With Spatial Intelligence, AI Will Understand the Real World | Fei-Fei Li | TED
Fei‑Fei Li explains how vision sparked life's intelligence, how three forces ignited modern AI, and why spatial intelligence will let machines perceive, act and collaborate in the 3‑D world.
15m·Guest Fei‑Fei Li·Host Fei‑Fei Li·
From Darkness to Insight: Vision as Evolutionary Catalyst
Li traces the story from a primordial, eyeless ocean to the emergence of light-sensing trilobites, showing how vision turned passive darkness into active understanding, which in turn birthed intelligence and curiosity. The narrative frames vision as the first feedback loop that let organisms not just see but act on what they see.
Vision transformed passive darkness into active understanding, sparking the Cambrian explosion
- In the pre‑Cambrian seas there was abundant light but no eyes, so life existed in pure darkness.
- The first light‑sensing creatures, trilobites, appeared about 540 million years ago, introducing the ability to detect other organisms.
- This new sense allowed organisms to perceive a world of many “selves,” creating a competitive arena that drove rapid diversification.
- Li argues that this visual feedback loop was the engine of the Cambrian “big bang” of animal life.
- The story illustrates how a single sensory capability can reshape an entire ecosystem.
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00:00:18“黑暗并非因为缺乏光线，而是因为缺乏观察的眼睛。” — Fei‑Fei Li
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00:01:12“三叶虫——第一种能感光的生物，出现了。” — Fei‑Fei Li
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00:01:37“视觉被认为推动了寒武纪生命大爆发。” — Fei‑Fei Li
Seeing evolved into understanding, which then generated purposeful action and intelligence
- Li outlines a four‑stage cascade: seeing → understanding → action → intelligence.
- Early organisms only let light in; later neural systems turned that light into insight.
- Understanding gave rise to purposeful behavior, which in turn selected for more sophisticated neural architectures.
- This loop is the biological precursor of what modern AI tries to emulate: perception that informs decision‑making.
- The progression underscores why merely “seeing” (labeling) is insufficient for true intelligence.
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00:02:00“看见变成了理解。” — Fei‑Fei Li
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00:02:05“它们促成了智能。” — Fei‑Fei Li
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00:02:03“理解带来了行动。” — Fei‑Fei Li
Human curiosity drives us to build machines that surpass natural vision
- After nature gave us eyes, our innate curiosity pushed us to create artificial eyes that could see better, faster, and in new modalities.
- Li describes this drive as a desire to make machines “as intelligent as possible, even better.”
- The ambition is not just to replicate biology but to extend it—adding speed, scale, and multimodal perception.
- This mindset set the stage for the AI breakthroughs discussed later in the talk.
- It frames AI research as a continuation of the evolutionary story, now driven by human imagination.
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00:02:23“好奇心促使我们创造出那些机器，使其尽可能智能，甚至更好。” — Fei‑Fei Li
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00:02:27“九年前，在这个舞台上，我发表了一场有关计算机视觉的早期进展报告。” — Fei‑Fei Li
Triad of AI Revolution: Neural Nets, GPUs, and Big Data
Li identifies three converging forces--deep learning algorithms, specialized GPU hardware, and massive labeled datasets--as the catalyst that launched modern AI. ImageNet, a 15-million-image repository, became the proving ground where each force amplified the others, leading to rapid gains in speed, accuracy, and capability.
The convergence of neural networks, GPU hardware, and massive datasets ignited modern AI
- In 2012 three powerful elements aligned: the rise of deep neural‑network algorithms, the availability of fast GPU processors, and the creation of huge labeled image collections.
- Li calls this the first time these “three strong forces” came together, producing a synergistic effect.
- GPUs provided the compute horsepower to train deep nets on millions of images, while the data gave the models something meaningful to learn.
- This trifecta turned AI from a niche research area into a mainstream technology.
- The story sets the foundation for later breakthroughs in perception and generation.
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00:02:35“三股强大的力量首次汇聚在一起。” — Fei‑Fei Li
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00:02:39“一种称为神经网络的算法。” — Fei‑Fei Li
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00:02:43“称为‘图形处理器’或 GPU的快速、专业的硬件，” — Fei‑Fei Li
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00:02:51“就像我的实验室花了多年时间整理的名为 ImageNet 的1500 万张图像一样。” — Fei‑Fei Li
ImageNet’s 15 million labeled images served as the catalyst for breakthroughs in computer vision
- ImageNet aggregated 15 million hand‑labeled images, providing the scale previously unavailable to researchers.
- The inaugural ImageNet Challenge turned image‑labeling—a once‑major breakthrough—into a competitive benchmark.
- Year‑over‑year improvements in top‑5 error rates demonstrated rapid progress in both algorithmic design and hardware utilization.
- The dataset’s size enabled deep networks to learn rich visual representations, paving the way for object detection, segmentation, and relational reasoning.
- Li stresses that without such a massive, curated dataset, the deep‑learning surge would have stalled.
