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Comment by gwern | Hacker News Reader

the scans are actually of a retarded person with hydrocephalus, and are not scans of the near-mythical never-verified anecdote from Lorber (https://www.gwern.net/docs/iq/1980-lewin.pdf) of the 126 IQ guy (Hawks gives a couple reasons why that might not have been as impressive as it seems: http://johnhawks.net/weblog/reviews/brain/development/ten_pe... ).

So the best example doesn't have hard fMRI scan evidence, and the documented cases of hydrocephalus going along with an IQ of 70 or whatever are much less impressive - if you think the brain is doing something, you would expect profound deficits like that!

More interesting is a recent paper: "Miniature spiders (with miniature brains) forget sooner", Kilmer & Rodriguez 2019 https://www.gwern.net/docs/psychology/2019-kilmer.pdf

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About that John Hawks's evolutionary argument - "if humans with smaller brains could manage, lesser energy requirements would select for this":

I wonder if maybe having larger brains didn't bring extra survival advantages thanks to having available "spare capacity".

From modern day sports we know how bad head injuries can be (boxing, American football, soccer). If in our evolutionary history a chance of brain damage was common, larger brains could be difference between "brain damaged but still functional" and "brain damaged and dead".

Interesting side-effect could then also be availability of this extra capacity for other functionality in individuals which managed to escape brain damage.

I don't know, the mere fact that someone with brain damage that severe could even be conscious and interact with the world is pretty damned impressive to me. An AI with that kind of systemic damage would present a Blue Screen. The difference between vegetative and able to take an IQ test is far, far higher than the difference between an IQ of 70 and one of 130.

There are networks that can be “damaged” (in the sense of having neurons ripped out) and still function reasonably well.

Dropout is regularization technique in deep learning that works little like like that. Randomly selected neurons are ignored during training, typically the probability is 0.5. This means that half of the neurons don't work.

In other words, the network is trained with 50% 'brain damage' to make it learn better and become more robust.

Dropout is not deletion. The neurons turned off will be lit in the next run, and all would make into the model (brain).

(If anyone is wondering, yes, I did tell Watts about this years ago but he apparently hasn't gotten around to updating it.)

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