![]() One didn’t cause the other both were caused by some externality the reporter had ignored. What really happened: The 49ers were playing that weekend, explaining both the jersey being worn, and an ad campaign simultaneously targeted at the SF Bay Area. Go back to that uploaded 49ers jersey photo. Some are pure correlation-means-causation confusion. Not that every such coincidence is false. The harsh truth is that Facebook doesn’t need to perform technical miracles to target you. Put another way: Just because I have a naked photo of you on the internet, doesn’t mean anyone would pay money to see it. But that’s a completely false equivalence advertisers don’t care about the vast majority of even your most personal data. As a result, we equate what we’d most hate to have revealed with what advertisers (or Facebook) would most like to know. We’re all the center of our own worlds, and assume our lives terribly important or interesting to outsiders. Herein lies one of the key misunderstandings about Facebook, which I like to call the Narcissistic Fallacy. And Facebook users are a very large herd. It was like pressing a field of livestock into the sausage grinder, and getting out one hot dog as a result. On the order of single-digit percentages of Facebook posts resulted in any sort of reading from the targeting machine. Before we even got to the performance side of things (and we’ll cover that shortly), we were instantly struck by how small a fraction of Facebook content even triggered interest from the targeting machine. Code-named 'Project Chorizo,' it involved pushing every piece of Facebook user data then available-posts, link shares, check-ins-into the targeting grinder and seeing if it improved ads performance. We did just such a test in my first year at Facebook. You’re watching the video of the one Facebook user who experienced some improbable event, and ignoring the millions of users who had no such odd coincidence. That could change as smartphones get more powerful, and mobile developers more clever at running real computation in situ, but Facebook's targeting engine won't be running on your phone anytime soon. Targeting a specific type of phone would ease that burden somewhat, but any significant scale presents an extraordinary challenge.įurthermore, as in our naive approach above, this would be eminently noticeable as a performance degradation on your phone, since the background inference process would soon eat all your phone's CPU and battery, something you could easily check via the device's monitoring tools. You could maybe hack around the problem by limiting the keyword list, or tightening the mapping from spoken word to targeting keyword to reduce the search space (only the literal word 'golf' instead of 'Tiger Woods'), but it's still daunting to do on every smartphone in existence, from slow, older phones to fast flagships like the iPhone X. That means the speech-to-text translation code could only run on your phone itself, a taxing demand even for the beefy cloud servers that usually handle those tasks. Because it has no specific trigger word for Facebook, your phone would need to listen for every targetable keyword. But unlike the Amazon Echo, which listens for just one of four trigger words, millions or perhaps billions of words and phrases could land you in a Facebook targeting segment.įor example, saying 'golf,' 'Tiger Woods,' 'The Masters,' or 'Augusta National Golf Course' all should land you in the 'Golf' targeting segment, and your phone would need to detect each and every one. The Facebook targeting system had something like a million targetable keywords when I left, and it's likely held steady or increased slightly. ![]() The Echo functions merely as a microphone, speaker, and weak computer that does a small voice-recognition task well.Ĭould the Facebook app do the same, listening only for specific keywords that trigger ads? Data or a request for more details are then beamed back, and your conversation with 'Alexa' continues. Once it detects that trigger word, it's also just smart enough to record the command that follows it, and send it to the Amazon mothership, where the real speech-to-text translation and natural language processing work happens. ![]() The Echo has just enough hardware to detect a very small set of 'trigger' words, which start it listening. The Amazon Echo voice-controlled personal assistant (and its Google equivalent, Google Home), put a slightly Orwellian-seeming listening device in many American homes. Of course, there's a smarter way to do it. ![]()
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