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Tuesday, June 28, 2016

Google says machine learning is the future. So I tried it myself | The Guardian



Photo: Alex Hern
"If deep learning will be as big as the internet, it’s time for everyone to start looking closely at it." inform Alex Hern, technology reporter for the Guardian. 

Google DeepMind’s artificial intelligence program, AlphaGo, used machine learning to defeat its human opponent, but that is just the beginning. 
Photograph: Ahn Young-joon/AP

The world is quietly being reshaped by machine learning. We no longer need to teach computers how to perform complex tasks like image recognition or text translation: instead, we build systems that let them learn how to do it themselves.

“It’s not magic,” says Greg Corrado, a senior research scientist at Google. “It’s just a tool. But it’s a really important tool.”

The most powerful form of machine learning being used today, called “deep learning”, builds a complex mathematical structure called a neural network based on vast quantities of data. Designed to be analogous to how a human brain works, neural networks themselves were first described in the 1930s. But it’s only in the last three or four years that computers have become powerful enough to use them effectively.

Corrado says he thinks it is as big a change for tech as the internet was. “Before internet technologies, if you worked in computer science, networking was some weird thing that weirdos did. And now everyone, regardless of whether they’re an engineer or a software developer or a product designer or a CEO understands how internet connectivity shapes their product, shapes the market, what they could possibly build.”

He says that same kind of transformation is going to happen with machine learning. “It ends up being something that everybody can do a little of. They don’t have to do the detailed things, but they need to understand ‘well, wait a minute, maybe we could do this if we had data to learn from.’”

Google’s own implementation of the idea, an open-source software suite called TensorFlow, was built from the ground up to be useable by both the researchers at the company attempting to understand the powerful models they create, as well as the engineers who are already taking them, bottling them up, and using them to categorise photos or let people search with their voice.

Machine learning is still a complex beast. Away from simplified playgrounds, there’s not much you can do with neural networks yourself unless you have a strong background in coding. But I wanted to put Conrado’s claims to the test: if machine learning will be something “everybody can do a little of” in the future, how close is it to that today?

One of the nice things about the machine learning community right now is how open it is to sharing ideas and research. When Google made TensorFlow open to anyone to use, it wrote: “By sharing what we believe to be one of the best machine learning toolboxes in the world, we hope to create an open standard for exchanging research ideas and putting machine learning in products”. And it’s not alone in that: every major machine learning implementation is available for free to use and modify, meaning it’s possible to set up a simple machine intelligence with nothing more than a laptop and a web connection.

Which is what I did.

Following the lead of writer and technologist Robin Sloan, I trained a simple neural network on 119mb of Guardian leader columns. It wasn’t easy. Even with a detailed readme, it took me a few hours to set up a computer to the point where it could start learning from the corpus of text. And once it reached that point, I realised I had vastly underrated the amount of time it takes for a machine to learn. After running the training software for 30 minutes, and getting around 1% of the way through, I realised I would need a much faster computer.
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Source: The Guardian


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