How algorithms shape our world - Kevin Slavin

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This is a photograph by the artist Michael Najjar, and it's real in the sense that he went there to Argentina to take the photo. But it's also a fiction. There's a lot of work that went into it after that, and what he's done is he's actually reshaped digitally all of the contours of the mountains to follow the vicissitudes of the Dow Jones index. So what you see, that precipice, that high precipice with the valley, is the 2008 financial crisis. The photo was made when we were deep in the valley over there. I don't know where we are now.

This is the Hang Seng Index, or Hong Kong, and similar topography. I wonder why. And this is art, right? This is metaphor. But I think the point is, is that this is metaphor with teeth. And it's with those teeth that I want to propose today that we rethink a little bit about the role of contemporary math—not just financial math, but math in general. That it's transition from being something that we sort of extract and derive from the world to something that actually starts to shape it, the world around us and the world inside us.

And it specifically algorithms, which are basically the math that computers use to decide stuff. They acquire the sensibility of truth because they repeat over and over again, and they kind of ossify and calcify, and they kind of become real. And I was thinking about this, of all places, on a transatlantic flight a couple years ago because I happen to be seated next to a Hungarian physicist about my age.

We were talking about what life was like during the Cold War for physicists in Hungary, and I said, “So what were you doing?” He said, “Well, we were mostly breaking stealth.” I said, “That's a good job. That's interesting. How does that work?” And so, to understand that, you have to understand a little bit about how stealth works. And so this is an oversimplification, but basically it's not like you can just pass a radar signal right through 156 tons of steel in the sky. It's not just going to disappear.

But if you can take this big massive thing and you could turn it into a million little things—something like a flock of birds—well then the radar that's looking for that has to be able to see every flock of birds in the sky. And if you're a radar, that's a really bad job. He said, “Yeah,” he said, “but that's if you're a radar. We didn’t use a radar; we built a black box that was looking for electrical signals, electronic communication. And whenever we saw a flock of birds that had electronic communication, we thought probably has something to do with the Americans.”

And I said, “Yeah, that's good. That's good. So you've effectively negated 60 years of aeronautical research. What's your act to? You know, like, what do you do when you grow up?” And he said, “Well, you know, financial services.” And I said, “Oh, because those have been in the news lately. How does that work?” He said, “Well, there's 2,000 physicists on Wall Street now, and I'm one of them.”

And I said, “Well, so what's the black box for Wall Street?” And he said, “Well, it's funny that you asked that because it's actually called black box trading, and it's also sometimes called algo trading, algorithmic trading.” And algorithmic trading involves, in part, because institutional traders have the same problems that the United States Air Force had, which is that they're moving these positions, whether it's Procter and Gamble or etc., or whatever. They're moving like a million shares of something through the market, and if they do that all at once, it's like playing poker and just going all-in right away, right? You just tip your hand.

And so they have to find a way, and they use algorithms to do this, to break up that big thing into a million little transactions. And the magic and the horror of that is, is that the same math that you use to break up the big thing into a million little things can be used to find a million little things and sew them back together and figure out what's actually happening in the market.

So if you need to have some image of what's happening in the stock market right now, what you can picture is a bunch of algorithms that are basically programmed to hide and a bunch of algorithms that are programmed to go find them and act. And all of that's great and it's fine, and that's 70% of the United States stock, where 70% of the operating system formerly known as your pension, your mortgage. And what could go wrong, right?

What could go wrong is, is that a year ago, 9% of the entire market just disappears in five minutes, and they called it the flash crash of 2:45. Right? All of a sudden, 9% just goes away, and nobody to this day can even agree on what happened because nobody ordered it, nobody asked for it, nobody had any control over what was actually happening. All they had was just a monitor in front of them that had the numbers on it and just a red button that said stop.

And that's the thing, right? Is that we're writing things, we're writing these things that we can no longer read, and it's we've rendered something kind of illegible and we've lost the sense of what's actually happening in this world that we've made. And we're starting to make our way; there's a company in Boston called Nanak's, and they use math and magic, and I don't know what, and they reach into all the market data, and they find actually sometimes some of these algorithms.

And when they find them, they pull them out and they pin them to the wall like butterflies. And they do what we've always done when confronted with huge amounts of data that we don't understand, which is that they give them a name and a story. So this is one that they found; they called the Knife, the Carnival, the Boston Shuffler, Twilight.

And the gag is that, of course, these aren't just running through the market right? You can find these kinds of things wherever you look once you learn how to look for them. Right? You can find it here, this book about flies that you may have been looking at on Amazon. You may have noticed it when its price started at 1.7 million dollars. It's out-of-print still. If you had bought it at 1.7, it would have been a bargain.

A few hours later, it had gone up to twenty-three point six million dollars plus shipping and handling. And the question is, nobody was buying or selling anything. What was happening? And you see this behavior on Amazon as surely as you see it on Wall Street. And when you see this kind of behavior, what you see is the evidence of algorithms in conflict, algorithms locked in loops with each other, without any human oversight, without any adult supervision to say, “Actually, 1.7 million is plenty. You stick with it.”

