
New Theory Cracks Open the Black Box of Deep Neural Networks - breck
https://www.wired.com/story/new-theory-deep-learning/
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rdlecler1
NN look like black boxes because we’re not stripping out spurious axons in the
network to understand the computational circuit. If you can remove a weight
(w_ij -> O) from a NN with no change in the network’s function across a broad
spectrum of inputs then it clearly wasn’t necessary in the first place as that
weighted edge was not involved in the information processing. Training
networks will not only tune certain weights to dial up on signal but they will
also (effectively) zero out certain weights that don’t carry information (if
they’re not effectively zeroed our then they introduce noise and compromise
the network function), in effect reducing the noise and focusing on signal.
For anyone interested I wrote a paper in Natures Molecular Systems Biology in
2008 which goes into this. It describes artificial gene regulatory networks
but my background was in AI and the NN math is the same. I believe the results
are universally applicable across a broad range of NN-class network. Lots of
relevant remarks in the supplementary as well. This includes a description of
an algorithm to prune the network of spurious interactions.

[http://m.msb.embopress.org/content/4/1/213.abstract](http://m.msb.embopress.org/content/4/1/213.abstract)

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notaboutdave
Waste of a read! Reasoning that going from many input nodes to few output
nodes results in compression (or shedding of nonessential data) isn't a
brilliant theory, it's common sense.

The author even beats the dead horse of AI being a potential existential
threat.

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Zee2
That's exactly what I was thinking. Wow, a neural network discards information
at each layer by reducing the number of cells in each matrix? Water is wet.

How else would a network make a final decision? If it never discarded any
information, it would be just as heterogeneous of a dataset as when it
started. It would be as if a linear regression algorithm "fit" a dataset by
connecting the dots, except even more useless.

I truly don't understand what this "new" theory is trying to say.

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carlmr
It's so basic I was thinking they could have called me, someone who studied
neural networks only for half a year in uni, ten years ago. I could have told
them that.

Either the journalist compressed the information a bit too much and lost the
essential amazing insight. Or it's just not amazing.

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mrfusion
So there’s not chance the brain does back propagation right? I wonder what it
might do instead?

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carlmr
I wouldn't say not a chance, we have no clue what the brain does.

Only because a brain can learn building on old concepts doesn't mean anything
about the basic algorithm being different. The brain is a much bigger and
longer living neural network.

What they were explaining with the child learning character recognition with
the knowledge about strokes. Why would you think our brain doesn't simply
connect some of the stroke recognizing neural networks to a higher level
neural network built for character recognition. You can increase the depth of
the network basically indefinitely. Stack various neural networks to produce
more complex ones, etc.

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Shikadi
I don't think it's a good idea to be calling the human brain a neural network
in this context, as cnns/dnns only sort of vaguely resemble brain neurons. The
differences are massive, both due to different neurotransmitters as well as
different connections being formed, and other stuff

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carlmr
That doesn't have much to do with my point, also the brain is the original
neural network. If anything you should argue to call artificial neural
networks by a different name.

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mrfusion
How big of a deal is this from 1-10?

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gcoda
Like any other theory in science. Nuclear power or glow in the dark toys,
usually both, if enough time pass

