Honestly, I have a very hard time believing that. Machine learning has had an almost trailblazing relationship with the neuroscience community for years now, and it’s pretty comical. The number of moments where neuroscientists discover a structure or pattern developed for machine learning years and years ago and and then finally admit “Oh yeah… I guess that is how we worked all along,” is too damn high to be mere coincidence.

It sounds like you're pretty hard set on there being no ghost in the shell and pretty judgmental of anyone who thinks otherwise. I'm just saying you're far too certain you have the answers, as my example demonstrates. I also never said I believe a living being is jumping into existence because of whatever successful Turing test. I'm actually agnostic on that and think it's a waste of time trying to answer something that will never be answered. It's always going to come down to bickering over definitions and goalpost-shifting ("It can't be sentient if it doesn't model glial cells/recurrent cortical stacks/neurotransmitter densities/electrochemistry/quantum computational effects inside microtubules/the gut microbiome/the embedding within the environment/the entire rest of the universe like us"). I'd much rather play it safe and treat it as though it is conscious.

Maybe I'm misunderstanding you, but it sounds like you're now also being far too dismissive of the representational power tensors/linear operations and especially eigendecompositions can have (I could probably blow your mind with the examples I've seen), and of statistics as a loss function. After all, we as humans are literally no more than statistical mechanical partition functions of Von Neumann density matrices, what would you even use for a loss function instead? MSE, cross-entropy (perplexity), KL, L1/L2 are statistical and used to build every neural net you've heard about. The only difference between us and say a Turing-complete (nonlinear ReLU + attentional) Kalman filter for text like you're making GPT out to be is how the hyperparameters are adjusted. A Kalman filter uses Bayesian inference with either Laplace's method or maximum-likelihoodist rules, whereas we (and ChatGPT) are genetically rewarded for minimizing both cost (resp. perplexity) and nonlinear human feedback. Keep boiling things down and you'll find you're surrounded by philosophical zombies.

Edit: Didn't see the second paragraph you added. I'm not sure what ML orthodoxy you're from, but Chalmers' result is pretty well accepted in CogSci. The setup that you're describing, the Chinese room, is an appeal to common sense, but a lot of what motivates scientific development is trying to understand paradoxes and counter-intuitive results. Sure, it sounds absurd, but so does Schrödinger's cat or black holes, both of which failed to disprove the underlying phenomena. Chalmer's 1995 result came after the Chinese Room thought experiment (by about 15 years in fact) and updated the consensus since on the Chinese Room by stressing the importance of universality. Since your example has humans performing the computation, I would say it could be alive (depending on the complexity of the equations, are they reproducing the action potentials of a human brain?), and case in point I think the internet even before ChatGPT is the most likely and well-known contender for a mass of human scribbles being conscious.