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#distributed-compute2#model-architectures2#transformers1
Emergent capabilities require architectural innovation, not just scale
  • Larger models have shown surprising abilities (e.g., zero‑shot physics reasoning), but these are not guaranteed to appear with scale alone.
  • Introducing inductive biases—such as explicit geometry or physics modules—may accelerate the emergence of useful behaviours.
  • The team observes that certain capabilities appear only when the architecture aligns with the data modality.
  • Therefore, research must pursue both scaling laws and novel model designs to unlock true spatial intelligence.
Fei-Fei LiLatent Space00:28:11

Supporting quotes

Quote
00:28:11
“Scaling may bring emergent physics understanding; larger models show emergent capabilities.” — Fei-Fei Li
Quote
00:56:02
“Sequence‑to‑sequence may still be useful but we shouldn't discard what works.” — Fei-Fei Li

From this concept

Future Model Architectures Beyond Transformers

Transformers treat inputs as sets of tokens, which works well for language but is sub-optimal for spatial data that lives in 3-D. The discussion highlights the need for new primitives that map better to distributed hardware and for architectures that can capture physical laws implicitly.

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