Sources Contact Advanced Search Tutorials

An Interest In:

Web News this Week

Search Archive

Some of Our Sources

View All Sources

Help Webnuz

Referal links:

December 20, 2019 06:45 pm

The Next Frontier in AI: Nothing

How an overlooked feature of deep learning networks can turn into a major breakthrough for AI. From a report: Traditionally, deep learning algorithms such as deep neural networks (DNNs) are trained in a supervised fashion to recognize specific classes of things. In a typical task, a DNN might be trained to visually recognize a certain number of classes, say pictures of apples and bananas. Deep learning algorithms, when fed a good quantity and quality of data, are really good at coming up with precise, low error, confident classifications. The problem arises when a third, unknown object appears in front of the DNN. If an unknown object that was not present in the training set is introduced, such as an orange, then the network will be forced to "guess" and classify the orange as the closest class that captures the unknown object -- an apple! Basically, the world for a DNN trained on apples and bananas is completely made of apples and bananas. It can't conceive the whole fruit basket. While its usefulness is not immediately clear in all applications, the idea of "nothing" or a "class zero" is extremely useful in several ways when training and deploying a DNN. During the training process, if a DNN has the ability to classify items as "apple," "banana," or "nothing," the algorithm's developers can determine if it hasn't effectively learned to recognize a particular class. That said, if pictures of fruit continue to yield "nothing" responses, perhaps the developers need to add another "class" of fruit to identify, such as oranges. Meanwhile, in a deployment scenario, a DNN trained to recognize healthy apples and bananas can answer "nothing" if there is a deviation from the prototypical fruit it has learned to recognize. In this sense, the DNN may act as an anomaly detection network -- aside from classifying apples and bananas, it can also, without further changes, signal when it sees something that deviates from the norm. As of today, there are no easy ways to train a standard DNN so that it can provide the functionality above.

Slashdot

Slashdot was originally created in September of 1997 by Rob "CmdrTaco" Malda. Today it is owned by Geeknet, Inc..

More About this Source Visit Slashdot