Facebook is turning to machine-learning technologies to amplify the impact of human fact-checkers reviewing hoax news articles, as part of the company’s efforts to combat the spread of misinformation on its platform.

The social network announced Thursday it was starting to use automated systems to identify duplicates of fake news articles previously flagged by third-party reviewers.

“We know it doesn’t make sense to have fact-checkers review all content given the scale,” Tessa Lyons, a product manager at Facebook, said at a press briefing in Brussels, “which is why need to use technology to figure out how to scale the impact.”

The announcement follows months of pressure from lawmakers in the US and Europe over the company’s privacy policies, and whether state actors had managed to use the social network to influence the 2016 US presidential election and the UK’s Brexit vote.

In a blog post accompanying the announcement, Lyons said “more than a billion pieces of content” are posted to Facebook daily, and that one false story is often copied by different outlets across multiple links in a bid for financial gain.

She cited a fact-checker in France, who identified a story promoting a bunk scientific claim. Facebook said its software was able to use the analysis of that article to discover and demote more than 20 web domains and 1,400 links promoting the same false information.The machine-learning technology is also being deployed to identify and demote the visibility of pages that are likely to spread financially-motivated hoaxes, typically set up by businesses, political parties or other organizations.

Ahead of European Union elections next spring, the European Commission, the bloc’s executive body, in April called on web platforms and social media companies to do more to curtail disinformation. EU officials warned they could impose regulation should they fail to do so.

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Robots are coming to steal your reviews.

Researchers at the University of Chicago have trained a neural network, or artificial intelligence system, to write fake reviews on Yelp – and it’s pretty hard to tell them apart from a human review.

Their study, which will be presented at the ACM Conference on Computer and Communications Security in October, aimed to highlight how easily these systems can mimic human reviews – and how damaging they can become if they’re not monitored properly.

Since many small businesses rely on online reviews to help grow and sustain their reputation, a future where someone – like a rival or angry customer — could hypothetically spam their page with negative reviews written by a machine is pretty worrying.

And, according to the research team, the threat goes far beyond a bunch of fake reviews on Yelp.

“In general, the threat is bigger,” Ben Y. Zhao, one of the authors of the study, told Business Insider. “I think the threat towards society at large and really disillusioned users and to shake our belief in what is real and what is not, I think that’s going to be even more fundamental.”

To test how believable these reviews came across, the researchers recruited 40 volunteers and had AI generate five fake reviews for 40 actual restaurants. The volunteers were asked how useful they thought the review was and whether or not they thought it was fake. The AI reviews ranked as “effectively indistinguishable” from real reviews, according to the study. Further, the fake reviews were given a 3.15 “usefulness” rating, compared to a 3.28 rating for human reviews.

Below are four five-star reviews written for the same NYC restaurant, can you figure out which ones were written by a human?

1. “I love this place. I have been going here for years and it is a great place to hang out with friends and family. I love the food and service. I have never had a bad experience when I am there.”
2. “Easily my favorite Italian restaurant. I love the taster menu, everything is amazing on it. I suggest the carpaccio and the asparagus. Sadly it has become more widely known and becoming difficult to get a reservation for prime times.
3. “I had the grilled veggie burger with fries!!!! Ohhhh and taste. Omgggg! Very flavorful! It was so delicious that I didn’t spell it!!”
4. “My family and I are huge fans of this place. The staff is super nice and the food is great. The chicken is very good and the garlic sauce is perfect. Ice cream topped with fruit is delicious too. Highly recommended!”

Number two is the only review written by a human; numbers one, three and four were all generated by the AI.

“It is going to progress to greater attacks, where entire articles written on a blog may be completely autonomously generated along some theme by a robot,” said Zhao. “and then you really have to think about where does information come from, how can you verify.”

The researchers plan to use the study to start developing technology that can detect and get rid of AI-generated text.

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Facebook is doubling down on its war on fake news.

The social networking titan said on Thursday in an updated blog post that it would expand a program to offer related articles on a trending topic that offer fact-check articles and other perspectives. The company added it would use “updated machine learning” to detect more potential hoaxes and send them to third-party fact checkers.

In April, Facebook introduced the related articles program, which offered links to other stories before you even read the shared trending topic.

