This is incorrect. An undecidable problem is one for which no algorithm can compute the correct result for every given instance. Probabilistic classical computation is irrelevant here.
"obsolete dinosaur who hasn't written a line of code in decades"
The most recent code change in Emacs by RMS was on 2026-04-22 (0fb9d096e38), so ~3 months ago. He can write complex code (without AI), such as the `cond*` macro (707 LOC, all macro code), authored and pushed to Emacs on 2024-08-02 (18491f48d97).
I left Apple Music over this. My albums would keep quietly disappearing. I spent hours on the phone with their support, and despite their promises, nothing ever changed. I left Apple Music, and then all of their cloud services as well. Today, I'm 100% Apple-free, happily playing MP3s from Bandcamp in my Emacs. :)
But there is no dichotomy, is there? Good code is code that solves the problem well. Today and sustainably into the future. What else would "good code" even mean in general context?
This is incorrect reasoning. Science is advancing. It is like saying we should not listen to physicists because "Didn't those physicists gave us the original heliocentric system?"
No, that is plain old recursion. Dynamic programming is recursive programming with a twist. The twist is that identical sub-problems are short-circuited with memoization.
For me, Emacs on Mac OS is not all that stable. I see a freeze about twice a month, which is not "very rarely" in my book. It also leaks memory, albeit now (in the upcoming version) less so. (Disclaimer: I am a heavy user and contributor.)
Psychology is stuck in pre-Galilean era. Even if it studies "properties of thought", as you put it, it does so without formal basis, let alone understanding from first principles. As Chomsky said, about psychology and the like, "You want to move from behavioral science to authentic science." [1]
Yes. An artificial neuron, as a mathematical function f, is defined by f(x) = g(wx + b) where x is the input, w is the weight, b is the bias, and g is some non-linear activation function. Is that "good old linear regression followed by an activation function to make it non linear"? Yes, it is exactly that.
Abstraction is the opposite of specialization, not precision. Multiplication, for example, is an abstraction that can be defined in terms of repeated addition of the same term, which is less general, and so more special, than addition of two arbitrary terms, but it not less precise.