My god if I hear this argument one more time I'm going to pop. What on earth gives you the idea that you are something more than statistical inference?
Do you honestly believe statistical inference completely explains a human’s ability to learn?
Stating that we are no more than statistical inference is no different to saying that we are no more math - really, you can build the deepest mathematical truths on statistical inference.
The thing is that the math/s.i. that ML models do is too trivial in comparison to the math that would be required to describe a human identity. It can detect faces very well, it can calculate fifths roots very quickly, it can solve discrete problems quickly, yes, but its achievements are, in comparison to actual human understanding (and purpose), more closer to those of another tool, like a hammer, than those of humans.
edit: typos/grammar
"The thing is that the math/s.i. that ML models do is too trivial in comparison to the math that would be required to describe a human identity" why do you think this? How do you know the expressive power of neural networks? How have you guaged the expressive power of biological neural networks. How have you made any of these conclusions?
I am exasperated.
I never assumed we are more than math. Just that there is a lot to still learn about the structure in which those buildings blocks are organized in order to fully tame what is understood as human intelligence. If we could simulate all the neurons of a real adult living brain accurately enough it would be intelligent from a human perspective, and still, both the model and us would be equally far, very far, from understanding how to arrive to that point of neural organization to achieve that kind of intelligence.
But why think reason isn't a kind of statistical inference? GPT-3 demonstrates a rudimentary capacity for reasoning and its "merely" a statistical inference engine.
Induction isn’t probabilistic. It’s the origin of all discovery and based in sussing out what’s important in patterns, selectively proposing causal mechanisms and testing those hypotheses by following priors and implications — an essential basis for original and creative thought that no purely probabilistic engine can employ.