I made the direct comparison to neural nets as it uses a very similar method to them (i.e. using weights as parameters and minimizing a cost function via gradie

I made the direct comparison to neural nets as it uses a very similar method to them (i.e. using weights as parameters and minimizing a cost function via gradient descent) but is simpler (gets rid of layers, neurons, activation functions, etc). I never stated "AGI means solving polynomials". Based on how far LLMs have come, function approximation seems to play a role in it.