Comment by estearum
8 hours ago
Looks interesting (added to my list, thank you for the recommendation), but not actually relevant to the topic at hand.
We know that the brain is a probabilistic input → output machine because the universe is a probabilistic input → output machine. The brain is made of universe. There are deterministic relationships (which at high sensitivity or complexity become easier to describe as probabilistic), and quantum relationships. That's it. The brain, like every other thing comprised of "universe" is comprised of those two types of relationships.
If Romain's book provides evidence of relationships in the brain that are neither quantum (therefore random) NOR classical (therefore deterministic), then 1) he would have already won at least one Nobel prize, and 2) anyone in this thread would be able to at least gesture toward what relationship that is.
This is a vacuous argument. If everything is a “probabilistic input output machine” then what exactly are you claiming?
You should look into mathematical definitions of probability. You will be surprised to find not all formal mathematical systems can fit inside probability spaces.
Formal mathematical systems are not physical objects. The brain – which is presumably the source of natural intelligence – is.
By "everything" I was referring (obviously) to things in the universe. Mathematical systems are not things in the universe. They are not made of universe the way every object, including a brain, is.
And no, it's not vacuous at all. GP is trying to argue that a probabilistic input → output machine doesn't produce intelligence. But we have an existence proof of exactly that!
You're a fast reader :-D
No quantum bullshittery in there I promise.
Equating "classical" with "deterministic" is however pushing it a bit too far, when no one and nothing can ever do even a very small fraction of said determination...
Quantum is not bullshit and yes, classical is deterministic.
That's why the brain cannot possibly be anything other than an input → output machine, which is functionally deterministic (with maybe some fully random components), but is easiest to describe as probabilistic.
In the same way that LLMs are functionally deterministic, but easiest to describe as probabilistic.