TEDGlobal 2011
Kevin Slavin: How algorithms shape our world
Kevin Slavin: Kako algoritmi oblikuju naš svijet
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Kevin Slavin zagovara tezu da živimo u svijetu oblikovanom, i sve više kontrolisanom od strane algoritama. U ovom nadahnutom izlaganju sa TEDGlobal on demonstrira kako ovi kompleksni kompjuterski programi oblikuju taktike špijunaže, cijene dionica, scenarije za film, arhitekturu. Također nas upozorava da pišemo programe koje ne možemo razumjeti, sa posljedicama koje ne možemo kontrolisati.
Kevin Slavin - Algoworld expert
Kevin Slavin navigates in the algoworld, the expanding space in our lives that’s determined and run by algorithms. Full bio
Kevin Slavin navigates in the algoworld, the expanding space in our lives that’s determined and run by algorithms. Full bio
Double-click the English transcript below to play the video.
00:15
This is a photograph
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Ovo je fotografija
00:17
by the artist Michael Najjar,
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koju je načinio umjetnik Michael Najjar,
00:19
and it's real,
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i ona je stvarna
00:21
in the sense that he went there to Argentina
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u smislu da je on otišao u Argentinu
00:23
to take the photo.
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da je uslika
00:25
But it's also a fiction. There's a lot of work that went into it after that.
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Ali je također i fikcija. Uloženo je mnogo truda u nju nakon samog fotografisanja.
00:28
And what he's done
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Ono što je on ustvari uradio je
00:30
is he's actually reshaped, digitally,
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preoblikovao, digitalno,
00:32
all of the contours of the mountains
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sve vrhove planina
00:34
to follow the vicissitudes of the Dow Jones index.
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kako bi odgovarali promjenama Dow Jones indeksa.
00:37
So what you see,
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Tako da je ovo što vidite
00:39
that precipice, that high precipice with the valley,
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ova padina, ova strma padina sa dolinom u produžetku
00:41
is the 2008 financial crisis.
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ustvari finansijska kriza iz 2008. godine.
00:43
The photo was made
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Fotografija je napravljena
00:45
when we were deep in the valley over there.
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kada smo zagazili duboko u dolinu.
00:47
I don't know where we are now.
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Ne znam gdje se sada nalazimo.
00:49
This is the Hang Seng index
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Ovo je Hang Seng indeks
00:51
for Hong Kong.
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berze u Hong Kongu.
00:53
And similar topography.
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I slična topografija.
00:55
I wonder why.
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Pitam se zašto.
00:57
And this is art. This is metaphor.
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Ovo je umjetnost, zar ne? Ovo je metafora.
01:00
But I think the point is
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Ali čini se da je ključno
01:02
that this is metaphor with teeth,
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da je ovo metafora sa zubima,
01:04
and it's with those teeth that I want to propose today
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i sa tom mišlju vam želim predložiti danas
01:07
that we rethink a little bit
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da ponovo promislimo
01:09
about the role of contemporary math --
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ulogu savremene matematike --
01:12
not just financial math, but math in general.
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ne samo finansijske matematike, nego matematike uopšte.
01:15
That its transition
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Njena trazicija
01:17
from being something that we extract and derive from the world
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iz nečega što izvodimo i deriviramo iz stvarnog svijeta
01:20
to something that actually starts to shape it --
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u nešto što zapravo počinje oblikovati svijet --
01:23
the world around us and the world inside us.
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svijet oko nas, svijet unutar nas.
01:26
And it's specifically algorithms,
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I posebno algoritme,
01:28
which are basically the math
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koji su u osnovi matematika
01:30
that computers use to decide stuff.
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koju kompjuteri koriste kako bi donosili odluke.
01:33
They acquire the sensibility of truth
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Oni stiču osjećaj istine
01:35
because they repeat over and over again,
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jer se iznova ponavljaju.
