
Researchers Move Closer to Completely Optical Artificial Neural Network - benryon
https://www.osa.org/en-us/about_osa/newsroom/news_releases/2018/researchers_move_closer_to_completely_optical_arti/
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sytelus
Tip: When you read articles that says _" researchers might have..."_ or _"
researchers get closer to..."_, read them as follows:

These folks ran out of their funding and need to renew their grants. For that
purpose here's their progress report and no, tech is still quite far away.
Watch out for same article title next year same time.

To be clear, there is nothing wrong with this. Some progress takes time and
funders just need to know that folks are working hard at it.

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OscarCunningham
I think it has more to do with the way that researchers describe their
projects to journalists. Whatever you're working on is to complicated to
explain properly, but you can at least say what it might eventually be useful
for. So you tell that to the journalist, and (if they are good) the journalist
publishes "A step towards X" (if they're a bad journalist you'll see
"Scientists discover X").

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etaioinshrdlu
Can it compute anything non linear? I.e. can it actually have any activation
function?

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ovi256
Without a non-linear activation function, it wouldn't be an ANN, because
multiple linear layers are equivalent to a single layer applying the
composition of their transforms.

The article gives no clue about this part. I have no idea if the optical
domain can compute non-linear functions.

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jf-
If you want a non-linear function that can be produced optically, consider an
evanescent wave[1], as used in TIRF microscopy. Whether this is suitable in
practise as an activation function I couldn’t say, though the curve does to my
eye look like something that could be used in place of relu.

[1]
[https://en.m.wikipedia.org/wiki/Evanescent_field](https://en.m.wikipedia.org/wiki/Evanescent_field)

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mmf
Optical computation will never become relevant at scale. There are fundamental
reasons for this: first, particle size. A photon at usable wavelengths is
extremely large, much larger of any modern electron based _devices_ This makes
it imossible to scale to usable density. Second, optic-optic (as opposed to
electro-optic) non linear effects are based on interaction with electrons, in
particular with electron decay from an energy state to another which is
tipically extremely slow.

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taneq
"If an elderly but distinguished scientist says that something is possible, he
is almost certainly right; but if he says that it is impossible, he is very
probably wrong." \- _Arthur C. Clarke_

~~~
nabla9
"A platitude is a trite, meaningless, or prosaic statement, often used as a
thought-terminating cliché, aimed at quelling social, emotional, or cognitive
unease" – Wikipedia

~~~
taneq
"Touche." \- Me.

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EchoAce
It may be my lack of knowledge about optics, but from an ML perspective this
seems rather mundane if not useless. Model training involves high levels of
parallelism on a large scale for difficult tasks, something I can’t see these
optical chips doing. Does anyone have any further information that might
enlighten me to otherwise?

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stochastic_monk
By performing these transformations optically, they primarily get data
parallelism (like [GTV]PUs). I expect this to happen. NVIDIA’s ACDC paper
provides an FFT-accelerated neural network layer (similar to deep-fried
convnets), with an offhand remark that the transformations could be performed
optically. I wonder what kind of information bandwidth they can get, though.

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dekhn
Physicists were using optical lenses to do approximate FFTs over a hundred
years ago.

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lawlessone
Makes me think of the positronic brains from Asimovs books.

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bra-ket
related: "All-optical machine learning using diffractive deep neural networks"
[https://news.ycombinator.com/item?id=17698135](https://news.ycombinator.com/item?id=17698135)

