A thread about proofs described as "AI is intractable / AGI can't work". That 2024 paper and similar ones are worst-case analyses. That is, there are distributions that no machine learning technique can learn effectively. Yes, computers cannot learn everything. But what does that show?
To appear in Computational Brain & Behavior soon: the claimed 2024 proof (also in CBB) that AGI via learning is intractable also "proves" that ImageNet is intractable. My reading of the hole: equivocation on the variable D. Preprint here: arxiv.org/abs/2411.06498