Mathematician debunks proof that AI can't achieve human-like intelligence

A recent paper claimed to have mathematically proven that machine learning cannot produce human-level intelligence. A new preprint shows the proof rests on an assumption that was never justified. The problem: nobody has defined what 'human-like intelligence' actually means in mathematical terms, and the proof ignores how real machine learning systems are built with specific biases that shape what they can learn.

The original paper was meant to settle a decades-old debate using pure mathematics — proving it's impossible, not just hard. If that proof held, it would have closed off entire research directions as mathematically futile. But this critique shows the proof was built on sand: it assumes something about how data is distributed in the world without ever justifying that assumption. What matters here is not whether AGI is possible, but that mathematical 'proofs of impossibility' in this space are fragile. They smuggle in unstated assumptions and then declare the conclusion inevitable.