Facebook 10 Year Challenge: Intelligent Algorithms Aside, Are We The Product, Again?
Like several internet fads, the first major one amongst 2019 could be a “challenge” that's not truly challenging. And additionally, like several internet fads, it mostly acts as an excuse to post a photograph of oneself. This is fine, of course; that’s pretty much what social media is designed for already. But for its inherent self-concern, and a litany of other slightly more bizarre reasons, people have attempted to make the argument that it is secretly evil.
The 10-Year Challenge, or 2009 vs. 2019 Challenge, or the Glow Up Challenge, or the How Hard Did Aging Hit You Challenge, whatever you want to call it, is simple: You post a photo of yourself in 2009 next to a photograph of yourself in 2019. Caption it whatever you want. That’s it!
Unless you have been living under a rock, you would have surely seen the 10-year challenge that is populating our Facebook and Instagram timelines. Participating in the challenge means you need to share two of your photos, taken 10 years apart—one from the year 2009 and one from now, the year 2019. Within just the realms of it being a fun thing to do, these comparative images do usually show up a rather interesting contrast reflecting how most of us have changed, at least visually, over the 10-year period.
But as with most things Facebook these days considering all the data breaches and the secretive liberties with user data over time, it is perhaps only logical that there is a healthy sense of suspicion about this 10-year challenge. This surely can’t be an elaborate plot to tell us what we already knew—we are getting old, and ten years is a long time.
The Cambridge Analytica scandal from last year and the subsequent data breaches are still fresh in the minds of users, globally. On its part, Facebook has come out with a clarification that it has had nothing to do with the 10-year challenge trending on social networks such as Facebook, the Facebook-owned Instagram, and even Twitter, for that matter. “The 10-year challenge could be a user-generated meme that started on its own, without our involvement.
Its evidence of the fun people have on Facebook, and that’s it,” says the social network, in an official statement. It is important to weigh both sides—does Facebook really even need this data? And if yes, why? Or is alleging that Facebook is behind this is as ludicrous as saying something on the lines of how Google may be spying on us just because our Android phones have a microphone for voice calls?
Back on 30 April 2009, Facebook has said that as many as 15 billion photos had been uploaded by the users on the social network, already. And as many as 220 million new photos were being added every week. That’s 15 billion at some point in the year 2009. In the year 2013, users were uploading as many as 330 million photos a day. A day. We need to keep our eyes on the fact that smartphone penetration, Facebook usage and the willingness to upload photos online have all increased tremendously since then.
We are in 2019 now, and it is impossible to know exactly how many billion or trillion photos Facebook currently has—unless they tell us. Which is unlikely. But we digress. Back to the point, and along the way, image processing algorithms, powered by artificial intelligence (AI), have only become even smarter. Almost every company is using facial recognition in one way or the other. The Photos app on your Apple iPhone can do facial recognition. The Google Photos app on your Android phone understands and matches faces to people and contacts.
It’s everywhere. As with most things on the internet, all data is good data. The more data, the merrier. “#Facebooks 10 Year Challenge, the best way I have ever seen to train their AI in face recognition with your personal data,” says Thomas Tscherisich, SVP Internal Security at Deutsche Telekom, in a tweet.
All data is good, but why does Facebook need this?
Simple. All of this data could be mined to train facial recognition algorithms to better understand changes in facial structure, contours, skin color and ageing. Simply put, most people show significant differences in how they look if one is to consider a 10-year period. That helps AI learn better the fine art of age progression in humans. “Let's simply imagine that you just wanted to, say, train a facial recognition algorithm on age-related characteristics. You'd ideally desire a piece of broad and rigorous information set with millions of people's photos. It'd help if you knew they were taken a fixed number of year’s apart — say 10 years,” says Kate O’Neill, author of tech Humanist, in a tweet.