01:37
and they ossify and calcify,
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I oni se okoštaju i kalcificiraju,
01:40
and they become real.
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i zapravo postaju stvarni.
01:42
And I was thinking about this, of all places,
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Ja sam razmišljao o ovome, od svih mjesta,
01:45
on a transatlantic flight a couple of years ago,
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na preko-atlantskom letu prije par godina
01:48
because I happened to be seated
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jer sam se slučajno našao na sjedištu
01:50
next to a Hungarian physicist about my age
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pored mađarskog fizičara, otprilike mojih godina,
01:52
and we were talking
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i mi razgovaramo
01:54
about what life was like during the Cold War
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o tome kakav je bio život fizičara
01:56
for physicists in Hungary.
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u Mađarskoj tijekom hladnog rata.
01:58
And I said, "So what were you doing?"
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I upitao sam ga, "Šta ste Vi radili?",
02:00
And he said, "Well we were mostly breaking stealth."
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I rekao je, "Pa, uglavnom smo pokušavali da razbijemo nevidljivost lovačkih aviona.“
02:02
And I said, "That's a good job. That's interesting.
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A ja sam rekao, "Zvuči kao odličan posao. To je interesantno."
02:04
How does that work?"
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"Kako to funkcioniše?"
02:06
And to understand that,
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Da biste to razumjeli,
02:08
you have to understand a little bit about how stealth works.
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morate imate osnovno razumijevanje kako funkcioniše nevidljivost aviona.
02:11
And so -- this is an over-simplification --
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I -- ovo je potpuno pojednostavljeno --
02:14
but basically, it's not like
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ali u osnovi ne možete jednostavno
02:16
you can just pass a radar signal
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da provučete radarski signal
02:18
right through 156 tons of steel in the sky.
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kroz 156 tona čelika na nebu.
02:21
It's not just going to disappear.
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Objekat neće jednostavno nestati.
02:24
But if you can take this big, massive thing,
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Ali ako možete uzeti ovu veliku, masivnu stvar,
02:27
and you could turn it into
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i pretvoriti je u
02:30
a million little things --
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milion malih stvari --
02:32
something like a flock of birds --
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nešto kao jato ptica --
02:34
well then the radar that's looking for that
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u tom slučaju, radar koji to traži
02:36
has to be able to see
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mora biti u stanju uočiti
02:38
every flock of birds in the sky.
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svako jato ptica na nebu.
02:40
And if you're a radar, that's a really bad job.
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Ako ste radar, to je zaista težak posao.
02:44
And he said, "Yeah." He said, "But that's if you're a radar.
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"Da", rekao je, "ali samo ako si radar.
02:47
So we didn't use a radar;
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Tako da nismo koristili radar;
02:49
we built a black box that was looking for electrical signals,
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napravili smo crnu kutiju koja je pretraživala električne signale,
02:52
electronic communication.
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elektronsku komunikaciju.
02:55
And whenever we saw a flock of birds that had electronic communication,
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I kad god bismo vidjeli jato ptica koje ima elektronsku komunikaciju,
02:58
we thought, 'Probably has something to do with the Americans.'"
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mogli smo biti prilično sigurni Amerikanci imaju nešto s tim."
03:01
And I said, "Yeah.
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A ja sam rekao "Da.
03:03
That's good.
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To je dobro.
03:05
So you've effectively negated
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"Znači, Vi ste efektivno poništili
03:07
60 years of aeronautic research.
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"60 godina aeronautičkog istraživanja.
03:09
What's your act two?
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Šta se dešava u drugom činu?
03:11
What do you do when you grow up?"
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Šta radite sad kad ste porasli?"
03:13
And he said,
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I on reče,
03:15
"Well, financial services."
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"Pa... finansijske usluge."
03:17
And I said, "Oh."
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"Oh.", rekao sam.
03:19
Because those had been in the news lately.
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Obzirom da su vijesti o finansijama nešto češće u posljednje vrijeme.
