Working in the cognitive sciences is very weird, because you can almost always point to an idea in neuroscience and cognitive psychology, to something about the real brain, that makes a new machine learning paper look downright trivial. Simultaneously, actually implementing even established ideas about cognition from the meat-side to the software-side tends to take a lot of work.
Admittedly, IMHO, this is often because machine learners are constantly trying to transform any idea from neuroscience or cognitive science into an idea about supervised loss minimization in a neural network, runnable on a graphics card, rather than having to come up with ways of efficiently computing the stuff that we're confident the brain actually computes.
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[ 3.6 ms ] story [ 40.5 ms ] threadhttps://worldmodels.github.io/doomrnn/
Original article:
https://worldmodels.github.io/
PDF version of the paper:
https://arxiv.org/abs/1803.10122
Nice.
Admittedly, IMHO, this is often because machine learners are constantly trying to transform any idea from neuroscience or cognitive science into an idea about supervised loss minimization in a neural network, runnable on a graphics card, rather than having to come up with ways of efficiently computing the stuff that we're confident the brain actually computes.
Additionally, the brownish blur has some additional variation at the top of the screen.
I'm clocking more than 2GB memory usage for this site after 30 secs on firefox.
EDIT: Seems to be firefox specific. Lots of CPU use on Chrome but memory consumption seems to be under control.