@gkxspace: LLM is likely just the first stop for AI large models. Professor Biwei Huang divides AI paradigms into four generations: First generation (1990s): Small models learn correlations. Second generation (2010s): Small models learn causation. Third generation (current LLMs): Large models learn correlations. Fourth generation (next step): Large models learn causation. Over 30 years, models have grown from small to large...
Summary
Professor Biwei Huang proposes a four-generation theory of AI paradigms, believing LLMs are just the first step, and the future lies in causal world models. Aether AI has completed a $20 million funding round, dedicated to building causal world models.
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LLM is likely just the first stop on the AI express.
Professor Biwei Huang divides AI paradigms into four generations:
- First generation (1990s): Small models learn correlations
- Second generation (2010s): Small models learn causality
- Third generation (current LLMs): Large models learn correlations
- Fourth generation (next step): Large models learn causality
Over 30 years, models have scaled up, but what they learn hasn’t upgraded—still statistical correlations. LLMs are adequate for language and code because humans have already condensed regularities into text, where surface-level statistical signals suffice. Not so in the physical world, where laws are deeply hidden. VLA tried for three years, but bump the table up two centimeters and the robot fails.
Key takeaways:
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Compression is intelligence. LLMs with terabytes of parameters are essentially rote memorization. Once you understand the underlying laws, you don’t need that much capacity. Compute requirements will look completely different.
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Causality isn’t just for embodied AI. Biology, new materials, longevity—all stuck for the same reason: can’t tell driver from marker. Causal models can discover patterns from observational data that humans don’t yet know.
Today Professor Biwei Huang (@huang_biwei) leads Aether AI in announcing funding—the world’s first causal world model. With 12 years of deep work in causal AI and as the creator of Causal-Learn, I’m excited to see their next breakthrough!
Biwei Huang (@huang_biwei): I’ve spent over a decade working on causal discovery and causal AI. A lot of late nights, a lot of papers, and a lot of open questions.
Today we’re putting something into the world. Aether AI has raised $20M to build causal world models that understand mechanisms. We believe the
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