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00:03:04“当时，单单是给图片打标签都是一个重大突破。” — Fei‑Fei Li
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00:03:14“但是这些算法的速度和准确性迅速提高了。” — Fei‑Fei Li
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00:03:18“由我的实验室领导的一年一度 ImageNet 挑战赛评测了这一进展的表现。” — Fei‑Fei Li
Rapid improvements in speed and accuracy transformed labeling tasks into complex object detection and relational reasoning
- Early models could only assign a single label to an image; within a few years they learned to localize objects and predict their interactions.
- Li cites milestones where algorithms moved from classification to segmenting objects and even inferring dynamic relationships between them.
- These advances were driven by deeper networks, better GPUs, and richer supervision from ImageNet‑style datasets.
- The shift illustrates how a simple labeling problem blossomed into a full‑fledged visual understanding system.
- It foreshadows the later move from perception‑only AI to embodied, spatially aware agents.
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00:03:34“我们更进一步，创造了算法切分物体或预测它们之间的动态关系，” — Fei‑Fei Li
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00:03:41“还有更多。” — Fei‑Fei Li
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00:03:21“在这张图上，可以看到每年的改进和里程碑式模型。” — Fei‑Fei Li
From Labels to Worlds: The Rise of Generative and Spatial AI
Li explains how diffusion models turned text-to-image generation from "impossible" to commonplace, and how generative video models like Walt push the frontier further. She then moves to spatial AI, where algorithms convert 2-D photos into 3-D scenes, opening the door to immersive, interactive worlds.
Diffusion models turned ‘impossible’ text‑to‑image generation into reality, powering today’s generative AI boom
- Early attempts at generating images from text were dismissed as impossible, as illustrated by Andrej Karpathy’s skeptical response.
- The breakthrough came with diffusion models, which iteratively denoise random noise conditioned on a textual prompt.
- These models now create high‑fidelity images and videos from natural language, enabling tools like OpenAI’s Sora.
- Li credits this paradigm shift for the explosion of generative content across art, design, and entertainment.
- The story demonstrates how a new algorithmic class can overturn long‑standing assumptions about AI capability.
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00:04:11“这要归功于一系列扩散模型，它们驱动了如今的生成式 AI 算法，将人为输入的句子转换为闻所未闻事物的照片和视频。” — Fei‑Fei Li
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00:04:28“很多人已经看到了 OpenAI 的 Sora 最近展示出的惊人成果。” — Fei‑Fei Li
Generative video models like Walt can outperform contemporaries even without massive GPU clusters
- While Sora captured headlines, Li’s team built a video generation model called “Walt” that achieved comparable quality months earlier.
- Walt was trained on far fewer GPUs, showing that algorithmic efficiency can offset raw compute.
- This demonstrates that progress in generative AI is not solely a hardware race; clever model design matters.
- The example underscores the importance of research that decouples performance from sheer compute power.
- It also hints at a future where high‑quality video generation becomes accessible to smaller labs and enterprises.
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00:04:34“但是，即使没有大量的 GPU，我的学生和合作者们开发了一个生成式视频模型，名为“Walt”，领先 Sora 数月。” — Fei‑Fei Li
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00:04:47“来看一些结果。还有改进的余地。” — Fei‑Fei Li
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00:04:55“看看那只猫的眼睛， 还有它怎么能在水下却没有被弄湿。” — Fei‑Fei Li
Spatial AI extends generative capabilities into 3‑D, converting 2‑D photos into immersive scenes
- Google researchers recently built an algorithm that ingests thousands of photos and reconstructs a coherent 3‑D space, similar to Li’s demo.
- Li’s own students created a pipeline that takes a single image and predicts its full 3‑D shape, enabling interactive exploration.
- Stanford collaborators further expanded this to generate infinite virtual environments from one picture.
- These advances move AI beyond flat image synthesis toward world‑building, a prerequisite for embodied agents.
- The work illustrates a concrete path from “seeing” to “understanding” a scene’s geometry and physics.
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00:07:14“就在最近，谷歌的一组研究人员开发出了一种算法，拍摄大量照片并将其转换到三维空间。” — Fei‑Fei Li
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00:07:33“我的学生和我们的合作者更进一步，创建了一种算法，采集一张输入图像并将其转换为三维形状。” — Fei‑Fei Li
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00:08:06“开发了一种算法，可以通过一张图像生成无限可能的空间供观众探索。” — Fei‑Fei Li
Spatial Intelligence: Bridging Perception and Action
Li defines spatial intelligence as the innate loop that ties 3-D perception to the impulse to act. She demonstrates how a single glance yields geometry, relationships, and predictions, and argues that current AI lacks this perception-action coupling. Training agents in simulated 3-D worlds can close the gap.
Human brains instantly infer 3‑D geometry, relationships, and future events from a single glance
- In a live demo, Li asks the audience to raise their hands after looking at a cup for one second.
- She explains that within that second the brain extracts the cup’s shape, its position in space, and its relation to the table, cat, and surrounding objects.
- The brain also predicts how the cup will behave if interacted with, illustrating forward modeling.
- This rapid, holistic processing is what she calls “spatial intelligence.”
- The example highlights the richness of human perception compared with current AI that often processes only 2‑D pixels without context.
