AI-Human Partnerships Tackle “Fake News”

Facebook, Google, and smaller tech companies are now using machine learning to flag misinformation—but automated systems aren’t reliable enough on their own

4 min read
Photo-illustration: iStockphoto
Photo-illustration: iStockphoto

During the 2016 U.S. presidential election, inaccurate and misleading articles burned through social networks. Since then, tech companies—from behemoths like Facebook and Google to scrappy startups—have built tools to fight misinformation (including what many call “fake news,” though that term is highly politicized). Most companies have turned to artificial intelligence (AI) in hopes that fast and automated computer systems can deal with a problem that’s seemingly as big as the Internet.

“They’re all using AI because they need to scale,” says Claire Wardle, who leads the misinformation-fighting project First Draft, based in Harvard University’s John F. Kennedy School of Government. AI can speed up time-consuming steps, she says, such as going through the vast amount of content published online every day and flagging material that might be false.

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds
DarkBlue1

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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