Evolution of Sight and Intelligence
📌 540 million years ago, the world was dark due to a lack of sight, despite the presence of light filtered by the ocean and hydrothermal vents.
👁️ The emergence of trilobites, the first organisms with the ability to sense light, is thought to have triggered the Cambrian explosion, leading to a huge variety of animal species.
🧠 The evolution moved from passive sight (letting light in) to active insight, where seeing evolved into understanding, leading to action and ultimately intelligence.

The Rise of Computer Vision and AI
🤖 Modern AI was ushered in by the convergence of three forces: neural networks, GPUs (graphic processing units), and big data, exemplified by the ImageNet dataset of 15 million images.
📈 Performance in image recognition rapidly improved; the annual ImageNet challenge showed rapid annual improvement in milestone models.
🖼️ Progress moved beyond simple labeling to object segmentation, predicting dynamic relationships, and generating natural language descriptions of photos, as seen in early work with Andrej Karpathy.
🌌 Generative AI, powered by diffusion models, made the "impossible" reverse task—turning human-prompted sentences into new photos/videos (like Sora and Walt)—a reality.

The Necessity of Spatial Intelligence
💡 The speaker argues that simply seeing and talking is insufficient; AI must advance to spatial intelligence, which links perception with action in 3D space and time.
🔬 Spatial intelligence involves translating 2D visual data into 3D information, with recent algorithmic breakthroughs allowing computers to generate 3D space from single images or textual prompts (e.g., 3D room layouts).
🤖 Spatial intelligence is catalyzing robotic learning by providing simulated 3D environments for infinite practice, moving beyond static ImageNet data to train robots on behaviors and actions.
🦾 Progress in robotic language intelligence allows robotic arms to perform complex tasks (like making sandwiches or opening drawers) based on verbal instructions from Large Language Models (LLMs).

Future Applications in Healthcare and Robotics
🏥 AI is being piloted in healthcare as ambient intelligence using smart sensors to detect improper handwashing, track surgical instruments, or alert teams about patient fall risks.
🦾 The next step involves interactive help, such as autonomous robots transporting supplies or augmented reality guiding surgeons.
🧠 The ultimate goal includes enabling patients with severe paralysis to control robots via non-invasive EEG cap signals (brainwaves) to perform everyday tasks, as demonstrated by a robot cooking a sukiyaki meal.

Key Points & Insights
➡️ The evolution of intelligence stems from the virtuous cycle of seeing $\rightarrow$ doing $\rightarrow$ learning better, which nature took millions of years to develop.
➡️ The digital Cambrian explosion will be fully realized only when computers and robots are powered by spatial intelligence, enabling interaction with the real and virtual 3D world.
➡️ Future AI development must be thoughtful and human-centered, aiming for computers and robots to become trusted partners that augment productivity while respecting human dignity.
➡️ The most exciting future involves AI growing more perceptive, insightful, and spatially aware to help pursue a better world.

📸 Video summarized with SummaryTube.com on Jan 17, 2026, 02:54 UTC