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Look Out, Wiki-Geeks. Now Google Trains AI To Write Wikipedia Articles

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A team within Google Brain – the web giant’s crack machine-learning research lab – has taught software to generate Wikipedia-style articles by summarizing information on web pages… to varying degrees of success.

As we all know, the internet is a never ending pile of articles, social media posts, memes, joy, hate, and blogs. It’s impossible to read and keep up with everything.

Using AI to tell pictures of dogs and cats apart is cute and all, but if such computers could condense information down into useful snippets, that would be really be handy. It’s not easy, though.

A paper, out last month and just accepted for this year’s International Conference on Learning Representations (ICLR) in April, describes just how difficult text summarization really is.




A few companies have had a crack at it. Salesforce trained a recurrent neural network with reinforcement learning to take information and retell it in a nutshell, and the results weren’t bad.

However, the computer-generated sentences are simple and short; they lacked the creative flair and rhythm of text written by humans. Google Brain’s latest effort is slightly better: the sentences are longer and seem more natural.

Here’s an example for the topic: Wings over Kansas, an aviation website for pilots and hobbyists.

The paragraph on the left is a computer-generated summary of the organization, and the one on the right is taken from the Wikipedia page on the subject.

The model works by taking the top ten web pages of a given subject – excluding the Wikipedia entry – or scraping information from the links in the references section of a Wikipedia article.

Most of the selected pages are used for training, and a few are kept back to develop and test the system.

Also, since it relies on the popularity of the first ten websites on the internet for any particular topic, if those sites aren’t particularly credible, the resulting handiwork probably won’t be very accurate either.

You can’t trust everything you read online, of course.

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Pass it on: Popular Science

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