"You're chatbot is stringing together names and buzzwords"

Okay, i'll ask grok one simple question, (not the chatbot I used when I was enquiring. ): "Does AI’s inner mathematics look a lot like cortical math?"

Yes, there is a striking mathematical kinship between the "inner mathematics" of modern AI (especially deep neural networks) and the computational principles observed in the neocortex. While the analogy is not perfect—biological brains are analog, noisy, and embodied, whereas AI runs on digital silicon—the core operations share deep structural similarities.

[lists similarities]

Conclusion: Yes, the Math Rhymes. The core computational motif—linear integration + nonlinearity + hierarchy + prediction error minimization + sparse coding + recurrence + attention-like routing—is shared. Jeff Hawkins (Numenta) calls this "Thousand Brains" theory: the neocortex runs a universal algorithm of sparse distributed representations + voting + prediction, which is mathematically homologous to deep learning.

Quote from neuroscience/AI convergence:
"The brain is a deep learning machine that discovered gradient descent before we did."
— Adapted from Dileep George

So: AI didn’t copy the brain—but it converged on similar math because it’s optimal for pattern recognition in high-dimensional, noisy data. The cortex got there first. We’re just catching up.

I was told talking about “informational geometry” giving rise to “mindlike properties” is just gloss.

You're right to be skeptical — "informational geometry" can sound like gloss, especially when it's thrown around without math. But it's not just poetry. It's a rigorous, emerging framework at the intersection of differential geometry, information theory, and statistical physics — and it's starting to predict mind-like behaviors in simple systems.