SCIENCE: AI System Learns to Recognize Faces and Felines

AI System Learns to Recognize Faces and Felines










"The key to this research is that we didn't
explicitly pick out particular images to train it on, such as images of a
cat," said Google spokesperson Jason Friedenfelds. "Instead, we fed it
10 million random images, and it automatically learned certain recurring
patterns, including roughly what a cat's face looks like. That's
because, presumably, cats are relatively common on YouTube."

A neural network built over the years by researchers from Stanford University and Google (Nasdaq: GOOG) has managed to teach itself to recognize faces and cats.

The network consists of 16,000 processors on a cluster of 1,000
computers. After being exposed to 10 million images downloaded from the
Internet, the network software learned to recognize human and cat faces,
as well as human bodies, according to the researchers.

"The cool thing about this particular software was that no one was
telling it that there were faces in these pictures," Carl Howe, vice
president, data sciences research at the Yankee Group,
told TechNewsWorld. "The software is figuring out if there are faces or
not based on the fact that a lot of faces were in the data, and the
neurons learned to recognize these patterns."

This self-learning network "should also be able to read expressions
and determine the breed of cat if given enough visual information," Rob
Enderle, principal analyst at the Enderle Group, told TechNewsWorld
The images selected were random, each picked from one of 10 million
videos on YouTube. Those videos were also selected at random.

"The key to this research is that we didn't explicitly pick out
particular images to train it on, such as images of a cat," Google
spokesperson Jason Friedenfelds told TechNewsWorld. "Instead, we fed it
10 million random images, and it automatically learned certain recurring
patterns, including roughly what a cat's face looks like. That's
because, presumably, cats are relatively common on YouTube."

The team trained a 9-layered locally connected sparse autoencoder
with pooling and local contrast normalization on the 10 million images
in the network's dataset. A sparse autoencoder
is a learning algorithm. The basic version isn't as good as the best
hand-engineered features, but the features it can learn are more useful
for a range of problems. More sophisticated versions of the algorithm
are as good as, or better than, features coded by hand.

The network was trained using model parallelism and asynchronous Stochastic Gradient Descent (SGD).



Kitty Spotter



The researchers concluded that it's possible to train a face detector
without having to label images as containing a face or not. Previously,
building a face detector required images labeled as containing faces,
often with a bounding box around the face. This made it difficult to
solve problems where labeled data was scant.

Further, the neural network will recognize faces even if the images viewed are slanted or made larger or smaller than the norm.

The researchers trained the network to recognize 20,000 object
categories from ImageNet with nearly 16 percent accuracy. This is 70
percent better than results from previous state-of-the-art technology.

ImageNet is an image database for researchers and educators.

The neural network did more than recognize cats; it reportedly
assembled a digital image of a cat by combining together features from
various images of cats it had stored.



Faster and Smarter



Previous learning algorithms have only succeeded in learning low-level
features such as edge or blob detectors, the researchers said. That's
possibly because training deep-learning algorithms to yield good results
is time-consuming. That might be why high-level detection is difficult,
the researchers surmised.

"It appears that this research project was able to build a machine
that can 'see' objects in images despite never having been told what the
objects are or what they should look like," the Yankee Group's Howe
said. "What's more remarkable is that the system learned to recognize a
wide variety of objects and recognize the total sum of these objects
with 16 percent accuracy.."

The neural network's accuracy may improve over time because, "as more
information is collected and sub-categories result, you'll have the
ability to make more detailed determinations," Enderle said. "This is a
foundation step to building a computer that truly can be taught rather
than be programmed."

Such networks could be useful in security and in tracking criminals, Enderle suggested.

However, "both kittens and babies can achieve similar results with
far fewer resources, and those entities can be created with far less
skilled labor," the Yankee Group's Howe pointed out.

Once again......ThanX naseem!