一个十年中最重大的挑战与机遇。 - [05:16](https://www.youtube.com/watch?v=yleXdrvFfaE&t=316s) 教育界应摒弃“防范作弊”的消极防御思维，转而借鉴苏格拉底式的提问艺术（Prompt），将 AI 生成的结果视为思考基线并发挥人类独有的能动性超越其上。 - [07:55](https://www.youtube.com/watch?v=yleXdrvFfaE&t=475s) AI 从创立之初追求的就是通用智能，无需刻意区分 AI 与 AGI；技术发展必须坚守“以人为本（Human-Centered AI）”原则，确保 AI 由人类创造、为人类所用、受人类治理。 - [10:48](https://www.youtube.com/watch?v=yleXdrvFfaE&t=648s) World Labs 致力于探索空间智能（Spatial Intelligence），通过赋予 AI 对 3D 几何与物理规律的生成和理解能力，构建数字与物理无缝交织的多元宇宙应用。 - [12:58](https://www.youtube.com/watch?v=yleXdrvFfaE&t=778s) 空间智能将全面赋能沉浸式电商、交互影视娱乐、全息跨地域社交，以及包含自动驾驶和人形机器人在内的多形态辅助机器人生态。 - [15:41](https://www.youtube.com/watch?v=yleXdrvFfaE&t=941s) 在地球近 10 亿年的生命演化史上，生物始终局限于单一物理 3D 宇宙，而数字化与空间智能使人类首次成为能够穿梭于多重 3D 宇宙的物种。 - [17:28](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1048s) 李飞飞自处“无聊的中间派”，既反对末日论也反对盲目乐观，主张借助 AI 终结死记硬背的应试教育，释放人类以好奇心驱动的原生创造力。 - [22:06](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1326s) 明确反驳 Geoffrey Hinton 提出的“母性 AI（Mother AI）”构想，坚信无论工具多么强大，人类在任何时刻都绝不能放弃自身的尊严与能动性。 - [24:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1444s) 当前最紧迫的现实风险在于 AI 被用于信息操纵和侵蚀民主制度，以及劳动力市场（如软件工程领域）正在经历的断层级剧烈震荡。 - [26:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1564s) 前沿模型训练能耗已达到 2 吉瓦（相当于欧洲中等国家整体能耗），AI 发展正面临国家级能源负荷考验与全社会人力资本技能重塑（upskill）的深层挑战。 - [26:43](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1603s) 警惕技术狂热与单一视角——全球生产力的飞跃并不等同于、也不保证能够带来共同繁荣（shared prosperity），健全社会必须审慎兼顾两者。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=4s) AI 革命的序幕：文明级技术而非零和博弈 在大会开场中，主持人 Matt Egan 表达了能与李飞飞（Fei-Fei Li）对谈的荣幸，称她是全球最具备资格探讨这场 AI 变革的人士之一。李飞飞以教授特有的幽默回应，调侃道自己作为大学教授，平时最怕上早八点的课，因此由衷钦佩一早齐聚现场的听众。随后，Matt Egan 抛出了一个经典的商业与科技隐喻，询问如果把当前的 AI 革命比作一场九局制的棒球比赛，我们现在正处于第几局。 ```mermaid flowchart LR accTitle: 技术演进类比图 subgraph S1["电力技术演进 (1870s)"] A["基础科学探索"] --> B["工程技术突破"] --> C["产品与基础设施普及"] end subgraph S2["AI 革命进程 (当下)"] D["科学与好奇心驱动"] --> E["深度学习与生成式 AI"] --> F["文明级赋能与人类共赢"] end ``` 李飞飞并未直接给出局数，而是旗帜鲜明地反驳了“棒球比赛”这一比喻。她强调，AI 绝不是一场充满输赢对抗的体育竞技，“因为在这场变革的终点，我不希望看到任何输家；人类必须全员获胜。”在她看来，AI 是一场全人类共同踏上的旅程，一项真正的“文明级技术”（civilizational technology）。她将当前的 AI 发展与 150 至 180 年前（即 1870 年代前后）电力的崛起相类比——当时电力正经历从前沿科学走向工程技术、再转化为实际产品的漫长转变。 - **文明级技术的定义**：如同电力在过去一个多世纪中持续为人类文明提供动力一样，AI 也将作为基础设施级的核心动力，深刻赋能人类的未来。 - **发展阶段**：如果一定要给出一个定位，我们当前正处于这一文明级技术的“最初起点”（the very beginning）。 --- ### [02:02](https://www.youtube.com/watch?v=yleXdrvFfaE&t=122s) AI 演进中最被低估的领域：AI 与教育 当被问及在 AI 演进的长河中，哪一个维度最重要却最被社会低估时，李飞飞回顾了这门学科的初心。她指出，AI 的起点是纯粹的科学探索与人类好奇心——从艾伦·图灵（Alan Turing）到约翰·麦卡锡（John McCarthy）等学科奠基人，他们以非凡的胆识向人类提出了一个核心命题：“机器能够思考吗？”随着深度学习、数据驱动的机器学习乃至当下的生成式 AI（GenAI）相继爆发，AI 已经展现出能成倍提升人类工作与生活能力的超级力量。 然而，李飞飞敏锐地指出，尽管当下从华盛顿特区政界到各大企业董事会、从播客节目到社交媒体，到处充斥着关于 AI 的喧嚣讨论，但有一个关乎社会根基的议题却严重缺乏探讨与资源投入——那就是 **AI 与教育（AI and Education）**。这涵盖了个性化教育、K-12 基础教育、企业培训乃至终身学习技能重塑。 - **核心使命**：只要人类拥有坚定的意志，AI 的定位就绝非取代人类的能动性（human agency），而是全面赋能人类。 - **时代机遇**：利用 AI 彻底重塑教育体系，深度赋能学生与教师的教学与学习过程，是“这一个十年中最重大的挑战与机遇”。 --- ### [05:16](https://www.youtube.com/watch?v=yleXdrvFfaE&t=316s) 重塑提示词理念：从防范“作弊”到苏格拉底式提问 针对当前教育界应对 AI 的方式以及 Prompt（提示词工程）的重要性，李飞飞分享了她前一天在斯坦福大学与美国参众两院跨党派高级幕僚的闭门交流感受。她直言，无论是国家层面、教育系统，还是家长、教师与学生个体，目前整个社会其实并未形成一套成熟的“AI 赋能学习”方案。造成这种局面的原因之一，是外界对“AI 末日论”等杂音的过度渲染，导致教育界对 AI 产生了一种膝跳反应式的负面防御心理。 ```mermaid flowchart TD accTitle: AI 教育理念转变对比 subgraph C1["传统防御视角（错误路径）"] A1["视 AI 为作弊工具"] --> B1["限制、封禁与被动防守"] end subgraph C2["人类赋能视角（正确路径）"] A2["苏格拉底式提问 (Prompting)"] --> B2["AI 提供基线答案 (数学/科学/写作)"] --> C3["发挥人类独有智慧与能动性，超越基线"] end ``` 李飞飞强调，当人们谈及 AI 与教育时，脑海中浮现的第一个词往往是“作弊”（cheating），这完全用错了词；真正核心的关键词应当是“赋能”（empowering）。关于如何通过 Prompt 展开学习，她生动地溯源至古代哲学： > “你知道谁是人类历史上第一位‘提示词工程师’（prompter）吗？是苏格拉底。两千多年前，正是他教会了我们如何通过提问来启发思考。提问的艺术正是人类学习的核心。” 在她看来，AI 给出的无论是一道数学题解法、科学问题推演还是一篇英语范文，都仅仅是提供了一个“基线”（baseline）。教育应当引导学生认识到：作为拥有独立思考能力与自主能动性的人类，拥有任何超级计算机和 AI 都无法企及的智慧；站在 AI 提供的基线之上，人类应当思考“借助这一工具，我能走得多远”。 --- ### [07:55](https://www.youtube.com/watch?v=yleXdrvFfaE&t=475s) AGI 与以人为本的 AI（Human-Centered AI） 谈到硅谷、华尔街和政策制定者热衷追逐的通用人工智能（AGI）概念，李飞飞澄清了外界对她立场的误解。她解释称，自己从未说过 AGI 是坏的，而是从科学与学术严谨性的角度来看，她并不认为“AI”与“AGI”有实质区别。当年图灵、麦卡锡、马文·明斯基（Marvin Minsky）等先驱在构想“会思考的机器”时，怀抱的就是最宏大的想象力，从未限定机器只能进行狭隘、非通用的思考。因此，AI 从诞生之日起其目标就是通用智能。 ```mermaid flowchart TD accTitle: 以人为本的 AI 核心原则 A["以人为本的 AI<br>(Human-Centered AI)"] --> B["由人类创造 (Created by people)"] A --> C["为人类所用 (Used by people)"] A --> D["受人类治理 (Governed by people)"] B & C & D --> E["核心宗旨：负责任地增强人类能力，改善人类生存境况"] ``` 李飞飞进一步阐述了自己在 2018 年发表《纽约时报》专栏文章倡导该理念，并联合创立斯坦福大学以人为本人工智能研究院（Stanford HAI）的根本原因： - **拒绝技术中心主义**：面对就业转型、企业提效、降本创新及下一代教育等宏观议题，社会必须坚守“以人为中心”，而非“以技术为中心”。 - **三大治理基石**：AI 是“由人类创造、为人类所用、受人类治理”的工具。 - **根本原则**：以负责任的方式运用 AI 来增强与扩展人类能力（augment and enhance humanity），进而改善人类的生存境况（better human condition），这一原则应当成为商业实践、学术研究与公共政策制定的核心指南。 --- ### [10:48](https://www.youtube.com/watch?v=yleXdrvFfaE&t=648s) 空间智能与 World Labs：通往无限多元宇宙的愿景 在探讨她联合创办的前沿初创公司 **World Labs** 以及核心技术方向“空间智能”（Spatial Intelligence）时，Matt Egan 提及李飞飞曾描绘过 AI 将创造“无限宇宙”并让人类生活在“多元宇宙”（multiverse）中的惊艳构想。