Felismerés minden szinten

Amerikai kutatók biológiai modellen, az agyon alapuló „többszörös” felismerő-rendszert fejlesztenek.

Software systems could one day analyze everything from blurry war-zone footage to the subtle sarcasm in a written paragraph, thanks to two unassuming scientists who are inspired by biology to make revolutionary strides in intelligent computing. Yann LeCun and Rob Fergus, both computer science professors at New York University, are the brains behind “Deep Learning,” a program sponsored by Darpa. The idea, ultimately, is to develop code that can teach itself to spot objects in a picture, actions in a video, or voices in a crowd. Existing software programs rely heavily on human assistance to identify objects. A user extracts key feature sets, like edge statistics and then feeds the data into a running algorithm, which uses the feature sets to recognize the visual input. LeCun said, “There’s some sort of learning algorithm within the brain. We just don’t know what it is.” But the algorithmic talents of the mind, along with its ability to identify visual data by abstraction, will be the key components of the NYU team’s new system. Right now, an algorithm recognizes objects in one of two ways. In one, it is shown some representative examples of what, say, a horse looks like. Then the code tries to match any new creature to the ur-stallion. (That’s called “supervised” learning.) In the other way, the software is shown lots and lots of horses, and it builds its own model of what a horse is supposed to resemble. (That’s “unsupervised” learning.) What LeCun and Fergus are trying to do is make code that can get it right on a first, unsupervised example — using layer after layer of code to abstract the essential attributes of an object. And this is only the beginning. Darpa also wants a system that can spot activities, like running, jumping or getting out of a car. The final version will operate unsupervised, by being programmed to hold itself accountable for errors — and then auto-correct them at each algorithmic layer. It should also be able to apply the layered algorithmic technique to text. Right now, computer systems can parse sentences to categorize them as positive or negative, based on how often different words appear in the text. By applying layers of analysis, the Deep Learning machine will — LeCun and Fergus hope — spot sarcasm and irony too.

IT3 komment: A használatban és fejlesztési stádiumban lévő felismerő-rendszerek általában egy területen tevékenykednek: kép, hang/beszéd, szöveg stb. A DARPA által szponzorált „Mély tanulás” program keretében két kutató önmagától tanuló különleges képfelismerő algoritmuson dolgozik, amit a későbbiekben hangok, szöveg stb. azonosítását is elvégezné. Olyan szinten, hogy például mondatokat pozitív és negatív tartalmuk alapján különböztetne meg egymástól. A tervek szerint „Mély tanulás” komplex felismerő rendszerként működik majd.

Forrás: www.wired.com