v a l l e j o n o c t u r n o

Wicked Algorithms

From Technology Review, some elucidation on the wicked Facebook algorithms in clear English:

How does Facebook’s algorithm work?

Colloquially, we use the term “Facebook’s algorithm” as though there’s only one. In fact, Facebook decides how to target ads and rank content based on hundreds, perhaps thousands, of algorithms. Some of those algorithms tease out a user’s preferences and boost that kind of content up the user’s news feed. Others are for detecting specific types of bad content, like nudity, spam, or clickbait headlines, and deleting or pushing them down the feed.

All of these algorithms are known as machine-learning algorithms. As I wrote earlier this year:

Unlike traditional algorithms, which are hard-coded by engineers, machine-learning algorithms “train” on input data to learn the correlations within it. The trained algorithm, known as a machine-learning model, can then automate future decisions. An algorithm trained on ad click data, for example, might learn that women click on ads for yoga leggings more often than men. The resultant model will then serve more of those ads to women.

And because of Facebook’s enormous amounts of user data, it can

develop models that learned to infer the existence not only of broad categories like “women” and “men,” but of very fine-grained categories like “women between 25 and 34 who liked Facebook pages related to yoga,” and [target] ads to them. The finer-grained the targeting, the better the chance of a click, which would give advertisers more bang for their buck.

The same principles apply for ranking content in news feed:

Just as algorithms [can] be trained to predict who would click what ad, they [can] also be trained to predict who would like or share what post, and then give those posts more prominence. If the model determined that a person really liked dogs, for instance, friends’ posts about dogs would appear higher up on that user’s news feed.

Before Facebook began using machine-learning algorithms, teams used design tactics to increase engagement. They’d experiment with things like the color of a button or the frequency of notifications to keep users coming back to the platform. But machine-learning algorithms create a much more powerful feedback loop. Not only can they personalize what each user sees, they will also continue to evolve with a user’s shifting preferences, perpetually showing each person what will keep them most engaged.

My Walk in Old Ellicott City on Monday

For this year's cards that we send out during the Holiday I went out in Old Ellicott City for my first search for images. Here are some things I saw and captured.

Artificial People Online

From New York Magazine, an article that lays out the difficulties of dealing with the real on the Internet.

In late November, the Justice Department unsealed indictments against eight people accused of fleecing advertisers of $36 million in two of the largest digital ad-fraud operations ever uncovered. Digital advertisers tend to want two things: people to look at their ads and “premium” websites — i.e., established and legitimate publications — on which to host them.
The two schemes at issue in the case, dubbed Methbot and 3ve by the security researchers who found them, faked both. Hucksters infected 1.7 million computers with malware that remotely directed traffic to “spoofed” websites — “empty websites designed for bot traffic” that served up a video ad purchased from one of the internet’s vast programmatic ad-exchanges, but that were designed, according to the indictments, “to fool advertisers into thinking that an impression of their ad was served on a premium publisher site,” like that of Vogue or The Economist. Views, meanwhile, were faked by malware-infected computers with marvelously sophisticated techniques to imitate humans: bots “faked clicks, mouse movements, and social network login information to masquerade as engaged human consumers.” Some were sent to browse the internet to gather tracking cookies from other websites, just as a human visitor would have done through regular behavior. Fake people with fake cookies and fake social-media accounts, fake-moving their fake cursors, fake-clicking on fake websites — the fraudsters had essentially created a simulacrum of the internet, where the only real things were the ads.

How much of the internet is fake? Studies generally suggest that, year after year, less than 60 percent of web traffic is human; some years, according to some researchers, a healthy majority of it is bot. For a period of time in 2013, the Times reported this year, a full half of YouTube traffic was “bots masquerading as people,” a portion so high that employees feared an inflection point after which YouTube’s systems for detecting fraudulent traffic would begin to regard bot traffic as real and human traffic as fake. They called this hypothetical event “the Inversion.”

In the future, when I look back from the high-tech gamer jail in which President PewDiePie will have imprisoned me, I will remember 2018 as the year the internet passed the Inversion, not in some strict numerical sense, since bots already outnumber humans online more years than not, but in the perceptual sense. The internet has always played host in its dark corners to schools of catfish and embassies of Nigerian princes, but that darkness now pervades its every aspect: Everything that once seemed definitively and unquestionably real now seems slightly fake; everything that once seemed slightly fake now has the power and presence of the real. The “fakeness” of the post-Inversion internet is less a calculable falsehood and more a particular quality of experience — the uncanny sense that what you encounter online is not “real” but is also undeniably not “fake,” and indeed may be both at once, or in succession, as you turn it over in your head.

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