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The Generalization Problem – Q

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Current AI research has mostly converged on a single approach to AGI: gradient-based optimization of large neural networks on massive datasets. Deeper questions about intelligence have taken a backseat to brute empiricism, as theoretical results that aim at the fundamentals--Solomonoff induction , PAC learning--remain uncomputable or utterly impractical. Can we formulate principles of intelligence that are both universal and practical? How can these principles lead to learning algorithms where generalization scales optimally with compute--the one resource we can control? If we have infinite compute, what are the limits to how intelligent systems can be? We argue that solving these fundamental questions is a promising path to AGI. The bitter lesson says general methods that scale with compute always win. Yet our current scaling is fundamentally constrained by data availability. Compute grows exponentially and is fully programmable; data grows much slower and remains outside of our cont

The Generalization Problem – Q The Generalization Problem Our Research Direction Authors Samip Dahal, Akshay Vegesna Published August 2025 Current AI research has mostly converged on a single approach to AGI: gradient-based optimization of large neural networks on massive datasets. Deeper questions about intelligence have taken a backseat to brute empiricism, as theoretical results that aim at the fundamentals--Solomonoff induction , PAC learning --remain uncomputable or utterly impractical. Can we formulate principles of intelligence that are both universal and practical? How can these princi

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