Come on. Calling them neural nets doesn't make them that.
Actual neural nets are living compositions of individual predictors, in a constant state of restructuring and communication across multiple channels, infinitely more complex than static matrix multiplication on arbitray vectors which happen to represent words and their positions in sequences, if you just shake the jar long enough.
>It's pretty plausible that your intelligence is derived from gradient-descent prediction
I highly doubt that gradient descent in the calculus-sense is the determining factor that allows biological organisms to formalize and reason about their environment. Minimizing some cost function - yes, possible. But the systems at play in even the simplest organisms don't spend expensive glucose to convert sensory signals to vectors. Afaik, they work with representations of energy-states. Maybe there is an operational equivalence somewhere there though.
Gradient descent is an algo that optimizes derivatives wrt some cost function. An intelligent system may use the resulting inferences for its own fitness function, and it may do this using gradient descent itself, but at no point does the mechanical process of iterating over cost-values escape its algorithmic nature. A system performing symbolic reasoning may delegate cognitive tasks to context-specialized evaluators ("am I in danger?", "how many sheep are on that field?", "is this person a friend?", "what is a pumpkin?"), all of which are conditioned to minimize cognitive effort while avoiding false positives, but the sequence of results returned by those evaluators (think neural clusters) is observed by a centralized agent, who has to make new inferences in a living environment. Gradient descent fails at that.