Google researchers and Stanford scientists have discovered that if you show a large enough computing system millions of images from random YouTube videos for three days, the computer will teach itself to recognize … cats.
That might sound inconsequential at best and downright ridiculous at worst—but in fact, it is very important.
The research shows that if a computer is big enough, and programmed correctly, it can learn to make sense of random, unlabeled data, in just days—without any help from humans.
And this research is especially important to Google, because it has major implications for web search.
As of now, Google is very good at searching labeled data, such as cat images that are labeled as cat images. But imagine the improvement to search if Google’s computers were able to find cat images that aren’t labeled cat images, for example. Or even if Google could “see” a cat in the real world and recognize what it is, and then tell you information about it. After all, there is much more unstructured data floating around our world than labeled data from a computer’s perspective.
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It is possible, of course, to teach a computer what a cat looks like. You can show the computer thousands of pictures of cats so that it learns what constitutes a cat’s face—pointy ears, almond eyes, furry face.
But then if you want the computer to learn what a crocodile looks like, you’d have to show it thousands of pictures of crocodiles, too.
It is a time-consuming and expensive process.
For this paper, the researchers worked with the idea of “deep learning,” which involves software that loosely simulates the learning process of the human brain, building a “neural” network with 1 billion connections.