李飞飞通过一个生动的未来场景，向听众具体拆解了这一数字世界与物理世界无缝交织的未来工作与生活形态。 以未来的外科医生为例，李飞飞勾勒出由空间智能构筑的典型多重工作流： 1. **精准医疗的数字手术世界**：外科医生通过穿戴式 AR 设备或数字系统进入病人体内的三维数字孪生世界。在数字副驾驶（digital co-pilot）的高精度辅助下，医生能够以远超肉眼极限的精度与稳定性，清晰辨识并处理最微小的血管与病灶组织。 2. **数字交通世界的自主穿梭**：完成高难度手术后，身心俱疲的医生坐进具备空间环境感知能力的智能车辆中。车辆实时解析道路网与交通流这一复杂的数字世界，医生既可选择人机协同驾驶，也可交付全自动驾驶安全返家。 3. **沉浸式虚拟生活空间**：作为母亲，医生在车内或休息间隙进入下一个由空间智能生成的拟真世界，为即将参加舞会的孩子挑选甚至定制下一套礼服。 李飞飞借此生动地展示了 World Labs 所探索的空间智能如何通过生成和理解具备三维几何与物理规律的世界，将人类带入充满无限创造力与生产力跃迁的全新维度。 ### [12:58](https://www.youtube.com/watch?v=yleXdrvFfaE&t=778s) 空间智能与多重 3D 世界的未来愿景 李飞飞描绘了由 3D 虚拟与物理世界交融带来的丰富应用场景。从个人与家庭生活切入，作为一名母亲，她坦言自己平时根本没有时间逛街购物，因此极度渴望能够步入一个沉浸式的电商世界，在这个数字空间里自由地为自己和孩子们试穿衣物。而在娱乐与社交领域，未来的电影体验将不再是单向被动的屏幕观看，人们可以通过护目镜、交互式屏幕等媒介，身临其境地进入电影设定的外太空奇境；甚至远隔重洋的亲人也能借此相聚——身处欧洲的祖父母可以被数字化“传送”到客厅中，与家人一同唱生日歌庆祝。这些体验展现了物理世界与数字世界的深度融合，构成了可供人类生活、工作与社交的独立宇宙。 赋予这些愿景实现可能的核心技术，正是对三维世界的数字化生成、理解、推理与交互能力，李飞飞将其定义为“空间智能”（Spatial Intelligence）。这也是她所创立的初创公司 World Labs 的核心使命——从底层基础科学出发，构建具备生成能力的通用技术底座，赋能未来多样化的产品形态。李飞飞特别强调，机器人也将是这一生态不可或缺的一环；然而机器人并不等同于科幻电影中体型庞大、外表骇人的具身人形机器，它们本质上是协助人类的机械形态，既可以是自动驾驶汽车，也可以是人形辅助机器人或其他任何工具形态。 ```mermaid flowchart TD accTitle: 空间智能赋能未来应用架构图 subgraph CoreTech["底层核心技术"] SI["空间智能 (Spatial Intelligence)<br/>3D 世界的生成、理解、推理与交互"] end subgraph Applications["多元应用场景"] ECom["沉浸式电商与虚拟试穿"] Ent["沉浸式交互影视娱乐"] Social["跨地域全息社交与家庭团聚"] Robotics["具身智能与多形态辅助机器人"] end CoreTech --> Applications ``` ### [15:41](https://www.youtube.com/watch?v=yleXdrvFfaE&t=941s) 走出象牙塔：人类首次跨入“多重三维宇宙” 当主持人 Matt Egan 追问这种跨越多个宇宙的科幻图景究竟离现实还有多远时，李飞飞笑着调侃他依然热衷于“棒球比赛打到第几局”的比喻。她指出，技术落地的苗头已经显现，这正是她决定走出象牙塔、从大学教授转型为创业者，与昔日学生及同行共同创立 World Labs 的原因。当前这项技术正处于转变为现实与实际产品的临界点，无论是在赋能内容创作者还是在机器人仿真模拟领域，加速度都极为显著。 李飞飞分享了一个宏大的演化视角：地球上的生命演化了近十亿年，无论动物还是人类，此前都仅仅生存于一个唯一的、可通过实体触碰的 3D 物理宇宙中。而得益于数字化革命与空间智能的突破，人类将成为地球历史上第一个能够同时体验与穿梭于“多重 3D 宇宙”的物种。正是这种前所未有的跃升，让未来的可能性变得无限广阔，并能全方位增强人类的能力。 - **地球生命历史尺度**：近 10 亿年来生命始终局限于单一的物理 3D 宇宙。 - **物种级突破**：数字化与空间智能将使人类成为首个能够在多个 3D 宇宙中感知、交互与生活的物种。 - **技术落地进程**：空间智能正处于从学术理论迈向赋能创作者和机器人仿真的实用化拐点。 ### [17:28](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1048s) 拒绝极化修辞：重塑教育与释放人类原生创造力 在展望 AI 对人类社会的积极影响时，李飞飞首先表明自己的立场：她既非“毁灭论者”（doomsayer），也绝非“盲目乐观的空想家”（delusional utopian），而是身处“无聊的中间派”（the boring middle）。她认为，从远古的火、石斧到现代的人工智能，人类制造的所有工具无一例外都是双刃剑。作为一种文明级的使能技术，AI 最令人振奋的应用首先在于重塑教育系统。她呼吁公众应当对“AI+教育”保持高度关注，并直言：希望百年后的历史学家在记录 21 世纪时，能够写下人类借助 AI 彻底重构了教育体系——不再把最宝贵的童年人力资本耗费在死记硬背那些 AI 随手就能通过的标准化考试上，而是开辟一条真正面向未来的成长路径。 另一个令她极度振奋的领域是赋能人类的创造力。李飞飞幽默地分享了自己去非洲度假时的感悟：从纯粹的生物学与动物机能来看，人类其实“相当差劲”——跑得不快、看得不远、游得不深，更不会飞翔，在非洲大草原的神奇野生动物面前甚至显得有些“百无一用”（她随后笑着叮嘱台下：“请千万别在报道标题写‘李飞飞说人类很没用’，我指的是纯生理机能层面”）。人类真正的非凡力量源自好奇心与想象力驱动的创造力。AI 不应去剥夺人类擅长的工作，而应作为超级催化剂，去赋能科学家、艺术家、音乐家和医生，或者接管那些危险、有害、机械重复的任务，从而做大全球生产力与创新的“总蛋糕”。 - **中间派技术哲学**：摒弃乌托邦幻想与末日恐慌，将 AI 定位为需要负责任驾驭的双刃剑文明工具。 - **教育范式转移**：终结以记忆与应试为核心的传统教育，转向培养人类独有的综合素养。 - **创造力与生产力外延**：通过空间智能加速科学探索、艺术表达与制造业/机器人底层技术的升级。 ### [22:06](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1326s) 驳“母性 AI”论：坚守以人为本与尊严底线 主持人提到，被誉为“AI 教父”的 Geoffrey Hinton 前一天在同一舞台上表达了深切担忧，甚至提出为了防止超级 AI 失控毁灭人类，唯一的希望是为其注入类似“母性本能”（Mother AI）以保护人类。对于被外界称为“AI 教母”的李飞飞而言，尽管她对这位相识二十余年的导师与好友怀有崇高的敬意，但她明确且坚定地反驳了这一观点。 李飞飞指出，构想一个比人类更聪明且拥有母性本能的超级 AI 来庇护人类，本身就是一种错误的叙事框架。她严正强调，唯一的正轨是坚持“以人为本的人工智能”（Human-Centered AI）——在个体、社群以及整个社会的每一个层面上，坚决维护人类的尊严与能动性（human dignity and human agency）。任何时刻、任何个人都不应仅仅因为工具的强大而选择放弃自身的尊严与自主权。作为一名母亲、教育者和发明家，她坚信以人为本才是 AI 发展的不可动摇的基石。 > “在任何时刻，没有一个人类应当被要求、或主动选择放弃我们的尊严与能动性，只因为某个工具变得无比强大。” —— 李飞飞 ### [24:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1444s) 现实隐忧：民主治理挑战与劳动力市场的剧烈阵痛 谈及真正让她“夜不能寐”的风险时，李飞飞再次强调了人性的复杂性与阴暗面。纵观历史，人类屡屡利用手中的各种技术工具彼此伤害，而人性的混乱与破坏力同样会投射到 AI 的使用上。她最担忧的并非虚无缥缈的科幻灾难，而是迫在眉睫的现实危机： 1. **信息操纵与民主侵蚀**：AI 被滥用于信息污染与认知操纵，对民主社会的公共生活与制度根基构成严峻冲击。 2. **劳动力市场的剧变与阵痛**：技术迭代必然伴随着产品与服务形态的重构，并对就业结构带来巨大的结构性摩擦。李飞飞以自身所在的硅谷为例指出，当前的软件工程领域正在经历一场地质断层级的剧烈震荡（seismic change），而应对这一变革绝非一句轻飘飘的“那就别当软件工程师”所能解决。 ### [26:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1564s) 人力资本升级与国家级能源消耗挑战 李飞飞指出，在她的公司（World Labs）中，最优秀的软件工程师都已经获得了 AI 的超级赋能（superpowered by AI）。然而，社会与企业仍需面对一系列深层挑战：普通人该如何学习并适应新技术？如何进行技能重塑与提升（upskill）？企业又该如何重新考量人力资本投资（human capital investment）？这些都是当前无法回避的现实问题。 紧接着，李飞飞谈到了 AI 发展面临的全球能源消耗难题。她分享了前一天与 OpenAI 首席财务官（CFO）交流时的震撼数据：对方透露，最新一代前沿模型的训练能耗已经达到了 2 吉瓦（2 gigawatts / 20 亿瓦特）。李飞飞对比强调，欧洲一个中等规模国家的日常运行能耗通常也仅在个位数吉瓦级别，这意味着顶尖大模型的训练能耗已经攀升至“国家级”（nation-state level）的巨大体量。 - **AI 对工程师的倍增赋能**：顶尖软件人才正在 AI 辅助下极大拓宽能力边界，但全社会的技能升级与企业的人力资本投资机制必须跟上脚步。 - **国家级电力消耗**：OpenAI CFO 透露的最新模型训练耗电量已达 2 GW，与欧洲中等国家的整体运行负荷相当。 --- ### [26:43](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1603s) 警惕技术狂热：生产力的提升不等于共同繁荣 在对谈的尾声，李飞飞回顾了过去 30 年的技术演进轨迹。科技的进步毫无疑问推动了全球生产力（global productivity）的持续增长，并且这一趋势未来仍将继续。李飞飞坦言，硅谷的朋友和同行们极其热衷于高谈生产力的飙升，但全社会必须牢记至关重要的一点：“提高生产力并不保证能够带来共同繁荣（Increasing productivity does not guarantee shared prosperity）”。 