03:22
And I said, "How does that work?"
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"Kako to funkcioniše?", pitao sam.
03:24
And he said, "Well there's 2,000 physicists on Wall Street now,
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Rekao je, "Pa trenutno ima 2.000 fizičara na Wall Streetu,
03:26
and I'm one of them."
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i ja sam jedan od njih."
03:28
And I said, "What's the black box for Wall Street?"
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"I šta je crna kutija na Wall Streetu?", pitao sam.
03:31
And he said, "It's funny you ask that,
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"Interesantno da to pitate," rekao je
03:33
because it's actually called black box trading.
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"obzirom da se zaista zove trgovanje iz crne kutije ("black box trading").
03:36
And it's also sometimes called algo trading,
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A nekada se naziva i algo trgovanje,
03:38
algorithmic trading."
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algoritamsko trgovanje."
03:41
And algorithmic trading evolved in part
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I algoritamsko trgovanje je nastalo dijelom
03:44
because institutional traders have the same problems
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jer su institucionalni investitori imali iste probleme
03:47
that the United States Air Force had,
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kao američka ratna avijacija.
03:50
which is that they're moving these positions --
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Mijenjali su svoje vlasničke pozicije --
03:53
whether it's Proctor & Gamble or Accenture, whatever --
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bilo da je to bio Proctor & Gamble, Accenture, ili neka druga firma --
03:55
they're moving a million shares of something
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mijenjali su vlasništvo nad milionima dionica nečega
03:57
through the market.
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kroz tržište.
03:59
And if they do that all at once,
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I ako to urade odjednom
04:01
it's like playing poker and going all in right away.
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to je kao da uložite sve u prvom dijeljenju u igri pokera.
04:03
You just tip your hand.
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Jednostavno odate karte.
04:05
And so they have to find a way --
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Tako da su morali pronaći način --
04:07
and they use algorithms to do this --
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a za ovo koriste algoritme --
04:09
to break up that big thing
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da podijele tu jednu veliku stvar, veliku transakciju
04:11
into a million little transactions.
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u milion malih transakcija.
04:13
And the magic and the horror of that
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Čarolija i užas toga je da
04:15
is that the same math
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istu matematiku
04:17
that you use to break up the big thing
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koju koristite da podijelite veliku stvar
04:19
into a million little things
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u milion malih djelića
04:21
can be used to find a million little things
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možete koristiti da pronađete milion malih djelića
04:23
and sew them back together
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i ponovo ih sastavite
04:25
and figure out what's actually happening in the market.
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i shvatite šta se zapravo dešava na tržištu.
04:27
So if you need to have some image
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Tako da ako želite imati neku sliku
04:29
of what's happening in the stock market right now,
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berzovnog tržišta u ovom trenutku,
04:32
what you can picture is a bunch of algorithms
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možete ga zamisliti kao veliki broj algoritama
04:34
that are basically programmed to hide,
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koji su programirani da prikriju transakcije,
04:37
and a bunch of algorithms that are programmed to go find them and act.
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i veliki broj algoritama koji su programirani da ih pronađu i djeluju.
04:40
And all of that's great, and it's fine.
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I sve je to u redu.
04:43
And that's 70 percent
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Dok ne saznate da oni čine 70 procenata
04:45
of the United States stock market,
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berzovnog prometa Sjedinjenih Država,
04:47
70 percent of the operating system
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70 procenata operativnog sistema
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formerly known as your pension,
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prethodno poznatog kao vaše penzije,
04:52
your mortgage.
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vaši zajmovi za kuće.
04:55
And what could go wrong?
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A šta može poći po zlu?
04:57
What could go wrong
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Pa, šta može poći po zlu
04:59
is that a year ago,
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je da je prije godinu dana,
05:01
nine percent of the entire market just disappears in five minutes,
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9 posto vrijednosti cjelokupnog tržišta nestalo za samo pet minuta.
