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 for Hong Kong. And similar topography. I wonder why.
这是一张由艺术家 迈克尔·纳贾尔拍摄的照片, 这是真实的, 他在阿根廷 拍摄的这张照片。 但其中也有虚构的成分。在拍摄后还做了许多工作。 他所做的是, 他实际上数字化地重塑了 所有山峰的轮廓 以遵循道琼斯指数的变化。 因此各位所看到的, 这悬崖,深沟险壑, 是2008年的金融危机。 这张照片是在我们 陷入深谷时制作的。 我不知道我们现在在哪儿。 这是为香港恒生指数 制作的。 类似的形状。 我不知道为什么。
And this is art. This is metaphor. But I think the point 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 its transition from being something that we 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's 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 ossify and calcify, and they become real.
这是艺术。这是隐喻。 但我认为这是个 带着牙齿会咬人的隐喻。 带着这些齿状的线条,今天我建议 我们重新思考一下 当代数学的角色 -- 不仅是金融数学,还有一般数学。 它由 我们从世界中提炼出的某种事物 转变为事实上开始塑造世界的事物 -- 我们周围的世界和我们内心的世界。 特别是算法, 它基本上是 计算机用于决策的数学。 它们具有了真理的敏感性, 因为它们会不断地重复。 它们固化下来, 变得真实。
And I was thinking about this, of all places, on a transatlantic flight a couple of years ago, because I happened to be seated next to a Hungarian physicist about my age and we were talking about what life was like during the Cold War for physicists in Hungary. And I said, "So what were you doing?"
我随时随地都在思考这些, 数年前在一次跨越大西洋的航班上, 因为我恰好坐在一名 与我年纪相仿的匈牙利物理学家旁边, 我们谈论 冷战期间在匈牙利的 物理学家生活是什么样的。 我说道,“你们都做些什么?”
And he said, "Well we were mostly breaking stealth."
他回答道,“嗯,我们主要是在破解隐形飞机。”
And I said, "That's a good job. That's interesting. How does that work?" And to understand that, you have to understand a little bit about how stealth works. And so -- this is an over-simplification -- 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.
我说,“不错。很有趣。 怎么做呢?” 要理解这个, 要先对隐形飞机如何工作有点了解。 因此 -- 有点过于简化 -- 但基本上,不是 仅仅让156吨的钢铁 在天空中穿过雷达信号就完事了。 它不会就这么消失了。 但如果能把这巨大的东西 变成 上百万个小东西 -- 有点像一群鸟 -- 那么寻找目标的雷达 能看到天空中的 每个鸟群。 如果你是雷达,这真是个糟糕的工作。
And he said, "Yeah." He said, "But that's if you're a radar. So 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.'"
他说道,“是的。”他说,“如果你是雷达的话。 所以我们不用雷达; 我们造了个黑盒子来探测电子信号, 电子通讯。 当我们看到有电子通讯的一群鸟时, 我们就认为这可能与美国有关。
And I said, "Yeah. That's good. So you've effectively negated 60 years of aeronautic research. What's your act two? What do you do when you grow up?" And he said, "Well, financial services." And I said, "Oh." Because those had been in the news lately. And I said, "How does that work?" And he said, "Well there's 2,000 physicists on Wall Street now, and I'm one of them." And I said, "What's the black box for Wall Street?"
我说,”是的。 这很不错。 你们有效地让60年的 航空研究无效了。 你的下一步是什么? 你长大后,你想要做什么?“ 他说, ”嗯,金融服务。“ 我说,”哦。“ 因为这已经在最近的新闻里了。 我说,”这工作怎么样?“ 他说,”嗯,现在有2000名物理学家在华尔街工作, 我是其中之一。“ 我说,”华尔街的黑盒子是什么?“
And he said, "It's funny you ask that, because it's actually called black box trading. And it's also sometimes called algo trading, algorithmic trading." And algorithmic trading evolved 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 Proctor & Gamble or Accenture, whatever -- they're moving a million shares of something through the market. And if they do that all at once, it's like playing poker and going all in right away. 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 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.
他说,”你问的这个很有趣, 因为这实际上被称为暗箱交易。 也被称为算法交易, 算法交易。“ 算法交易的演化某种程度上 是因为机构交易员碰到了 与美国空军一样的问题, 他们要移动这些点 -- 不管是宝洁还是埃森哲,不管是什么 -- 他们在市场上交易上百万的 某公司的股票。 如果他们一次移动全部, 有点像象玩扑克室,所有筹码全部下注。 你就露了底牌。 因此他们不得不找一个方法 -- 他们用算法来完成这项工作 -- 把巨大的交易 转化为上百万次小的交易。 其中的神奇和可怕之处是 你用于把庞然大物分解成 上百万份的数学方法 也可以用于 找到上百万个小东西, 重新拼接起来 并算出市场上到底发生了什么。
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 percent of the United States stock market, 70 percent of the operating system formerly known as your pension, your mortgage.
