Let’s start this post the same way I start my day — by looking at Facebook.
Facebook made $40 Billion dollars in revenue in 2017, solely from advertising to pure schmucks like you. The mantra among the more technically literate is that facebook doesn’t have users it has products that it sells to advertisers, it just so happens that all its products are homo-sapien smart-phone totting urbanites (just like you!)
The platforms meteoric rise from nobody to top-dog, is a dream-story in Silicon Valley, but underneath the veneer of wholesome innovation lies a darker secret, one that could be responsible for the polarization of entire communities, including our own. And it’s all because of their most valuable employee.
No, not Mark Zuckerberg, but the real genius behind the blue and white site. The one responsible for billions of ad revenue facebook generates yearly, and unsurprisingly she’s female.
Anna Lytica and Machine Learning
There’s probably thousands of post your facebook friends make everyday, but she decides which 3 to fit onto your smartphone screen first, and the next 3 and so forth. From the millions of videos shared every hour, she painstakingly picks the few you’d see in your timeline, she decides which ads to show you, and which advertisers to sell you too, underneath the hood in the giant ad behemoth, she lies working all day, everyday.
She isn’t a person, ‘she’ is an algorithm, a complex program that does billions of calculations a second, and for this post we’ll give her the name… Anna Lytica.
Facebook doesn’t talk about her much, she is after all a trade secret (sort of), but what she does and how she does it, might be as much a mystery to us, as it is to Mr. Zuckerberg. Machine Learning algorithms are complex things, we know how to build them, and train them, but how they actually work is sometimes beyond our understanding.
Google can train Alpha-Go to play a game, but how it makes decisions is unknown to Google and even itself — it just IS a Go player.And it is really sad, when we watch these AI algorithms make amazing discoveries, but are unable to explain their rationale to us mere humans. It’s the reason why Watson, IBMs big AI algorithm, hasn’t taken off in healthcare, there’s no point recommending a treatment for cancer, if the algorithm can’t explain why it chose the treatment in the first place.
This is hard to grasp, but AI isn’t just a ‘very powerful’ program, AI is something else entirely. We don’t even use traditional words like write or build to refer to the process of creating them (like we do regular programs), instead we use the word train.
We train an algorithm to play Go, to drive, or to treat cancer. We do this the same way we breed dogs, we pick specimens with the traits we want, and breed them till we end up with a something that matches our desires. How a dog works, and what a dog thinks is irrelevant. If we want them big, we simply breed the biggest specimens, the process is focused entirely on outcome.
Similarly, how the algorithm behaves is driven by what it was trained to do. How it works is irrelevant, all that matters is outcome. Can it play Go, can it drive, can it answer jeopardy? If you want to understand an algorithm you need to know what it was trained to do.
Anna Lytica, was trained to keep you browsing Facebook, after all the companies other endeavors like internet.org, and instant articles were built with the same intention. And while good ol’ Mark stated that he’s tweaking Anna to reduce the time people spend on Facebook, this is something new, an exception to the years Facebook tweaked her to keep you on their site.
After all the average monthly user spends 27 minutes per day in the app, and if you go by daily users, they spend about 41 minutes per day on Facebook. If that’s the end-result of tweaking Anna to ensure we spend less time on Facebook — God help us all!
And while it’s difficult to understand how Anna works, its very easy to guess how she’ll behave. If the end result of Anna’s training is to keep you browsing Facebook, then human psychology reveals a simple trait all humans share — confirmation bias.