李飞飞郑重指出，“生产力提升”与“共同繁荣”是两个完全不同的概念，而作为一个健全的社会，两者缺一不可。在汹涌而来的 AI 时代，全社会必须以极其审慎和深思熟虑的态度去理解这一差异，并切实推行能够兼顾两者的政策与实践。随后，CNN 资深记者 Matt Egan 向李飞飞表达了诚挚的感谢，致谢她在这一极为关键的技术变革节点所带来的深刻洞见，圆满结束了本次对话。 ## Notable quotes > [00:04](https://www.youtube.com/watch?v=yleXdrvFfaE&t=4s) 因为在这场变革的终点，我不希望看到任何输家；人类必须全员获胜。 — 李飞飞 > [02:02](https://www.youtube.com/watch?v=yleXdrvFfaE&t=122s) 机器能够思考吗？ — 艾伦·图灵与约翰·麦卡锡（李飞飞引述） > [05:16](https://www.youtube.com/watch?v=yleXdrvFfaE&t=316s) 你知道谁是人类历史上第一位‘提示词工程师’（prompter）吗？是苏格拉底。两千多年前，正是他教会了我们如何通过提问来启发思考。提问的艺术正是人类学习的核心。 — 李飞飞 > [07:55](https://www.youtube.com/watch?v=yleXdrvFfaE&t=475s) 由人类创造、为人类所用、受人类治理 — 李飞飞 > [17:28](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1048s) 请千万别在报道标题写‘李飞飞说人类很没用’，我指的是纯生理机能层面 — 李飞飞 > [22:06](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1326s) 在任何时刻，没有一个人类应当被要求、或主动选择放弃我们的尊严与能动性，只因为某个工具变得无比强大。 — 李飞飞 > [26:43](https://www.youtube.com/watch?v=yleXdrvFfaE&t=1603s) 提高生产力并不保证能够带来共同繁荣（Increasing productivity does not guarantee shared prosperity） — 李飞飞 ## Mentioned 李飞飞 (Fei-Fei Li), Matt Egan, World Labs, CNN, Stanford University (斯坦福大学), Stanford HAI (斯坦福大学以人为本人工智能研究院), Alan Turing (艾伦·图灵), John McCarthy (约翰·麦卡锡), Marvin Minsky (马文·明斯基), 苏格拉底 (Socrates), Geoffrey Hinton, OpenAI, The New York Times (纽约时报) ## Video description Fei-Fei Li, Co-Founder & CEO, World Labs Matt Egan, Senior Reporter, CNN Abstracts Join Fei-Fei Li for a forward-looking conversation exploring the intersection of AI’s understanding of the physical world, spatial reasoning, and the pursuit of human-centered design. This broad discussion will touch on technical, ethical, and societal dimensions shaping AI’s role in the world. Keynotes, Meals, Networking, & More" [Source](https://www.youtube.com/watch?v=yleXdrvFfaE)

Source quotes

[0:04] 李飞飞: 因为在这场变革的终点，我不希望看到任何输家；人类必须全员获胜。 [2:02] 艾伦·图灵与约翰·麦卡锡（李飞飞引述）: 机器能够思考吗？ [5:16] 李飞飞: 你知道谁是人类历史上第一位‘提示词工程师’（prompter）吗？是苏格拉底。两千多年前，正是他教会了我们如何通过提问来启发思考。提问的艺术正是人类学习的核心。 [7:55] 李飞飞: 由人类创造、为人类所用、受人类治理 [17:28] 李飞飞: 请千万别在报道标题写‘李飞飞说人类很没用’，我指的是纯生理机能层面 [22:06] 李飞飞: 在任何时刻，没有一个人类应当被要求、或主动选择放弃我们的尊严与能动性，只因为某个工具变得无比强大。 [26:43] 李飞飞: 提高生产力并不保证能够带来共同繁荣（Increasing productivity does not guarantee shared prosperity）

Source quotes

[0:04] Matt Egan: literally one of the most qualified people on the planet to discuss the AI revolution. [0:04] Fei-Fei Li: AI is not a sports competition because I would not like to see losers at the end of this. Humanity needs to win. [2:00] Alan Turing: Can machines think? [2:00] Fei-Fei Li: this decade's most important challenge and opportunity. [4:54] Fei-Fei Li: You know who is humanity's first prompter? It's Socrates. [7:55] Fei-Fei Li: Kudos to Dan and friends. This is AI4, not AGI4. [17:46] Fei-Fei Li: kind of suck [17:46] Fei-Fei Li: kind of useless [22:06] Fei-Fei Li: At no moment, not a single human should be asked or should choose to let go our dignity and agency just because a tool is powerful. And as a mother, as a educator, and as a inventor, I really believe this is the core of what AI is is it should be human-centered. [24:04] Fei-Fei Li: seismic change [26:04] Fei-Fei Li: increasing productivity does not guarantee shared prosperity

Original transcript

[0:04] Uh I know everyone's not here to see me [0:05] but to see Dr. Lee. So thank you so much [0:08] Dr. Lee for doing this and sharing your [0:11] insights right now. I mean you are [0:12] literally one of the most qualified [0:15] people on the planet to discuss the AI [0:18] revolution. So thank you. [0:19] >> Thank you Matt. Thank you AI4. Uh as a [0:22] professor I dread teaching classes [0:24] starting at A. So kudos to all of you [0:26] being here. [0:29] Well, first I I want to ask you to talk [0:31] a little bit about where we are right [0:34] now in the AI revolution. If this were a [0:38] nine inning baseball game, what inning [0:41] would we be in? [0:43] >> Interesting you asked the question that [0:45] way, Matt. Um, I actually do not think [0:48] AI is a baseball game because [0:53] nothing against baseball. Um, [0:56] AI is not a sports competition because I [0:59] would not like to see losers at the end [1:02] of this. Humanity needs to win and [1:08] and it's much more like a journey we're [1:11] taking on together. If you're a little [1:13] more for excitement, I call this a [1:15] revolution. It's a civilizational [1:17] technology. And the an the analogy I [1:21] draw is think 150 180 years ago [1:25] electricity was on the rise from a [1:27] science to a technology to a product. [1:30] And if you ask people at that time 1870 [1:35] which inning are you in as a baseball [1:38] game for electricity? What answer would [1:40] you have? One nine seven. To this day, [1:45] electricity [1:47] continues