I'm not sure if you read the paper, because the analogy is not tenuous. It is a period of training where they require the model to be completely deprived of standard input, and fed noise instead, so that they can essentially flip the sign on the learning rule.
It is a simple, easy to state, hypothesis of what sleep is doing in organic brains, and you should note that there is an extreme paucity of those.
Perhaps the artifical brain referred to in the article is not the kind of artifical brain you're used to reasoning about, but the goal of these researchers is not to optimize their performance on ImageNet, it is to discover how the brain actually organizes itself. You should give it a read.
Please that paper is from the Los Alamos National Laboratory, read it and understand it...or the simple way, steady learning need's some time to analyze restructure and combine the gained information's together witch they call 'sleep'
Blame the system. There can be a lot of pressure to make research accessible. Although generally laudable, I think this can backfire when applied to more abstract topics.
For ~70 years, the brightest minds on earth have been trying and failing to solve AI. Many of them refusing to consider that something useful can be learned from the human brain; the gold standard of what it means to be intelligent.
Yet today, cutting edge deep learning technology is based on a crude and increasingly inaccurate model of neurons.
If we're now making discoveries that are revealing artificial processes that are similar to our own, it's a sign we're headed in the right direction.
Unless we've specifically defined intelligence in a way that reflects ourselves.
The useful things that we want to get out of an AI system, i.e generalized learning of abstract concepts, are most clearly demonstrated by the human brain.
Since we now seem to prefer down votes over discussion, I'll just leave this with my own speculation that the reason for this strange avoidance of the brain is that it's a dead end for both academia and industry.
It's much easier and more profitable to expand on already existing machine learning technologies than to try and find some revolutionary breakthrough in neuroscience.
Well we accepted that animals are intelligent too (no really long ago but still), even that something like a swarm-intelligence exists, but yeah i know what you mean.
EDIT: Wow you changed your comment that much that my comment makes nearly no sense anymore.
I actually think this kind of nonsense comes from the predisposition humans have to empathise and anthropomorphise things.
So, yeah, in a way a brain is like a computer, it works as a metaphor. But no, a brain not a computer in reality. So a poor use of metaphor.
Or in another way, this sort of stuff comes from confusion in conflating the map with the terrain - a map can be useful at one level, but it is not the terrain itself so useless at a different level of observation.
It is a simple, easy to state, hypothesis of what sleep is doing in organic brains, and you should note that there is an extreme paucity of those.
Perhaps the artifical brain referred to in the article is not the kind of artifical brain you're used to reasoning about, but the goal of these researchers is not to optimize their performance on ImageNet, it is to discover how the brain actually organizes itself. You should give it a read.