Technology

Machine learning is everywhere these days, but itusually more or less invisible: it sits in the background, optimizing audio or picking out faces in images. But this new system is not only visible, but physical: it performs AI-type analysis not by crunching numbers, but by bending light. Itweird and unique, but counter-intuitively, itan excellent demonstration of how deceptively simple these &artificial intelligence& systems are.

Machine learning systems, which we frequently refer to as a form of artificial intelligence, at their heart are just a series of calculations made on a set of data, each building on the last or feeding back into a loop. The calculations themselves aren&t particularly complex — though they aren&t the kind of math you&d want to do with a pen and paper. Ultimately all that simple math produces a probability that the data going in is a match for various patterns it has &learned& to recognize.

The thing is, though, that once these &layers& have been &trained& and the math finalized, in many ways itperforming the same calculations over and over again. Usually that just means it can be optimized and won&t take up that much space or CPU power. But researchers from UCLA show that it can literally be solidified, the layers themselves actual 3D-printed layers of transparent material, imprinted with complex diffraction patterns that do to light going through them what the math would have done to numbers.

If thata bit much to wrap your head around, think of a mechanical calculator. Nowadays itall done digitally in computer logic, but back in the day calculators used actual mechanical pieces moving around — something adding up to 10 would literally cause some piece to move to a new position. In a way this &diffractive deep neural network& is a lot like that: it uses and manipulates physical representations of numbers rather than electronic ones.

As the researchers put it:

Each point on a given layer either transmits or reflects an incoming wave, which represents an artificial neuron that is connected to other neurons of the following layers through optical diffraction. By altering the phase and amplitude, each &neuron& is tunable.

&Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement,& write the researchers in the paper describing their system, published today in Science.

To demonstrate it they trained a deep learning model to recognize handwritten numerals. Once it was final, they took the layers of matrix math and converted it into a series of optical transformations. For example, a layer might add values together by refocusing the light from both onto a single area of the next layer — the real calculations are much more complex, but hopefully you get the idea.

By arranging millions of these tiny transformations on the printed plates, the light that enters one end comes out the other structured in such a way that the system can tell whether ita 1, 2, 3 and so on with better than 90 percent accuracy.

What use is that, you ask Well, none in its current form. But neural networks are extremely flexible tools, and it would be perfectly possible to have a system recognize letters instead of numbers, making an optical character recognition system work totally in hardware with almost no power or calculation required. And why not basic face or figure recognition, no CPU necessary How useful would that be to have in your camera

The real limitations here are manufacturing ones: itdifficult to create the diffractive plates with the level of precision required to perform some of the more demanding processing. After all, if you need to calculate something to the seventh decimal place, but the printed version is only accurate to the third, you&re going to run into trouble.

This is only a proof of concept — thereno dire need for giant number-recognition machines — but ita fascinating one. The idea could prove to be influential in camera and machine learning technology — structuring light and data in the physical world rather than the digital one. It may feel like itgoing backwards, but perhaps the pendulum is simply swinging back the other direction.

Amazon web services AWS continue to be the highlight of the companybalance sheet, once again showing the kind of growth Amazon is looking for in a new business for the second quarter — especially one that has dramatically better margins than its core retail business.

Despite now running a grocery chain, the companyAWS division — which has an operating margin over 25 percent compared to its tiny margins on retail — grew 49 percent year-over-year in the quarter compared to last yearsecond quarter. Italso up 49 percent year-over-year when comparing the most recent six months to the same period last year. AWS is now on a run rate well north of $10 billion annually, generating more than $6 billion in revenue in the second quarter this year. Meanwhile, Amazonretail operations generated nearly $47 billion with a net income of just over $1.3 billion (unaudited). AmazonAWS generated $1.6 billion in operating income on its $6.1 billion in revenue.

So, in short, Amazondramatically more efficient AWS business is its biggest contributor to its actual net income. The company reported earnings of $5.07 per share, compared to analyst estimates of around $2.50 per share, on revenue of $52.9 billion. That revenue number fell under what investors were looking for, so the stock isn&t really doing anything in after-hours, and Amazon still remains in the race to become a company with a market cap of $1 trillion alongside Google, Apple and Microsoft.

This isn&t extremely surprising, as Amazon was one of the original harbingers of the move to a cloud computing-focused world, and, as a result, Microsoft and Google are now chasing it to capture up as much share as possible. While Microsoft doesn&t break out Azure, the company says itone of its fastest-growing businesses, and Google&other revenue& segment that includes Google Cloud Platform also continues to be one of its fastest-growing divisions. Running a bunch of servers with access to on-demand compute, it turns out, is a pretty efficient business that can account for the very slim margins that Amazon has on the rest of its core business.