to power our civilization. And [1:50] I think that's what AI is. We're at the [1:52] very beginning of the civilizational [1:55] technology that would power the the the [1:57] the future of humanity. [2:00] >> Okay. So, the very beginning. Okay. [2:02] Well, that's great. Now can you when you [2:05] when you take a step back and you really [2:06] think about the evolution of AI, what is [2:10] the most important but most [2:13] underappreciated aspect of artificial [2:16] intelligence? [2:19] >> Well, there are many right from the [2:21] evolution of AI. I think it's very [2:24] important to um to remember that this [2:28] began as a science a group of daring [2:32] individuals from Alan Turing to the [2:34] founding fathers of uh uh artificial [2:37] intelligence including my own colleague [2:39] like John McCarthy just dared humanity [2:43] to ask the question can machines think [2:45] and why do we ask that question because [2:48] we were curious and that's what humans [2:50] are humanity has always chased [2:53] innovation and discovery out of [2:55] curiosity. [2:57] But along the way, and I belong to that [2:59] generation, I feel very lucky that this [3:02] curiosity grew bigger. We not only [3:05] discovered some of the most important [3:07] scientific breakthroughs and innovations [3:10] along the way, including deep learning, [3:13] datadriven, um, machine learning and and [3:17] now Gen AI. We also discovered that we [3:20] are now changing the course of history [3:23] because uh it's so powerful that as a [3:27] technology it can literally superpower [3:30] every aspect of human work, human life. [3:33] And then looking to the future, we're [3:36] not lacking AI discussion today. today's [3:40] uh society, the airwaves and the [3:42] podcasts, the the the the the tweets and [3:46] the uh uh you know the videos and all [3:49] that is filled with AI discussion from [3:53] literally Washington DC to boardrooms. [3:56] But there is one topic of AI in today's [4:00] AI revolution that I think it's deeply [4:04] underdised under resources resourced and [4:07] underappreciated and that's AI and [4:10] education [4:11] >> and uh this goes from personal education [4:14] to K12 education to uh uh continuous [4:18] lifelong learning and skilling from both [4:21] company point of view as well as just uh [4:24] formal education point of you because [4:26] this is a technology that should not and [4:30] will not if we have the will replace [4:33] humanity or uh human agency but this is [4:36] a technology that empowers human agency [4:38] and using AI to transform education to [4:43] empower learning for students and [4:45] teachers is to me this decade's most [4:50] important um challenge and opportunity. [4:54] Wow. And are we [4:59] when it when it comes to AI and [5:02] education, [5:04] what should be is the is the current [5:06] approach the right one? I mean, is there [5:08] anything that you would say that we [5:09] should be doing differently? And how [5:11] does that relate to the importance of [5:13] prompting and knowing how to prompt? [5:16] >> Is there an approach right now? Yeah, I [5:19] >> I'm I'm asking you because literally [5:21] yesterday I was in a room with uh uh [5:24] senior staffers from both houses as well [5:27] as both parties at Stanford and I don't [5:30] think as a country I don't think as an [5:33] education system and I don't think even [5:35] as individuals whether you're parents or [5:38] teachers or many students have a [5:41] actually a approach of AI powered [5:44] learning because it's so early. I'm not [5:47] blaming anybody. I just think it's so [5:49] early and and because of all the [5:53] sometimes too much noise that has this [5:56] AI doomsday, we almost created a [6:01] instinctive negative um reaction, [6:04] knee-jerk reaction when it comes to AID [6:07] education. The number one word when [6:10] people think about AI and education is [6:12] the word cheating. And that's just [6:15] absolutely the wrong word. The number [6:17] one word we should uh think is [6:20] empowering is you you you talk about [6:22] prompting. [6:24] How to prompt to learn is literally [6:29] you know who is the first humanity's [6:31] first prompter? [6:34] It's Socrates. [6:37] He's the person more than 2,000 years [6:40] ago taught us how to prompt. Of course, [6:43] he didn't use that word prompt. I don't [6:45] even know what's a Greek word for [6:47] prompt, but [6:49] but it's asking the right question. And [6:51] that's the heart of human learning. And [6:54] we now have created a powerful tool that [6:58] can supercharge that process of [7:02] learning. And yet we tell our students, [7:05] don't cheat with AI. What we should tell [7:08] our