因此如果你需要一些 描绘了当前市场中的情景的图像, 你能呈现出的是一组 被设定为隐藏的算法, 一组被设定为可找到并执行的算法。 这一切太伟大了,太棒了。 美国股票市场 中的百分之70, 操作系统的百分之70 前身为退休金, 按揭。
And what could go wrong? What could go wrong is that a year ago, nine percent of the entire market just disappears in five minutes, and they called it the Flash Crash of 2:45. All of a sudden, nine percent 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."
什么可能出问题? 一年前 出的问题是 整个市场的百分之九消失了五分钟, 这被称为“2:45的瞬间崩溃”。 突然之间,百分之九就消失了, 直到今天大家 对发生了什么还不能达成一致, 因为没人下命令,没人要这么做。 对那天所发生的大家束手无策。 他们就是 看着面前的屏幕 上的数字 和一个红色按钮 上面写着,“停。”
And that's the thing, is that we're writing things, we're writing these things that we can no longer read. And we've rendered something 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 Nanex, 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.
事情就是这样 这就是我们正在编写的东西, 我们在编写我们读不懂的东西。 我们把一些事情变得 难以理解。 我们已经对 这个我们创造的世界中 正在发生的事情失去理解能力。 我们开始前进。 在波士顿有个名为Nanex的公司, 他们运用数学和魔法 和我不知道是什么的东西, 他们深入研究所有他们能找到的 市场数据,实际上有时候是一些算法。 当他们找到这些数据时,就把数据抽取出来 像蝴蝶似的把它们钉在墙上。 他们所做的也是我们在 面对大量我们无法理解的数据时所做的 -- 给它们一个名字 和一个故事。 这就是他们找的一个, 他们称之为‘小刀’, ‘嘉年华’, ‘波士顿洗牌者’, 暮光。
And the gag is that, of course, these aren't just running through the market. You can find these kinds of things wherever you look, once you learn how to look for them. 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 ... (Laughter) If you had bought it at 1.7, it would have been a bargain. A few hours later, it had gone up to 23.6 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."
有意思的是 这不仅存在于股票市场上。 一旦你知道如何寻找它们, 无论在哪儿你都能找到这类东西, 你能在这儿找到它:这本关于苍蝇的书 你可能在亚马逊上看到过这本书。 你或许已经注意到 它的价格是一百七十万美元。 绝版 -- 仍然是绝版... (笑声) 如果你在一百七十万美元是购买了它,那还算便宜的。 数小时后,它涨到了 两千三百六十万美元, 含运费和手续费。 问题是: 没有人购买或销售任何东西;发生了什么? 你在亚马逊看到的这一行为 毫无疑问与在华尔街看到的一样。 当你看到这类行为时, 你所看到的就是 算法冲突的证据, 算法相互锁定, 没有人类的监管, 没有任何成熟的监督 说,“实际上,一百七十万美元是很大一笔了。”
(Laughter)
(笑声)
And as with Amazon, so it is with Netflix. And so Netflix has gone through several different algorithms over the years. They started with Cinematch, 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 Pragmatic Chaos has. Pragmatic Chaos, like all Netflix algorithms, determines, in the end, 60 percent of what movies end up being rented. So one piece of code with one idea about you is responsible for 60 percent of those movies.
与亚马逊一样,Netflix也有这样的问题。 因此Netflix多年来已经 经历了若干不同算法。 他们开始用的是Cinematch,后来又尝试了一些其他的。 有Dinosaur Planet,Gravity。 现在他们在使用Pragmatic Chaos。 Pragmatic Chaos,与所有Netflix算法相同, 试着做同样的事情。 它试图把握住你, 掌控人类头骨内的固件, 这样它就能向你推荐 你可能想看的电影 -- 这是个非常非常困难的事情。 但问题和事实的难点 在于我们没有真的掌握它, 它没有消除 Pragmatic Chaos的影响。 Pragmatic Chaos,如同Netflix的所有算法, 最后决定了 百分之60 最终被租用的电影。 因此一段带有 你的看法的代码 对百分之60的电影负责。
But what if you could rate those movies before they get made? Wouldn't that be handy? Well, a few data scientists from the U.K. are in Hollywood, and they have "story algorithms" -- a company called Epagogix. 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. 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 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?
但如果你在这些电影制作之前 对它们进行评价会怎样? 这样岂不是很方便? 嗯,一些来自英国的数据科学家在好莱坞, 他们有故事算法 -- 一家名为Epagogix的公司。 你可以向他们提供你的剧本, 他们能量化地告诉你 这是个三千万美元票房的电影 或是个两亿美元票房的电影。 这不是Google。 不是信息。 不是金融统计;这是文化。 你在这儿看到的 或你没有真正察觉的, 是文化的物理学。 如果这些算法, 象华尔街中的算法, 某天崩溃了出错了, 我们怎么知道, 那会是什么样子?
And they're in your house. They're in your house. 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. 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.
