Slashdot Mirror
3D-Printed Deep Learning Neural Network Uses Light Instead of Electrons (newatlas.com)
Matt Kennedy from New Atlas reports of an all-optical Diffractive Deep Neural Network (D2NN) architecture that uses light diffracted through numerous plates instead of electrons. It was developed by Dr. Aydogan Ozcan and his team of researchers at the Chancellor's Professor of electrical and computer engineering at UCLA. From the report: The setup uses 3D-printed translucent sheets, each with thousands of raised pixels, which deflect light through each panel in order to perform set tasks. By the way, these tasks are performed without the use of any power, except for the input light beam. The UCLA team's all-optical deep neural network -- which looks like the guts of a solid gold car battery -- literally operates at the speed of light, and will find applications in image analysis, feature detection and object classification. Researchers on the team also envisage possibilities for D2NN architectures performing specialized tasks in cameras. Perhaps your next DSLR might identify your subjects on the fly and post the tagged image to your Facebook timeline. For now though, this is a proof of concept, but it shines a light on some unique opportunities for the machine learning industry. The research has been published in the journal Science.
Perhaps your next DSLR might identify your subjects on the fly and post the tagged image to your Facebook timeline.
No thank you.
and the "magazine" article, get the research paper straight from the horse's mouth for free.
http://innovate.ee.ucla.edu/wp...
They develop the network, then 3D print it. Once it's printed it performs a static task.
That sounds like one of the coolest magic tricks ever performed.
Set up a lamp shining on a screen and have someone paint a number on a glass plate. Put it in front of the lamp to project it on the screen. Add a stack of plates and instead of the number you will see a bright spot in a field on screen representing the number. And that without any hidden active equipment making a "decision" of any kind. Just a combination of refracting patterns.
bickerdyke
As long as it take more space and more weight than an IC performing an equivalent task, this will stay a nice research subject. :(
We replaced hardware radio receptor by software ones, I don't see why we would replace software neural network by hardware ones... Plus, they miss the 'plasticity' of software ones and a scratch would make it 'dead'
The concept of learning algorithms was developed in the 60's and Deep Learning was coined in the late 80's. Either had hardly any practical application at the time they where introduced. Fast forward and companies are now poring billions into this kind of technology. Point being nobody cared about most things, before they suddenly became the next big thing.
So basically this is like 3D printing a "machine" that is capable of instantaneously evaluating a R^2 -> R^2 function for some degree of precision (specially in the detectors side) and assuming you have already found a good enough NN representing the function.
Disclaimer: I have not read the paper.
Kill the rich? Starting with you?
Oh, you don't think you're rich. That's funny. You'll be classified that way once you're no longer useful.
Remember the kulaks.
....the Matrix started.
I SURVIVED THE GREAT SLASHDOT BLACKOUT OF 2002!
So this is the modern version of punch cards?
"Your most unhappy customers are your greatest source of learning." Bill Gates Yeah Right!
and you have the most 2018 article.
Unless it's also an IoT device that mines bitcoins in the cloud I'm not interested.
The impressive part of the work is the fact that they were able to print materials that control light so well, not the actual network itself. The weight optimization and topology design were still done using standard computing hardware - this is just a physical realization of a trained network. You could build something equivalent out of water and tubes (though it would be slower and wetter obviously). The cool part is the optical control which is now possible, not the fact that MNIST works.
... that's some serious buzzword bingo there.
Iâ(TM)m sure it will scale. But will it scale better than an ASIC?
You could probably design some kind of adaptive optical element that could change its local diffractive properties on command, which would let you train something like this, or adapt it to different situations. But at the moment, and probably for some time, that sounds quite a bit more complicated than just reprogramming an FPGA or loading different values into one of googleâ(TM)s TPUs or tweaking some resistances in an analog (electrical) computer.
You might be able to come up with some very specialised applications where a fixed filter thatâ(TM)s faster than four or five analog elements in series is useful, but Iâ(TM)m having troubling thinking of one.
You can do a Fourier transform with light too, but nobody ever does because itâ(TM)s much easier to use a dsp.
I hope I live to see the day when we just say "manufactured" instead of "3D-printed"
This posting is provided 'AS IS' without warranty of any kind, implied or otherwise.
There wasn't enough computing power back in the late 1980's. For a desktop PC with an 66MHz x86, graphics boards with i860's, TMS34020's and some TMS320x0's were the most performance you could get. Even then, those boards were around $1200, and you were lucky to get a C compiler let alone Fortran or C++. Even then rendering a single frame of the Mandelbrot set would still take minutes.
Today, you can buy a PC or server with custom DNN hardware, able to process Terabytes of image data.
Vintage computer adverts: http://www.vintageadbrowser.com/computers-and-software-ads
Either had hardly any practical application at the time they where introduced. Fast forward and companies are now poring billions into this kind of technology. Point being nobody cared about most things, before they suddenly became the next big thing.
Bullshit. There just wasn't enough computing power nor big enough data sets to do much with the technology.
If someone had invented an analogue analogue to the neuron as a component may be in IC form factor, the world would be a very different place today.
While the slashdot summary uses the term "light", the paper states that they used a 0.4 THz source — not the frequency/wavelength most people think of when they hear the word "light".
Check out the perceptron, it is almost exactly what you describe, made a long time ago
"First they came for the slanderers and i said nothing."
This must be at the top of many search requests, but does it actually make sense?
No - he employs a neural net to throw the darts.
Sent from my ASR33 using ASCII
The concept of learning algorithms was developed in the 60's and Deep Learning was coined in the late 80's. Either had hardly any practical application at the time they where introduced. Fast forward and companies are now poring billions into this kind of technology. Point being nobody cared about most things, before they suddenly became the next big thing.
Making a machine learning network does not require any of these new things. The ones first used in the 1960's were much slower, but just as good at learning.
https://www.youtube.com/watch?...
Also, Neural networks (that are not really AI at all) only need to be used for the learning. Once done they can be reduced to only the active logic and constructed as code or hard circuits. But they are useful for detecting logic that no one had recognized before that.
I don't think there was an analogue hardware implementation of the perceptron in the 80s or 90s even?
Back in the 60's lasers were considered a solution without a problem.
I'm pretty sure the original was https://en.m.wikipedia.org/wik... but maybe the particular aspect you are looking for was not.
"First they came for the slanderers and i said nothing."
It reminds me of the intro sequence from an Ian Banks book (I can't remember which one) where a robot is on a ship which is attacked and is forced to eject different levels of consciousness each of which uses a different underlying technology. The photonic brain was the third most powerful level of consciousness it had. It eventually has to resort to it's "primitive" silicon brain because everything else has been corrupted. Maybe the book was by Vinge Verge. I can't remember...