The company has drawn flak for its role in perpetuating hoaxes and its influence on the presidential election, and has in recent months worked to combat the rise of fake news.

Facebook said the changes would affect how the News Feed worked, and that you would still get stories that were relevant to you.

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Imagine that tomorrow, some smart kid invented a technology that let people or physical goods pass through walls, and posted instructions for how to build it cheaply from common household materials. How would the world change?

Lots of industries would probably become more productive. Being able to walk through walls instead of being forced to use doors would make it easier to navigate offices, move goods in and out of warehouses and accomplish any number of mundane tasks. That would give the economy a boost. But the negative might well outweigh the positive. Keeping valuables under lock and key would no longer work. Anyone could break into any warehouse, bank vault or house with relative ease. Most of the methods we use to keep private property secure rely on walls in some ways, and these would be instantly made ineffective. Thieves and home invaders would run rampant until society could implement alternative ways of keeping out intruders. The result might be an economic crash and social chaos.

This demonstrates a general principle — technological innovations are not always good for humanity, at least in the short term. Technology can create negative externalities — a economics term for harm caused to third parties. When those externalities outweigh the usefulness of the technology itself, invention actually makes the world worse instead of better — at least for a while.

Machine learning, especially a variety known as deep learning, is arguably the hottestnew technology on the planet. It gives computers the ability to do many tasks that only humans were able to perform — recognize images, drive cars, pick stocks and lots more. That has made some people worried that machine learning will make humans obsolete in the workplace. That’s possible, but there’s a potentially bigger danger from machine learning that so far isn’t getting the attention it deserves. When machines can learn, they can be taught to lie.

Human beings can doctor images such as photographs, but it’s laborious and difficult. And faking voices and video is beyond our capability. But soon, thanks to machine learning, it will probably be possible to easily and quickly create realistic forgeries of someone’s face and make it seem as if they are speaking in their own voice. Already, lip-synching technology can literally put words in a person’s mouth. This is just the tip of the iceberg — soon, 12-year-olds in their bedrooms will be able to create photorealistic, perfect-sounding fakes of politicians, business leaders, relatives and friends saying anything imaginable.

This lends itself to some pretty obvious abuses. Political hoaxes — so-called “fake news” — will spread like wildfire. The hoaxes will be discovered in short order — no digital technology is so good that other digital technology can’t detect the phony — but not before it puts poisonous ideas into the minds of people primed to believe them. Imagine perfect-looking fake video of presidential candidates spouting racial slurs, or admitting to criminal acts.

But that’s just the beginning. Imagine the potential for stock manipulation. Suppose someone releases a sham video of Tesla Inc. Chief Executive Officer Elon Musk admitting in private that Tesla’s cars are unsafe. The video would be passed around the internet, and Tesla stock would crash. The stock would recover a short while later, once the forgery was revealed — but not before the manipulators had made their profits by short-selling Tesla shares.

This is far from the most extreme scenario. Imagine a prankster creating a realistic fake video of President Donald Trump declaring that an attack on North Korean nuclear facilities was imminent, then putting the video where the North Koreans can see it. What are the chances that North Korea’s leadership would realize that it was a fraud before they were forced to decide whether to start a war?

Those who view these extreme scenarios as alarmist will rightfully point out that no fake will ever be undetectable. The same machine learning technology that creates forgeries will be used to detect them. But that doesn’t mean we’re safe from the brave new world of ubiquitous fakes. Once forgeries get so good that humans can’t detect them, our trust in the veracity of our eyes and ears will forever vanish. Instead of trusting our own perceptions, we will be forced to place our trust in the algorithms used for fraud detection and verification. We evolved to trust our senses; switching to trust in machine intelligence instead will be a big jump for most people.

That could be bad news for the economy. Webs of trade and commerce rely on trust and communication. If machine learning releases an infinite blizzard of illusions into the public sphere — if the walls that evolution built to separate reality from fantasy break down — aggregate social trust could decrease, hurting global prosperity in the process.

For this reason, the government should probably take steps to penalize digital forgery pretty harshly. Unfortunately, the current administration seems unlikely to take that step, thanks to its love of partisan news. And governments like Russia’s seem even less likely to curb the practice. Ultimately, the combination of bad government with powerful new technology represents a much bigger danger to human society than technology alone.

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