How algorithms shape our world Kevin Slavin

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 in the

world inside us and it specifically

algorithms which are basically the math

that computers used 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 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 and he

said well we were mostly breaking

stealth and 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 a

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 and he said yeah he said but

that’s if you’re a radar he said 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 and I

said yeah that’s 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 he said well you

know financial services and I said oh

because those have been in the news

lately and I said I said how does that

work and I 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 involved in part

because institutional traders have the

same problems that the United States

airforce 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 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 is that we’re

writing things we’re writing these

things that we can no longer read and

it’s we’ve 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 in to all the market data and they

find actually sometimes some of these

algorithms and they when they find them

they 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 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 or in

Hollywood and they have story algorithms

and 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 there 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 there’s 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 if 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 the tenth floor

goes into car two and everybody wants to

go 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 a Wall Street Marco

Polo and Cherokee Nation they’re eight

microseconds behind all these guys going

in to 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’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 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

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 your 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

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 no jars 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 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

这是艺术家

迈克尔·纳贾尔(Michael Najjar)的照片,从

某种意义上说,他去阿根廷

拍照是真实的,但这也是虚构

的,在那之后有很多工作

要做,他所做的是他

实际上是用数字方式重塑的 所有

山脉的轮廓都跟随

道琼斯指数的变迁所以

你看到的

悬崖与山谷的高悬崖是2008年的

金融危机这张照片是当

我们在那边的山谷深处拍摄的我

不 知道我们现在在哪里 这是

恒生指数或香港和类似的

地形我想知道为什么这是艺术

正确这是隐喻但我认为

关键是这是用

牙齿的隐喻而且我想用那些牙齿

今天建议我们重新思考

一下当代数学的作用,

不仅仅是金融

数学,而是一般的数学,它

正在从我们

从世界中提取和派生的东西转变

为某种东西 ng实际上开始

在我们内心的世界中塑造我们周围的

世界,特别是

算法,这些算法基本上

是计算机用来决定事物的数学,它们

获得了对真理的敏感性,因为

它们一遍又一遍地重复,它们

有点僵化和 钙化,

它们变得真实,几年前我

在跨大西洋飞行的所有地方都在考虑这个,

因为我

碰巧坐在一位

和我年龄相仿的匈牙利物理学家旁边,我们正在谈论

寒冷期间的生活 匈牙利物理学家的战争

,我说你在做什么,他

说我们主要是打破

隐身,我说这是一项很好的工作

,有趣的是它是如何工作的

,所以要了解你必须

了解一点关于如何

隐身 工作,所以这是一个

过度简化,但基本上它

不像你可以

通过 156 吨钢铁

在天空中直接传递一个雷达信号它不仅仅是要

显示

但是如果你能把这个巨大的

东西变成一

百万个小东西,就像

一群鸟,

那么正在寻找的雷达必须

能够看到天空中的每一群鸟

,如果你 ‘是一个非常

糟糕的工作的雷达,他说是的,他说,

但如果你是雷达,他说所以我们

没有使用雷达,我们建造了一个黑匣子

,它正在寻找电信号

电子通信,每当我们

看到 一群有电子通讯的鸟,

我们认为

可能与美国人有关,

我说是的,这很好,很好,

所以你有效地否定了 60 年

的航空研究你的行为

对你来说是什么你知道什么时候你做什么 你

长大了,他说他说你

知道金融服务,我说哦,

因为这些最近都在新闻中

,我说我说那是如何

工作的,我说华尔街现在有 2000 名

物理学家,我就是

其中之一 他们中的一个,我说得很好

,华尔街的黑匣子是什么,他

说得很好,你问这个很有趣,

因为它实际上被称为黑匣子

交易,有时也被称为

算法交易算法交易和

算法交易,部分

原因是机构交易者有

美国空军遇到的同样问题是,他们正在

转移这些职位,无论是

宝洁公司还是其他任何东西,

他们正在像通过市场出售一百万股一样的

东西,如果

他们同时这样做,就像

玩扑克,然后马上全押,

你只是提示你的牌,所以他们

必须找到一种方法,他们使用

算法来做到这一点,将那

件大事分解成一百万个小

交易,以及其中的魔力和

恐怖

就是你用来将大事

分解成一百万个小事的相同数学可以

用来找到一百万个小事并将

它们缝合在一起并计算出 找出

市场上实际发生的事情,所以如果

你需要了解

股市现在正在发生的事情,

你可以想象的是一堆

基本上被编程

为隐藏

的算法和一堆被

编程为去寻找的算法 他们并采取行动,

所有这一切都很好,很好,

这是美国股票的

70%,其中 70% 的操作系统

以前称为您的养老金,您的

抵押贷款,

可能出错的是一年

以前 9% 的市场

在五分钟内消失了,他们

称之为 2:45 的闪电崩盘,

突然之间 9% 就消失了,

直到今天没有人甚至可以就

发生的事情达成一致,因为没有人下令

没有人问 因为没有人可以

控制实际发生的事情,

他们所拥有的只是他们面前的一个显示器,上面

有数字,

只有一个红色按钮,说停止,

正确的是,我们是

w 我们正在写这些

我们无法再阅读的东西,

这是我们已经使某些

东西难以辨认,我们已经失去了对我们创造的

这个世界上实际发生的事情的感觉

,我们正在

开始我们的方式在波士顿有一家

名为 Nanak’s 的公司,他们使用数学和

魔法,我不知道是什么,他们

访问了所有的市场数据,他们

有时会发现其中一些

算法,当他们找到它们

时 他们把它们拉出来,

像蝴蝶一样把它们钉在墙上,

当面对大量我们不理解的数据时,他们会做我们一直在做的事情,

那就是

他们给他们一个名字和一个故事,所以

这是一个 他们发现他们称

这把刀为狂欢节波士顿

洗牌者暮光之城和插科打诨

当然这些不仅仅是

在市场上运行

一旦你学会了如何正确地寻找它们,你就可以在任何地方找到这些东西

可以找到它

如果你在亚马逊上看过这本关于苍蝇的书,你可能已经

注意到它的起价为 170

万美元,

如果你以 1.7 美元的价格购买它,它仍然是绝版

的,几个小时后它会很便宜

后来它涨到了

23 点 600 万美元,加上

运费和手续费,问题

是没有人买卖

正在发生的任何事情,你

在亚马逊上看到的这种行为就像

你在华尔街看到的一样,当你看到这个时

你所看到的行为是

冲突中算法的证据 算法在

没有任何人为监督的

情况下相互锁定循环 没有任何成人监督 说

实际上 170 万足够你

坚持下去,就像亚马逊一样,

