Google has announced another big push into artificial intelligence, unveiling a new approach to machine learning where neural networks are used to build better neural networks – essentially teaching AI to teach itself.
These artificial neural networks are designed to mimic the way the brain learns, and Google says its new technology, called AutoML, can develop networks that are more powerful, efficient, and easy to use.
Google CEO Sundar Pichai showed off AutoML on stage at Google I/O 2017 this week – the annual developer conference that Google throws for app coders and hardware makers to reveal where its products are heading next.
“The way it works is we take a set of candidate neural nets, think of these as little baby neural nets, and we actually use a neural net to iterate through them until we arrive at the best neural net,” explains Pichai.
That process is called reinforcement learning, where computers can link trial and error with some kind of reward, just like teaching a dog new tricks.
It takes a massive amount of computational power to do, but Google’s hardware is now getting to the stage where one neural net can analyse another.
Neural nets usually take an expert team of scientists and engineers a significant amount of time to put together, but thanks to AutoML, almost anyone will be able to build AI systems to tackle whatever tasks they like.
“We hope AutoML will take an ability that a few PhDs have today and will make it possible in three to five years for hundreds of thousands of developers to design new neural nets for their particular needs,” Pichai writes in a blog post