它们在你的屋子里,它们在你的屋子里。 有两种算法在争夺你的客厅。 有两种不同的清洁机器人 它们对清洁的含义有着非常不同的理解。 如果你让它慢下来,在它上面放上灯光 你就能够看到。 有点像你卧室里的秘密建筑师。 建筑本身 某种程度上服从算法优化的想法 并非牵强。 这是超现实,它就发生在你周围。
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 mishegas of letting everybody go into whatever car they want. Everybody who wants to go to the 10th floor goes into car two, and everybody who wants to go to the third floor goes into car five. And the problem with that is that people freak out. People panic. And you see why. You see why. It's because the elevator is missing some important instrumentation, like the buttons. (Laughter) 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 machine dialect. And how far can you take that? How far can you take it? You can take it really, really far.
当你在一个密封的金属盒子里时, 一种被称为目标控制电梯的 新式电梯, 最能感受到它。 在你进入电梯之前你要按下 你所要去的楼层的按钮。 它使用装箱算法。 因此让每个人进入 他们想进的电梯一点也不混乱。 想去10楼的人进入二号电梯, 想去三层的人进入五号电梯。 问题是 人们吓坏了。 人们抓狂了。 你知道为什么。你知道为什么。 因为电梯 缺少了些重要的东西,比如按钮。 (笑声) 正如人们使用的电梯。 都有 标明向上或向下的数字 还有一个红色按钮,上写着,“停。” 这就是我们正在设计的。 我们正在设计 这种机器方言。 能做到什么程度?能用它做到何种境界? 用它可以走得很远很远。
So let me take it back to Wall Street. 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 500,000 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 an algorithm could get close to the Internet.
让我们回到华尔街。 因为华尔街的算法 依赖于一个高于一切的特质, 速度。 它们的运行时间以毫秒和微妙计算。 让你们对微秒有点感觉, 点击一下鼠标 要花50万微秒的时间。 但如果你是一个华尔街的算法 落后5微秒, 你就是失败者。 因此,如果你是一个算法, 你得寻找一个像我在法兰克福所遇的那样的建筑师 把整个摩天大楼掏空 -- 扔掉所有的家具和人类使用的基础设施, 仅用刚才铺至地面, 准备好大批的服务器入驻 -- 整个算法 都能快速连入互联网。
And you think of the Internet as this kind of distributed system. And of course, it is, but it's distributed from places. In New York, this is where it's distributed from: the Carrier Hotel located on Hudson Street. And this is really where the wires come right up into the city. And the reality is that the further away you are from that, you're a few microseconds behind every time. These guys down on 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. 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.
把互联网看成一种分布式系统。 当然,它就是,但分布于不同地点。 在纽约,它分布在: 位于哈德逊大街的 电信酒店。 这是线缆真正进入这座城市的地方。 事实上你距离这地方越远, 每次都会落后几微秒。 在华尔街上的这些家伙, Marco Polo和Cherokee Nation, 他们比这些 在电信酒店周围的 被掏空了的大厦里的家伙 要落后八微秒。 这在不断发生。 我们要把它们不断掏空, 因为你,每一英寸 每一磅,每一美元, 没人能像‘波士顿洗牌者’那样 从中榨取收益。
But if you zoom out, if you zoom out, you would see an 825-mile trench between New York City and Chicago that's 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.
但如果你缩小地图, 如果你缩小地图, 你会看到一条长达825英里的 位于纽约城和芝加哥之间的沟渠, 它在过去几年中 由一家名为Spread Networks的公司建造。 这条 两座城市间的光缆 就是为了以比你点击鼠标 快37倍的速度传输信号 -- 就是为了这些算法, 就是为了‘嘉年华’和‘小刀’。 你想一想, 我们正在用炸药和岩石锯 穿过美国, 只是为了一个算法 能快三微秒完成交易, 都是为了一个没人会知道的 通信框架, 这有点命运天定论 并总是在寻找新的领域。
Unfortunately, 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 read this map, and what this map says 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. We'll actually part the water to pull money out of the air, because it's a bright future if you're an algorithm.
不幸地是,我们面前困难重重。 这仅仅是理论上的。 这是MIT的一些数学家制作的。 我并不太明白 他们所谈论的。 它涉及光锥体和量子纠缠, 这些我真的都不太明白。 但我能看明白这张地图。 这张地图表明 如果你要在市场上赚钱,那些红点所在位置, 也是人所在位置,也是城市所在位置, 就要把服务器放到蓝点所在位置 这样最有效率。 各位也许已经注意到这些蓝点 许多都在大洋中。 那么我们要做的是,建造一些气泡之类的东西, 或者是平台。 我们们确实能分离水, 从空气中挖掘财富, 因为这很有前途, 如果你是一个算法的话。
(Laughter)
(笑声)
And it's not the money that's so interesting actually. It's what the money motivates, 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. 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 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.
实际上有意思的不是钱。 而是钱所激发的东西。 我们实际上在用 这种算法的效率 在改造地球本身。 根据这点, 各位回去看看 迈克尔·纳贾尔的照片, 会领悟到它们不是隐喻,而是预言。 它们是 我们正在数学上掀起的 那种地震效应的预言。 风景总是由 自然和人类之间的这种 怪异不安的协作产生的。 但现在有这些第三方协同进化力量:算法 -- ‘波士顿洗牌者‘,’嘉年华’。 我们将不得不将这些视为自然。 某种程度上,它们是的。
Thank you.
谢谢。
(Applause)
(掌声)