A neural network created by connecting 16,000 computer processors appears to support biologists' theories on how the human brain identifies objects. Hint: It's all about the cats.
Google scientists working in the company's secretive X Labs have made great strides in using computers to simulate the human brain.
Best known for inventing self-driving cars and augmented-reality eyewear, the lab created a neural network for machine learning by connecting 16,000 computer processors and then unleashed it on the Internet. Along the way, the network taught itself to recognize cats.
While the act of finding cats on the Internet doesn't sound all that challenging, the network's performance exceeded researchers' expectations, doubling its accuracy rate in identifying objects from a list of 20,000 items, according to a New York Times report.
To find the cats, the team fed the network thumbnail images chosen at random from more than 10 billion YouTube videos. The results appeared to support biologists' theories that suggest that neurons in the brain are trained to identify specific objects.
"We never told it during the training, 'This is a cat,'" Google fellow Jeff Dean told the newspaper. "It basically invented the concept of a cat."
Falling computing costs has led to significant advancements in areas of computer science such as machine vision, speech recognition, and language translation, The Times noted.
Machine learning is useful for improving translation algorithms and semantic understanding and a favorite topic of Google co-founders Sergey Brin and Larry Page, according to Google.