reminiscent of a sci-fi script character for rendering inert of the & # 39 object endowed with intelligence. Nevertheless, it is the researchers of the University of Wisconsin with a glass that can recognize images without electricity source.
You will not look at heras he can in a short time was able to spy on you. Researchers from the University of Wisconsin-Madison have managed to create a piece of smart glass, which recognizes the numbers and figures.
Optical neural network to replace the digital neurons
The principle that described Zongfu Yu and his colleagues in the reviewbased on optical neural networks, where replaced (See. Below). The following layers of leaves depart in a certain direction, which enables to spread the signal in the network to perform the following tasks. French Thus, it developed a "OPU" (optical processing) equivalent computer.
Here, researchers have found a way to get rid of these multilayered networks to simplify the process to at least what they call "Nanotechnics ". Small bubbles of different sizes and impurities like have included in the glass in certain places. Each bubble or an impurity having In particular, it focuses light on a specific location. Light emerging from the image window between one end and the output waves, and then focuses on the exit points 10 on the other side.
Train glass tip as a conventional artificial intelligence
howclassic, researchers taught II of, by presenting his collection of 5000 images of numbers from 0 to 9, written by hand in different ways. If light is not focused onto the correct exit point, size, and arrangement of the bubbles have been adjusted to the correct direction signal. After thousands of iterations glass could recognize each figure, including in the forms for which it was not trained. " Each impurity acts as an artificial neuron Zongfu Yu explains that the success of currently limited 79% after 1000 iterations, but it is related to the quality of glass, which is used for the experiment, and it can be improved by less dense, according to the study.
Biometrics and autonomous vehicles
one ofIt is believed that , For example, to unlock , Can also be found on the windshield of a recognize traffic signs. " Since it does not require it It has unlimited life, which means that it can provide a device for thousands of years Zongfu Yu says, and it's super-fast, because it runs on … , " In contrast, a man of vision, a reasonable glass is likely to be reserved for specific applications, Moderate Ming Yuan, professor at the University of Statistics . A piece of glass in order to recognize the figures, the other – for the determination of the letters, the second – for parties, etc. ".
- Researchers from the University of Wisconsin-Madison was able to teach a simple glass to recognize handwritten numbers.
- This new form of artificial intelligence passive, not requiring energy; it's very fast and economical.
- Especially it can be used for biometric or autonomous vehicles.
This neural network operates without electricity, the speed of light
Ucla researchers created a neural network of deep learning, which does not workwith classical computation, but with light waves propagates through translucent 3D-prints, in which thousands with relief distracting light.
The fact that only what has been achieved by a research team from the University of California at Los Angeles (UCLA), very exclusive. thanksthey created a deep neural network, which does not work with electricity, and with the light. They call it " diffractive deep neural network And to explain the principles of the work in an article published in .
In the vast world, Deep Learning ( ) Currently, it is the most popular technique. It relies on several methods (machine learning) For the training systems of the various types of data with which they will develop a model of representation and abstraction, which would later be used for the interpretation of the information, which they do not know. depth training uses various data layers, thus, the concept of depth.
These layers are complex, audio information is output, which serves as a starting point for the next, and so on. networkArtificial multilayer using this architecture. He has made great progress in voice and audio processing language filtering and analysis of medical images.
Neyranalnaya network of five layers
deep learning systems work on computers all classically. This is where innovation comes Ucla. Their deep neural network needs to electrons, but only in the light. It uses passive components that will perform specific functions, depending on their location. This translucent plate productionthousands of pixels are coated with a relief. Each plate is a layer of a neural network, the artificial neurons – the pixels that reflect or transmit light at a certain angle.
Researchers used five plyatsovak face to face with the space between them, like dominoes. To test the system, they asked him to recognize the numbers from 0 to 9. The preliminary phase of training of the neural network is obviously carried out on a computer from a computer.55000 images of numbers. Researchers have identified the connection created between each layer of artificial neurons and turned them into pixels that are responsible for the management of light in the same way that they then secure with the help of 3D-printing.
Many of the technical constraints remain
In figure was projected during the testusing a 3D-printing mask placed in front of the first layer. Light passing through the other layers of the neural network, focusing on certain areas. Photodetector at the other end of the network received the output signal light, and then brought back. The system has been tested by thousands of digits, the researchers report that it has reached about 95% accuracy.
While performance is certainly impressive, many technical limitations remain. First, you need a very precise alignment of the platelets to audition light could efficiently reproduce the neural connections. Then, the recognition system is only one of & # 39; an object that must first 3D print in the mask before his project. Modification of the projection device for a full demateryyalizatsyi – this is probably the way to where you can find a system that can find practical application.