students is this is the baseline AI [7:11] will give you. Whether it's a science [7:13] question, a math question or English [7:15] essay. Now you are a human being with [7:19] agency, an intelligence that no AI, no [7:22] supercomputer can ever achieve. Given [7:26] this baseline, given this tool, where [7:29] can you go? [7:31] That is the the question of uh prompting [7:33] or education or learning. And I would [7:36] encourage every parent, every teacher, [7:39] every student to think in that way and [7:42] think how ha how did I learn better or [7:45] what have I learned better using AI as a [7:48] tool. [7:49] >> It's fascinating. [7:55] Now I know in the past um you've pushed [7:57] back on the term AGI. Can you explain [8:01] why instead of a race to AGI, which is [8:05] the focus right now in Silicon Valley [8:07] and Wall Street and elsewhere in [8:09] Washington, why do you think we should [8:11] be focused instead on human centered AI? [8:14] And what what is that? Can you explain [8:16] to the audience what you mean by human- [8:18] centered AI? [8:19] >> Yeah, first of all, I Okay, to correct [8:22] record, I never said AGI is bad. I just [8:25] said I don't know the difference between [8:28] the word AGI and AI. And kudos to Dan [8:31] and friends. This is AI4, not AGI4. [8:35] Because when Alan Turing and John [8:37] McCarthy and Marvin Minsky, the founding [8:40] fathers and and thinkers of AI, when [8:42] they dared humanity with this [8:44] possibilities of machines that think and [8:47] do, in my opinion, they didn't say that [8:50] machines that think narrowly and [8:53] non-generally. [8:55] They literally just had the biggest [8:57] imagination. So for me as a scientist, [9:00] AI was born with this audacious goal of [9:06] understanding and creating machines that [9:08] think. So AI and AGI are synonyms to me. [9:11] So this is why I I tend to answer as a [9:14] scientist and a scholar. I just don't [9:16] know if the these are two differentiated [9:18] terms. But beside that point, why did I [9:22] found um the world's and Stanford's [9:25] first human- center AI institute? Why [9:28] did I write a New York Times oped back [9:31] in 2018 actually calling this framework [9:35] of human center uh AI uh concept is [9:40] because this is a civilizational [9:42] technology. Uh many of you sitting here [9:44] here might be thinking about the future [9:46] of jobs. Many of you sitting here might [9:49] be thinking about how I can use AI to [9:51] supercharge my business to make my [9:54] product better to cut cost to to make [9:58] work more efficient. Your parents, [10:00] you're thinking about your kids [10:01] learning. We just talked about this. At [10:04] the end of the day, if we believe in the [10:06] benevolence of human society and where [10:08] we want to push for the future, we need [10:12] to put humans in the center. We cannot [10:14] put a technology in the center. the the [10:17] the the [10:18] AI is created by people. It is used by [10:22] people and it should be governed by [10:24] people. And that's why we should put [10:26] humans in the center of this. And the [10:28] principle of human- centered AI for me [10:31] very much so is responsibly using AI to [10:35] augment and enhance humanity to better [10:38] human condition. And I think that's um [10:41] that can guide business that can guide [10:43] research that can guide uh policy [10:46] making. [10:48] Um, let's talk a little bit about World [10:50] Labs, the startup that you co-founded. [10:53] Um, and also about spatial intelligence. [10:56] Um, I've heard you say that in the [10:59] future [11:00] AI can create infinite universes and [11:04] that we could live in the multiverse. [11:07] Uh, when I heard that, it sort of blew [11:08] my mind. Can you explain what you mean [11:11] here about this idea of infinite [11:13] universes and how does that relate to [11:15] the work you're doing right now? [11:17] Yeah, this is a m thanks for asking this [11:19] question. Um, this is a future I can [11:22] imagine and um let's just say you're a [11:28] doctor of the future. Okay, you're a [11:31] surgeon. I can imagine your life at work [11:34] in the following way. If you have to do [11:36] surgery for a patient and to save their [11:39] lives, you through a machine or wear a [11:43] goggle, you transport yourself at your [11:46] incredible skill set into a digital [11:49] world that connects. I'm not going to [11:52] get gory, but if you have to do surgery, [11:54] you have to do certain things with your [11:56] patient's body. But using a digital uh [12:01] co-pilot or help or or or AR devices, [12:05] you can get into that world in much more [12:08] precision and accuracy