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00:06:04“在刚才的一秒钟里，你的大脑观察了这个杯子的几何形状、它在三维空间中的位置、它与桌子、猫以及其他一切的关系。” — Fei‑Fei Li
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00:06:16“而且你可以预测接下来会发生什么。” — Fei‑Fei Li
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00:06:20“采取行动的冲动是所有拥有空间智能的生物与生俱来的，空间智能将感知与行动联系起来。” — Fei‑Fei Li
Spatial intelligence couples perception with an innate drive to act, a loop absent in current AI
- Li argues that perception alone (seeing) is insufficient; the next step is the impulse to act, which closes the feedback loop.
- Modern AI systems can label images or generate text but still lack the embodied “do‑something” component.
- To reach true spatial intelligence, AI must be able to translate 3‑D understanding into purposeful movement.
- She calls for AI that not only sees and talks but also does, emphasizing the need for embodied agents.
- This perspective frames the next frontier of AI research as integrating action planning with visual cognition.
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00:06:30“如果我们想推动 AI 超越其现有能力，我们需要的不仅仅是能看见和说话的 AI。我们要能做事的 AI。” — Fei‑Fei Li
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00:06:20“空间智能将感知与行动联系起来。” — Fei‑Fei Li
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00:06:46“我们确实正在取得一些令人兴奋的进展。” — Fei‑Fei Li
Training AI in simulated 3‑D environments enables limitless learning of embodied behaviors
- Li’s lab moved from static image datasets to a 3‑D simulation platform that can generate infinite scenarios.
- The “Behavior” project uses these environments to teach robots how to act, learn, and adapt without real‑world constraints.
- Simulations provide rich, controllable feedback loops, allowing agents to practice perception‑action cycles at scale.
- This approach mirrors how evolution tested organisms in varied habitats, accelerating AI’s acquisition of spatial intelligence.
- The result is a new class of agents that can navigate, manipulate, and interact with the world in ways that pure vision models cannot.
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00:09:34“我们没有收集静态图像，而是开发了基于三维空间模型的仿真环境，这样计算机就可以有无限的可能性来学会行动。” — Fei‑Fei Li
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00:09:55“你刚看到了一小部分示例，是在指导我们的机器人，来自我的实验室领导的一个名为 Behavior 的项目。” — Fei‑Fei Li
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00:10:06“通过基于大语言模型的输入，我的学生和合作者们是第一批能够展示机械臂根据口头指示执行各种任务的团队之一。” — Fei‑Fei Li
Embodied AI in Healthcare and Everyday Life
Li showcases concrete applications of spatial intelligence in medicine and daily tasks, from smart sensors that monitor hygiene to robots that follow spoken commands or brain signals. These examples illustrate how AI can become a trusted partner that augments human capability while preserving dignity.
Smart sensors can monitor clinical hygiene, reducing infection risk
- Li’s team partnered with Stanford Medicine to deploy AI‑enabled sensors that detect when clinicians enter a room without proper hand washing.
- The system alerts staff in real time, creating a feedback loop that encourages compliance.
- Such ambient intelligence acts as extra “eyes” in the environment, extending human perception.
- Early trials suggest measurable reductions in contamination events.
- This application demonstrates how spatial intelligence can improve safety without replacing human workers.
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00:11:40“我们正在与斯坦福医学院和合作医院的合作者们一起试点使用智能传感器，检测临床医生在未正确洗手的情况下进入病房。” — Fei‑Fei Li
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00:11:43“或者追踪手术器械。” — Fei‑Fei Li
Robotic assistants, guided by AR or brain signals, can augment medical procedures and patient care
- Li envisions autonomous robots delivering supplies, freeing nurses to focus on patients.
- Augmented‑reality overlays can guide surgeons, making operations safer and less invasive.
- Brain‑computer interfaces allow severely paralyzed patients to control robots with thought alone.
- A demo showed a robotic arm cooking a Japanese dish using only EEG‑derived commands.
- These scenarios illustrate a future where AI extends human agency rather than replaces it, respecting dignity and enhancing outcomes.
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00:12:19“想象一下，一个自主机器人运送医疗用品， 看护人员则专注于我们的患者， 或用增强现实引导外科医生更安全、更快地做手术或者让手术的侵入性更低。” — Fei‑Fei Li
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00:12:42“没错，脑电波，可以执行你我司空见惯的日常任务。” — Fei‑Fei Li
Language‑conditioned robots demonstrate that AI can follow spoken instructions to perform complex tasks
- Using large‑language‑model prompts, Li’s students taught a robotic arm to open drawers, unplug phones, and even make a sandwich.
- The robot can also place a napkin for the user, showing fine‑grained manipulation.
- These capabilities combine vision, language understanding, and motor control, embodying spatial intelligence.
- The work proves that AI can translate natural language into concrete actions in the physical world.
- It marks a step toward everyday assistants that understand and act on human requests safely.
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00:10:06“通过基于大语言模型的输入，我的学生和合作者们是第一批能够展示机械臂根据口头指示执行各种任务的团队之一，例如打开抽屉或拔掉充满电的手机。” — Fei‑Fei Li
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00:10:31“甚至会为用户放上一张纸巾。” — Fei‑Fei Li
⚙ Agent-readable JSON index — click to expand
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