Netflix 也是如此等等 Netflix 从电影开始的这些年来已经经历

了几种不同的算法,

他们已经尝试了很多其他算法,

有恐龙星球,有重力,

他们正在使用实用主义 c chaos now

pragmatic chaos 就像所有的 Netflix 算法都在

尝试做同样的事情

它试图掌握你在

人类头骨内的固件,以便

它可以推荐你

接下来可能想看的电影,这是一个非常非常

困难的问题,但问题的难度

以及我们并没有

真正解决它的事实并

没有消除语用混乱带来的影响,

就像所有

Netflix 算法最终决定的那样

60% 电影最终会被正确租用,

所以一段代码

对你有一个想法,占这些电影的 60%,

但是如果你能在

这些电影正确之前对它们进行评分,那

不是很方便,所以一些

数据科学家来自 在英国或

好莱坞,他们有故事算法

和称为史诗哦笑话的公司,你

可以在那里运行你的剧本,

他们可以量化地告诉你

这是一部 3000 万美元或

2 亿美元的电影,而且 他的问题

是这不是谷歌的权利这

不是信息

这些不是财务统计这是

文化你在这里看到的或者

你通常没有真正看到的是

这些是文化的物理如果

这些算法,比如华尔街的算法,

一天就崩溃了,然后

出了差错

清洁机器人对清洁的含义有非常不同的

想法

,如果你放慢速度并给它们安装灯,你可以看到它

,你的卧室里有点像秘密建筑师,是

的,建筑本身在

某种程度上受到算法

优化的想法是 不牵强 它是

超级真实的,它就在你身边发生

当你在一个密封的金属盒子里时,你最能感觉到它是

一种新型电梯,它们被

称为目的地控制电梯

,就是这样 s 如果

你上电梯之前按你要去的楼层,它使用

所谓的装箱算法,所以

没有这种让

每个人都进入他们想要的

每个人都想去的车的错误。 楼层

进入二号车厢,每个人都

想去三楼进入五号车厢

,问题是

人们吓坏了人们恐慌,你

明白为什么正确你明白为什么这是

因为电梯缺少一些重要的

仪器,比如按钮

就像人们使用的东西一样,它所

拥有的只是向上或向下移动的数字

和表示停止的红色按钮

,这就是我们正在设计的目标

我们正在为这种机器

方言设计 你能走

多远 你能走多远 你可以走

多远 所以让我把它

带回华尔街好吧,因为

华尔街的算法

首先取决于一种质量,那就是

速度 它们以毫秒和微秒为单位运行

,只是为了让您

了解微秒是多少,只需单击鼠标就

需要 50 万微秒

,但是如果您是

华尔街算法并且

落后五微秒,那么您就是 失败者,所以如果

你是一个算法,你会寻找

像我在法兰克福遇到的那个建筑师,

他正在挖空一座

摩天大楼,扔掉所有的

家具,所有的基础设施供

人类使用,只是

在地板上铺设钢筋以准备

服务器堆栈全部进入,

以便算法可以接近

互联网,您将互联网

视为这种分布式系统

,当然它是,但它是

从纽约的地方分发的,这

就是它的分发地 它的运营商

酒店位于哈德逊街,这

确实是电线直接

进入城市的地方,而现实情况是,

你离得越远,

你就会落后 e 几微秒 就

在这些家伙沿着华尔街

马可波罗和切诺基国家

前进的时候,他们比所有这些进入航母酒店周围

空荡荡的建筑物的人落后 8 微秒

,这将继续发生,我们

将继续挖空 将它们排除在外,因为

您以英寸为英寸,以英镑为英镑

,以美元为美元,您没有人可以

像波士顿洗牌机那样从该空间中挤出收入,

但是

如果您缩小,如果缩小,您会看到

纽约市之间有一条 825 英里的海沟

芝加哥是

由一家名为 spread networks 的公司在过去几年中建造的,

这是一条

铺设在这两个城市之间的光缆,它

传输一个信号的

速度比你点击鼠标的速度快 37

倍 只是为了

狂欢节和刀,当你

想到这一点时,我们正在

用炸药

和岩石锯穿过美国,这样算法就可以

在三微秒内完成交易 ds faster

all for a communication framework that

no human will know that’s a

manifest duty and we will always looking

for a new frontier and 幸运的

是我们为我们完成了我们的工作 这

只是理论上的 这是

麻省理工学院的一些数学家和 事实是

我不太了解

他们在谈论的很多内容,它涉及光

锥和量子纠缠,

我也不太了解,但

我可以

这张地图,这张地图说的是

,如果你 ‘试图

在红点所在的市场上赚钱,

这就是人们所在的

城市,你必须把

服务器放在蓝点所在的地方,这样

才能最有效地做到这一点,

你可能已经注意到了这些 蓝

点是它们中的很多都在

海洋中间,所以这就是我们

要做的,我们将建造气泡或其他东西

或平台实际上将

分水权以从空中提取资金,

因为这是一个光明的未来 如果你’

是一种算法,而真正有趣的不是钱

,而是

钱的动力,我们

实际上正在用这种算法效率对地球本身进行地球化改造

,在这种情况下,你回去

看看迈克尔没有罐子的照片,

你 意识到它们不是隐喻,

它们是正确的预言,它们是

我们正在制作的数学的那种地震地面效应的预言,

而景观总是由

自然与人类之间这种奇怪的不安合作所构成的,但是 现在有

这种第三种协同进化力

算法波士顿洗牌

狂欢节我们必须将它们

理解为自然并以某种方式感谢它们