And as with Amazon, so it is with Netflix. And so Netflix has gone through several different algorithms over the years. They started with cinema, and they’ve tried a bunch of others—there's Dinosaur Planet, there's Gravity. They're using Pragmatic Chaos now. Pragmatic Chaos is like all of Netflix algorithms trying to do the same thing; it's trying to get a grasp on you, on the firmware inside the human skull so that it can recommend what movie you might want to watch next, which is a very, very difficult problem.

But the difficulty of the problem, and the fact that we don't really quite have it down, it doesn't take away from the effects. The Pragmatic Chaos has—bring out a chaos, like all Netflix algorithms determines in the end 60% of what movies end up being rented, right? So one piece of code with one idea about you is responsible for 60% of those movies. But what if you could rate those movies before they get made? Right? Wouldn't that be handy?

Well, so a few data scientists from the UK are in Hollywood and they have story algorithms and a company called Epic Oh Jokes, and you can run your script through there, and they can tell you quantifiably that that's a 30 million dollar movie or a 200 million dollar movie. And the thing is, is that this isn't Google, right? This isn't information, these aren't financial stats, this is culture.

And what you see here, or what you don't really see normally is, is that these are the physics of culture. And if these algorithms, like the algorithms on Wall Street, just crashed one day and went awry, how would we know? What would it look like? And they're in your house, right? They're in your house, right? These are two algorithms competing for your living room. These are two different cleaning robots that have very different ideas about what clean means.

And you can see it if you slow it down and attach lights to them, and they're sort of like secret architects in your bedroom. Yeah. And the idea that architecture itself is somehow subject to algorithmic optimization is not far-fetched; it's super real, and it's happening around you. You feel it most when you’re in a sealed metal box: a new style elevator. They're called destination control elevators. These are the ones where you have to press what floor you're going to go to before you get in the elevator, and it uses what's called a bin packing algorithm.

So none of this mishegoss of just letting everybody go into whatever car they want. Everybody wants to go to the tenth floor goes into car two, and everybody wants to go to the third floor goes into car five. And the problem with that is, is that people freak out, people panic. And you see why, right? You see why. It's because the elevator is missing some important instrumentation, like the buttons, right? Like the things that people use. All it has is just the number that moves up or down and that red button that says stop.

And this is what we're designing for. We're designing for this kind of machine dialect, all right? And how far can you take that? How far can you take it? You can take it really, really far. And so let me take it back to Wall Street, okay? Because the algorithms of Wall Street are dependent on one quality above all else, which is speed. And they operate on milliseconds and microseconds.

And just to give you a sense of what microseconds are, it takes you five hundred thousand microseconds just to click a mouse. But if you're a Wall Street algorithm and you're five microseconds behind, you're a loser. So if you were an algorithm, you'd look for an architect, like the one that I met in Frankfurt, who was hollowing out a skyscraper, throwing out all the furniture, all the infrastructure for human use, and just running steel on the floors to get ready for the stacks of servers to go in—all so that an algorithm could get close to the internet.

And you think of the Internet as this kind of distributed system, and of course it is, but it's distributed from places, right? In New York, this is where it's distributed from its carrier hotel, located on Hudson Street, and this is really where the wires come right up into the city. And the reality is, is that the further away you are from that, you're a few microseconds behind every time these guys down Wall Street—Marco Polo and Cherokee Nation—they're eight microseconds behind all these guys going into the empty buildings being hollowed out up around the carrier hotel, right? And that's going to keep happening; we're going to keep hollowing them out because you inch for inch and pound for pound and dollar for dollar, none of you could squeeze revenue out of that space like the Boston Shuffler could.

But if you zoom out, if you zoom out, you would see an 825-mile trench between New York City and Chicago has been built over the last few years by a company called Spread Networks. This is a fiber-optic cable that was laid between those two cities to just be able to traffic one signal 37 times faster than you can click a mouse, just for these algorithms, just for the Carnival and the Knife.

And when you think about this, that we're running through the United States with dynamite and rock saws so that an algorithm can close the deal three microseconds faster—all for a communications framework that no human will ever know—that's a kind of manifest destiny. And we'll always look for a new frontier. And fortunately we have our work cut out for us. This is just theoretical; this is some mathematicians at MIT.

And the truth is, I don't really understand a lot of what they're talking about. It involves light cones and quantum entanglement, and I don't really understand any of that. But I can understand this map. And what this map says is, is that if you're trying to make money on the markets, where the red dots are, that's where people are, where the cities are; you're going to have to put the servers where the blue dots are to do that most effectively.

And the thing that you might have noticed about those blue dots is that a lot of them are in the middle of the ocean. So that's what we'll do; we'll build bubbles or something, or platforms will actually part the water, right, to pull money out of the air because it's a bright future if you're an algorithm. And it's not the money that's so interesting, actually; it's what the money motivates, right? That we're actually terraforming the earth itself with this kind of algorithmic efficiency.

And in that light, you go back and you look at Michael Najjar's photographs, and you realize that they're not metaphor; they're prophecy. Right? They're prophecy for the kind of seismic terrestrial effects of the math that we're making. And the landscape was always made by this sort of weird, uneasy collaboration between nature and man. But now there's this kind of third co-evolutionary force: algorithms, the Boston Shuffler, the Carnival. And we will have to understand those as nature, and in a way, they are. Thank you.

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