alt Hacker News> Ok, but my argument wasn’t with the OP mentioning capacity, but with their analogy. Am I not allowed to break down that analogy with evidence?
If someone makes an analogy between aspects of A and B, then there is an implicit assumed shared understanding that A & B are DIFFERENT, and what is being pointed out is that they nonetheless may be considered as having something, typically fairly abstract, in common.
Calling a horse's reins as analogous to a cars steering wheel doesn't mean that the person making the analogy can't tell a horse from a car - that would be a very dumb take. A reasonable criticism might be "it's an imperfect analogy since the reins also act as the brake".
If you disagree with an analogy, then you need to address the analogy, not do the dumb take of "a horse is not a car" ("a brain is not a computer").
So, yes, you're "allowed" to "break down" (criticize) the analogy, but please don't just do the dumb take. So, hash key collisions are not analogous to associative recall and sparse embedding overlaps in what way?
Replies
> Calling a horse's reins as analogous to a cars steering wheel doesn't mean that the person making the analogy can't tell a horse from a car
When a person says “I crashed the car because my steering wheel tore, like reigns tear”, they are overfitting their analogy, and it’s perfectly fine to point out the structural and physical differences that make the analogy useless for the question at hand.
You have dismissed the biology that shows the problems with your analogy as immaterial. And continue to insist it’s the right one for the question at hand. This is a pretty pickle, because no facts can shake you from your certainty that you're right.
You seem in love with your analogy no matter how incorrect it is. And I have no problem with that. But when you put half baked biological claims to support it, I’ll point it out. If that’s too much for you to bear, maybe come up with better analogies?
Kind of done with the other guy, but reading this reminded me of a paper I saw a while ago but admit I haven't read yet. https://journals.aps.org/prx/pdf/10.1103/6shh-9h5m
It's more about computational complexity it seems but maybe it cites stuff that might interest you.
It's using computational neural networks that no one has ever believed represent the biology of the brain, but I think I must still disclose the following to save the precious time of the genius solving the problem all by himself:
It doesn't fully account for every biological detail ever documented, so it's probably a meaningless "curiosity".
Oh it probably also doesn't account for every detail discovered since publication so even if had value at time of publication it is not worth reading now.