Facebook has a big problem with machine learning

Facebook CEO and chairman Mark Zuckerberg speaks during a session of the APEC CEO Summit in Lima on November 19, 2016. (RODRIGO BUENDIA/AFP/Getty Images)

Facebook’s ability to filter out fake news has been in the spotlight since the US presidential elections two weeks ago, with the social media giant at different times denying there is a problem and promising to improve its curation.

Now a prominent technology investor, advisor and entrepreneur has blasted Facebook for its claims that distinguishing between fake and real news is difficult — and said that this would lead to engineering talent staying away from the tech giant.

Elad Gil — who is a former Google and Twitter staffer, and investor and advisor to companies like AirBnB, Square, Stripe and Pinterest — wrote on his blog that fake news detection is similar to commonly used technologies like spam filters, social networking bots and pornography detection.

“This suggests one of two things: (1) Facebook really sucks at machine learning or (2) Facebook does not want to address the problem,” Gil said.

Gil, who is also the founder of biotech startup Color Genomics, said that considering the technologies already at Facebook’s disposal, he cannot accept chief executive Mark Zuckerberg’s claims that sorting out fake from real is a complex problem with a complex solution.

“This ‘grey area’ argument is made all the time. Yet machine learning classifiers work incredibly well for porn and other areas that have lots of grey. Similarly, getting rid of the 80% easy to spot, most egregious stuff is a good starting point. This argument strikes me as a red herring.”

He cited the case of one group of university students that solved the issue in a hackathon days after the election as a demonstration of how easy it would be to block out fake news.

“It is possible these Princeton students a set of once-in-a-generation geniuses. Or, perhaps, fake news is actually tractable as a problem using existing techniques Facebook already has in-house.”

As for a lack of motivation to fix the problem, according to Gil, Facebook’s consultation earlier this year with Republican party officials after accusations of left-wing bias could be a symptom of that.

“Fake news is not a partisan issue. It is about ensuring that people are helped to understand what is real and what are lies. A lack of willingness to tackle the issue of fake news is a willingness to accept a lack of truth in our society at mass scale.”

With Facebook unable or unwilling to fix a straightforward problem, Gil suggested that from now on engineering talent might avoid working for the social media company.

“Great engineers want to work with other great engineers. If Facebook lacks the talent to address the fake news problem, do you really want to join an organisation so poor at machine learning?

“Alternatively, if Facebook simply lacks the will to address this issue, it might be something worth taking into account as well. A number of talented engineers are also immigrants – a group much maligned in fake news posts.”

Gil then went on to suggest machine learning and AI engineers consider working for rivals such as Google, Uber, Tesla, Stripe, Amazon or one of bunch of up-and-coming startups specialising in the area.

“There are lots of deep learning, artificial intelligence, and machine learning companies that have been funded recently. There are lots of cool things for you to work on instead.”

Business Insider Emails & Alerts

Site highlights each day to your inbox.

Follow Business Insider Australia on Facebook, Twitter, LinkedIn, and Instagram.