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#cognitive-biases2#linguistic-intelligence1#spatial-intelligence4
Moving AI out of the data‑center into the world requires spatial models
  • The next decade of computer vision will shift from training inside massive data‑centers to deploying models that act directly in physical environments.
  • This transition demands models that understand geometry, dynamics, and affordances, not just static image classification.
  • Spatial intelligence therefore becomes the missing piece for embodied agents, robotics, and AR/VR applications.
  • World Labs’ focus on world models is a direct response to this emerging need.
Fei-Fei LiLatent Space00:03:07

Supporting quotes

Quote
00:03:07
“The next decade of computer vision will be about getting AI out of the data centre and out into the world.” — Fei-Fei Li
Quote
00:03:32
“If you think about AlexNet, the core pieces of it were obviously ImageNet, it was the move to GPUs and neural networks.” — Fei-Fei Li

From this concept

Spatial Intelligence vs. Language Intelligence

The conversation distinguishes spatial reasoning--understanding, moving, and interacting in 3-D space--from linguistic processing. Humans are born with high-bandwidth visual perception, while language is a comparatively low-bandwidth, symbolic channel. Building AI that matches human spatial acuity requires models that go beyond token-by-token prediction.

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