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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.
and the "magazine" article, get the research paper straight from the horse's mouth for free.
http://innovate.ee.ucla.edu/wp...
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.
Exactly this. They have bleeding edge technology on their hands and want to use it to post on FB? Get the fuck out of here.
sudo rm -r -f --no-preserve-root /
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.
We still say "machined" when we could just say something was manufactured via machining. And we will continue to machine forged billets for the foreseeable future, because forged metals have a superior internal structure as compared to sintered. Additive manufacturing is likely to never completely replace other forms of manufacturing, at least not until we develop true nanotechnology and can build our own structures at that level. And it is highly doubtful that will happen in any of our lifetimes.
"You're right," Fisheye says. "I should have set it on 'whip' or 'chop.'"
Additive manufacturing will never replace common manufacturing techniques like casting or forging. This isn't just due to structure, but energy costs as well. Rastering layer by layer, constantly heating and cooling, is far more energy intensive than a "melt and pour". Batch injection molding 1000 parts will always be faster than build by layer.
Not to shit on AM however - complexity becomes free. With AM you can make a gadget as optimum as you want with intricate bio-inspired structural conmponents, and so long as production volumes are relatively low, say 10,000, AM might be able to compete based on the combination of better performance and material cost savings. Things like rocket engine components are ideal for this.
Not necessarily a revolution, but a very welcome new tool. CNC machining probably had a greater impact, but went largely unnoticed by the general public. Advanced automation (think robotics mixed with machine learning and AI) is probably poised to make a larger contribution than AM, IMO.