than you can ever [12:12] do with your own eyes, right? Whether [12:14] you're looking for tiniest blood vessels [12:16] or looking for things to to fix, that's [12:21] one world. You finish the surgery [12:24] successfully. You you you you drive [12:27] home. We're not surprised. You might be [12:30] too tired to drive yourself. Then you [12:32] stay in the car that you know is works [12:38] operates in the digital world of roads [12:40] and traffics. And maybe you're [12:43] co-driving or maybe you're letting it to [12:45] drive. That's a different universe. [12:48] You're a mom doctor, so you might even [12:50] need to shop for your kids next prom [12:53] dress. You go into a world that um that [12:58] um is e-commerce world. It like as a [13:02] mom, I really love this. I have no time [13:04] for shopping. So I would love to go into [13:06] a world that uh I can try on different [13:09] clothes. Your kids can dry on different [13:11] clothes, shop for whatever you want and [13:13] that's a commercial world. Now you want [13:16] to have a weekend with your kids and [13:18] then you go to a movie experience. The [13:20] future of movie is not just passive [13:22] watching. You can weather through [13:24] goggles or screens or whatever uh [13:27] interactive media and you can actually [13:30] experience a a you know a totally [13:33] different outer space world based on [13:35] whatever that story you're watching. uh [13:39] or you have a loved one living in Europe [13:41] and uh you guys want to share a birthday [13:44] party in you know transatlantic [13:47] and then you have a platform where you [13:50] you you transport the grandparents from [13:54] from Europe digitally to your living [13:57] room sing the birthday songs together. [13:59] Why am I experiencing all this? Every [14:02] world I just experienced is a blend of [14:05] physical and digital world. it it has [14:08] its own universe. You can interact and [14:11] and work and live in it in ways that can [14:14] benefit your life. You can bring [14:16] different people in. It can be social. [14:18] It can be solo. And if this sounds like [14:22] sci-fi, the question is what technology [14:26] empowers that? And that technology to me [14:29] is the digital creation of 3D worlds and [14:34] uh as well as so many things that comes [14:37] with it. The ability to understand to [14:39] reason to interact and that's what I [14:42] call spatial intelligence and um that's [14:45] what world lab is doing. World Lab is [14:48] creating that generative capability, [14:51] you know, from a basic science uh ground [14:54] up of technology that can empower and [14:57] envision you usages and products like [15:01] this. And also just to add to all what I [15:05] described that robots will be part of [15:07] this. Robots is not necessarily what you [15:10] see in these um movies. They have to [15:13] come in humanoid forms and they have to [15:16] look really big and scary. You robots [15:18] just is another form of machines that [15:21] can help people whether it's driving in [15:24] the shape of a car or if it's helping [15:27] people in the shape of humanoid or if [15:29] it's anything else. So the fundamental [15:32] technology I envision here is spatial [15:35] intelligence and that's why I see there [15:37] are many universes we can create and [15:40] live and work in. [15:41] >> Wow. Well, that it does sound like [15:43] science fiction. How far away are we [15:46] from this vision that you have of of [15:49] multiple universes? [15:50] >> You just love your baseball game [15:52] analogy. [15:55] Um, [15:57] we're we're beginning to see that. Um [16:00] I'm excited to um take a step outside of [16:03] the ivory tower from being a professor [16:06] to uh be becoming an entrepreneur with [16:09] my former students and colleagues [16:12] because I think this technology is at [16:14] the cusp of becoming reality and [16:17] becoming uh uh products. uh whether [16:20] we're talking about just superpowering [16:23] creators uh robotic simulations all this [16:27] is uh we're seeing that acceleration and [16:31] everything I do I firmly believe um is [16:35] for the purpose of augmenting humans and [16:38] I think this technology is um is really [16:41] part of that and this is one more thing [16:43] that's really cool I want to share with [16:45] you because most people probably don't [16:47] don't appreciate that is that for [16:52] as long as animals or living things on [16:55] earth existed which is close to a [16:57] billion years we existed in one universe [17:01] it's the 3D universe the 3D world we can [17:04] tangibly touch with digital revolution [17:08] hum humanity will be the first species [17:11] that can