@rohanpaul_ai: Intelligence may be less about bigger models and more about better knowledge structures. This paper argues that current…
Summary
This paper argues that biological intelligence is efficient due to organizing meaning around goals and context rather than language patterns, proposing Synthetic Intelligence using structured semantic knowledge and Asymmetric Information Resolution models.
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Cached at: 06/25/26, 09:27 PM
Intelligence may be less about bigger models and more about better knowledge structures.
This paper argues that current AI is being built mostly on network mathematics, not on a theory of knowledge.
A human brain makes fast, adaptive decisions on roughly the power of a dim light bulb, while frontier AI often buys competence with enormous computation.
The paper says biological intelligence may be efficient because it organizes meaning around goals, context, and decisions, instead of mainly searching through language patterns.
It separates mental activity into physical cognition, emotional cognition, mental cognition, and intelligence, where intelligence means making useful decisions while the situation still matters.
The proposed answer is Synthetic Intelligence, which would use structured semantic knowledge, meaning information tied to purpose, rather than only syntax, statistics, or neural network weights.
The paper uses Asymmetric Information Resolution models to show how knowledge can be arranged into decision maps, with a simple predator-prey example where each state has only a few possible moves.
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