3D printed artificial intelligence device identifies objects at the speed of light -- only $50 bucks!

3D printed artificial intelligence device identifies objects at the speed of light

(more at link: https://www.3ders.org/articles/20180803-3d-printed-artificial-intelligence-device-identifies-objects-at-speed-of-light.html )

Aug 3, 2018 | By Thomas

Using a 3D printer, a team of UCLA electrical and computer engineers has created an Artificial Intelligence (AI)-based device that can analyze large volumes of data and identify objects at the actual speed of light.

Deep learning is one of the fastest-growing machine learning methods in the machine learning community and is often used in medical image analysis, language translation, image classification, speech recognition as well as addressing more specific tasks, such as solving inverse imaging problems.

Traditionally, deep learning systems are implemented to be executed on a computer to digitally learn data representation and abstraction, and perform advanced tasks, comparable to or even better than the performance of humans. However the team led by Dr. Aydogan Ozcan, the Chancellor's Professor of electrical and computer engineering at UCLA, has introduced a physical mechanism to implement deep learning using an all-optical Diffractive Deep Neural Network (D2NN).

This optical artificial neural network device is intuitively modeled on how the brain processes information. It uses the light bouncing from the object itself to identify that object in as little time as it would take for a computer to simply “see” the object.

The process of creating the artificial neural network began with a computer-simulated design. Then, the researchers used a 3D printer to create very thin, 8 centimeter-square polymer wafers. Each wafer has uneven surfaces, which help diffract light coming from the object in different directions. The layers look opaque to the eye but submillimeter-wavelength terahertz frequencies of light used in the experiments can travel through them. And each layer is composed of tens of thousands of artificial neurons — in this case, tiny pixels that the light travels through.

Together, a series of pixelated layers functions as an “optical network” that shapes how incoming light from the object travels through them. The network identifies an object because the light coming from the object is mostly diffracted toward a single pixel that is assigned to that type of object.

Pretty cool!

They don't really talk about results or its accuracy much though. Could it for example tell the difference between a softball and a baseball? An orange and a tangerine?

Yeah, if it is as good as something like Intel's Movidius Deep Learning USB stick. Then image recognition is a whole new ballgame. Looks like this could be very easily hidden as well. Probably will get "compartmentalized" by US Mil if nothing is much about it in a few years. Apparently these sticks can be daisy-chained on USB hubs to make the modeling even more powerful/accurate.

https://techcrunch.com/2016/04/28/plug-the-fathom-neural-compute-stick-into-any-usb-device-to-make-it-smarter/

(Uses Caffe: http://caffe.berkeleyvision.org/tutorial/net_layer_blob.html
https://github.com/movidius/ncappzoo/
)

Movidius
Intel Launches Movidius Neural Compute Stick: Deep Learning and AI on a $79 USB Stick
by Nate Oh on July 20, 2017 11:00 AM EST
https://www.anandtech.com/show/11649/intel-launches-movidius-neural-compute-stick


Today Intel subsidiary Movidius is launching their Neural Compute Stick (NCS), a version of which was showcased earlier this year at CES 2017. The Movidius NCS adds to Intel’s deep learning and AI development portfolio, building off of Movidius’ April 2016 launch of the Fathom NCS and Intel’s later acquisition of Movidius itself in September 2016. As Intel states, the Movidius NCS is “the world’s first self-contained AI accelerator in a USB format,” and is designed to allow host devices to process deep neural networks natively – or in other words, at the edge. In turn, this provides developers and researchers with a low power and low cost method to develop and optimize various offline AI applications.

Movidius's NCS is powered by their Myriad 2 vision processing unit (VPU), and, according to the company, can reach over 100 GFLOPs of performance within an nominal 1W of power consumption. Under the hood, the Movidius NCS works by translating a standard, trained Caffe-based convolutional neural network (CNN) into an embedded neural network that then runs on the VPU. In production workloads, the NCS can be used as a discrete accelerator for speeding up or offloading neural network tasks. Otherwise for development workloads, the company offers several developer-centric features, including layer-by-layer neural networks metrics to allow developers to analyze and optimize performance and power, and validation scripts to allow developers to compare the output of the NCS against the original PC model in order to ensure the accuracy of the NCS's model.