experience multiple universe in [17:15] 3D and that's why it makes us so special [17:18] it makes the future So incredible to [17:20] imagine because the the the possibility [17:23] is literally unbounded because of this. [17:27] >> Wow, that's fascinating. Now, as you [17:29] look into your crystal ball and you [17:31] think about the future, what are you [17:32] most excited about when it comes to the [17:35] positive impacts that AI can have on [17:39] humanity and society, whether it's [17:42] medicine, science, or or elsewhere? [17:46] Yeah, I I am excited by many things, but [17:49] that I also want to make sure that um [17:53] that I'm not delivering a wrong message. [17:56] I'm not a delusional utopian of AI. [18:00] There's a lot of extreme rhetoric of AI [18:03] from doomsaying to delusional utopian. I [18:07] am actually the boring middle in the [18:10] sense that [18:12] we are going to talk about the positive [18:13] of this technology but I think no [18:16] technology is own is perfect. Humanity [18:19] is very good at creating double-edged [18:21] sword. Every single piece of tool from [18:23] fire to stone axe all the way to AI we [18:27] have made our tool double-edged sword. [18:30] So um we we we should definitely make [18:33] sure that uh we acknowledge that but it [18:37] is a tool. It is a civilizational [18:40] technology that can superpower so many [18:42] tech uh sectors. We already talk about [18:44] education. I I know I'm a little [18:47] obsessed with this topic partially [18:48] because I think the public should be way [18:50] more obsessed about AI and education [18:53] than what it is now. is that 50 years [18:57] from now, 100 years from now when [18:59] historians writes the history of 21st [19:02] century, I really hope a salient part of [19:06] this history is that 21st century [19:08] humanity reshaped its education system [19:12] because of AI. We should not be using [19:15] the most precious human capital which [19:18] are our children for so many years of [19:21] their early lives to just pass memory [19:25] based tests, standardized tests that AI [19:28] can pass today [19:35] and we should really forge a completely [19:38] different path for our future [19:40] generations. So that is one sector I'm [19:42] extremely excited about. I'm absolutely [19:45] extremely excited about creativity. And [19:48] when I say creativity, it's not just [19:50] artists and musicians and writers. [19:53] Humans are fundamentally creative [19:55] animals. We actually kind of suck as [19:58] animals. We don't run very fast. We [20:00] don't see very far. We don't swim very [20:03] very much. We don't fly. We we just [20:05] don't do much physically compared to [20:07] many other animals. Like I was in Africa [20:10] for vacation. Oh my god, the animal [20:12] world is just magical and we're kind of [20:15] useless. But [20:18] sorry, please don't write Fay says [20:20] humans are useless. That's not what I [20:22] mean. I I'm just saying as an animal. [20:25] But we're creative beings and our power [20:29] is trying to create for good. create for [20:33] out of imagination, curiosity and uh AI [20:37] can superpower artists, AI can [20:39] superpower scientists, AI can superpower [20:43] um you know um musicians uh doctors. So, [20:47] alien creative business from scientific [20:50] discovery to artistic expression to [20:52] storytelling to business creation AI [20:56] should be right there and I'm excited [20:58] about I'll just give one last example. I [21:01] think um AI can I mean there's so many [21:04] possibilities but AI also can become a [21:08] catalyst for um manufacturing [21:13] um and uh robotics you know the spatial [21:16] intelligence that I'm creating to me is [21:19] the foundation of robotics and uh it's [21:24] time that we we create machines to help [21:27] us live better and uh work better. They [21:30] are they we should not let machines take [21:33] over jobs that humans do best but we [21:37] should use machines to do jobs that harm [21:40] human or does not um index of human [21:44] creativity or is repetitive or or many [21:49] other forms. So I think we should allow [21:51] oursel that opportunity to increase the [21:57] global pie. So these are the things that [21:59] truly uh excite us. I I could go on and [22:03] on, but I I I'll stop here. [22:06] >> Well, so you're you're um you're often [22:08] referred to as the godmother of AI, and [22:11] yesterday the godfather of AI, Jeffrey [22:14] Hidden, was on this stage talking about [22:18] some of his concerns. Um he said that [22:21] our one chance to ensure that AI doesn't [22:24] gain control of humans is to have a [22:26] mother AI so that when it's smarter than [22:29] us, it won't wipe us out or try to [22:32] control us. What do you make of this [22:33] idea that we need to instill AI systems [22:36] with a maternal instinct? [22:40] >> I love Jeff. He's he's been my friend [22:42] and mentor um personally for in this [22:45] field for um for more than two three [22:48] decades. It's honor to be even called [22:51] godmother of AI when he's the godfather [22:53] of AI. But u I disagree with Jeff that [22:57] we should have a a a mother AI that um [23:02] that's smarter than us and that has a [23:05] whatever instinct because I think that's [23:08] the wrong way to frame it. I would [23:10] respective fully push back and say we [23:13] need human center AI that gives gives [23:17] back or preserves human dignity and [23:20] human agency at every individual, [23:23] community and societal level. It is our [23:26] responsibility at every single level to [23:30] create and and use this and govern this [23:33] technology in the most responsible way. [23:36] And at no moment, not a single human [23:40] should be asked or should choose to let [23:43] go our dignity and agency just because a [23:46] tool is powerful. And as a mother, as a [23:49] educator, and as a inventor, I really [23:53] believe this is the core of what AI is [23:56] is it should be human- centered. [24:04] Now, I I know in the past you've said [24:05] that we need to focus on science, not [24:07] science fiction, but there are concerns [24:11] about losing control of AI. What keeps [24:14] you awake at night when it comes to [24:17] super intelligent AI systems and the [24:20] risks posed to humans? [24:23] >> I mean, truthfully, a lot of things. [24:25] This is where I I become the the middle, [24:29] right? I do talk about the positive [24:31] things but I do think that humanity also [24:34] has a dark underbelly. We have in [24:37] throughout the course of history have [24:38] proven that we're very capable of [24:41] hurting each other, hurting our society, [24:44] hurting our hum uh community in using [24:47] whichever tool again from fire to stone [24:50] axe all the way to the most powerful [24:52] technology. And I do foresee that that [24:56] part of our DNA, part of the messiness [24:58] of being human will actually transpire [25:02] using AI technology. Um so things that [25:06] um makes me lose sleep. Um I do think [25:09] that AI can be a powerful tool that can [25:13] be manipulated whether it's on the [25:16] information side as well as in this um [25:21] in the public life of a democratic [25:24] society. I think AI can be uh used in [25:28] the wrong way. um preserving democracy [25:32] and a strong democracy for our country [25:34] is very important. I do think that there [25:37] is going to be incredible growing pain [25:41] in job and labor landscape shift shifts [25:46] and this has happened in every time [25:48] technology transforms uh products and [25:51] and services and we're going to see that [25:54] software engine I come from Silicon [25:56] Valley software engineering is going [25:58] through a seismic change. It's not as [26:01] simple as don't be a software engineer. [26:04] That's not true. In my company, the best [26:07] software engineers are superpowered by [26:09] AI. But how do you learn? How do you [26:11] upskill? How does companies think about [26:14] human capital investment is a real it's [26:18] a real thing. of course global energy [26:21] you know yesterday I was uh talking with [26:24] the CFO of open AI and and she was [26:27] sharing that uh the latest models are [26:30] trained on two gigawatts of uh energy [26:33] and a country a middlesized country in [26:36] Europe runs on singledigit gigawatts so [26:39] this is nation state level energy usage [26:43] and last but not the least I want to say [26:46] say is especially we have seen this in [26:48] the last 30 years is that um we've seen [26:52] global productivity increase because of [26:56] technology and we'll continue to see [26:59] global productivity increase and I know [27:03] my friends and colleagues in Silicon [27:05] Valley love to talk about this [27:08] increasing productivity. I think that [27:11] what we need to keep in mind is [27:14] increasing productivity does not [27:17] guarantee shared prosperity. [27:20] These are two different concepts and as [27:23] a society we need both and this is [27:26] something that we should really [27:28] thoughtfully understand and implement in [27:32] the age of AI. [27:36] >> Well, thank you so much Dr. for uh [27:38] talking to us and sharing all of your [27:40] insights in this incredibly important [27:42] moment. Thank you. [27:43] >> Thank you, Matt. [27:43] >> Thank you so much. [27:45] >> Thank you.