05:04
and they called it the Flash Crash of 2:45.
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i to nazivaju "fleš krahom u 2:45".
05:07
All of a sudden, nine percent just goes away,
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Potpuno neočekivano, 9 posto jednostavno nestane,
05:10
and nobody to this day
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i nitko do današnjeg dana,
05:12
can even agree on what happened
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se čak ne može ni složiti oko toga šta se dogodilo,
05:14
because nobody ordered it, nobody asked for it.
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jer nitko nije izdao nalog, nitko to nije tražio.
05:17
Nobody had any control over what was actually happening.
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Niko nije imao kontrolu nad ovim.
05:20
All they had
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Sve što su imali bio je monitor
05:22
was just a monitor in front of them
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ispred njih
05:24
that had the numbers on it
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sa brojevima na njemu
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and just a red button
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i crveno dugme
05:28
that said, "Stop."
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na kojem je pisalo "Stop".
05:30
And that's the thing,
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Stvar je zapravo u tome
05:32
is that we're writing things,
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da mi trenutno pišemo stvari,
05:34
we're writing these things that we can no longer read.
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pišemo ove stvari koje više ne znamo pročitati.
05:37
And we've rendered something
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Načinili smo nešto
05:39
illegible,
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nečitljivim.
05:41
and we've lost the sense
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I izgubili smo osjećaj
05:44
of what's actually happening
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šta se zapravo zbiva
05:46
in this world that we've made.
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u ovom svijetu koji smo napravili.
05:48
And we're starting to make our way.
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I počinjemo tražiti put.
05:50
There's a company in Boston called Nanex,
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U Bostonu postoji kompanija koja se zove Nanex,
05:53
and they use math and magic
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koja koristi matematiku i čarolije
05:55
and I don't know what,
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i ne znam šta sve ne,
05:57
and they reach into all the market data
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i oni posegnu za svim podacima o trgovini na berzi
05:59
and they find, actually sometimes, some of these algorithms.
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i ponekada zaista pronađu neke od ovih algoritama.
06:02
And when they find them they pull them out
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I kada ih pronađu, oni ih iščupaju iz mase podataka
06:05
and they pin them to the wall like butterflies.
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i zakače ih na zid, kao preparirane leptirove.
06:08
And they do what we've always done
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I rade ono što smo uvijek radili
06:10
when confronted with huge amounts of data that we don't understand --
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kad se suočimo sa velikom količinom podataka koje ne razumijemo --
06:13
which is that they give them a name
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damo im ime
06:15
and a story.
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i priču.
06:17
So this is one that they found,
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I tako, ovo je jedan od algoritama koji su pronašli
06:19
they called the Knife,
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koji zovu "Nož",
06:23
the Carnival,
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"Karneval",
06:25
the Boston Shuffler,
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"Bostonski mješač",
06:29
Twilight.
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"Sumrak".
06:31
And the gag is
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Stvar je da,
06:33
that, of course, these aren't just running through the market.
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naravno, oni nisu ograničeni samo na tržišta.
06:36
You can find these kinds of things wherever you look,
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Ovakve algoritme možete naći gdje god da pogledate,
06:39
once you learn how to look for them.
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ukoliko naučite šta da tražite.
06:41
You can find it here: this book about flies
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Možete ih naći ovdje: ova knjiga o muhama
06:44
that you may have been looking at on Amazon.
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koju ste možda uočili na Amazonu.
06:46
You may have noticed it
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Možda vam je zapala za oko
06:48
when its price started at 1.7 million dollars.
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kada je njena cijena dostigla 1,7 miliona dolara.
06:50
It's out of print -- still ...
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Knjiga se više na štampa, ali ipak...
06:52
(Laughter)
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(Smijeh)
06:54
If you had bought it at 1.7, it would have been a bargain.
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Da ste je kupili po cijeni od 1.7 miliona, to bi bila bagatela.
06:57
A few hours later, it had gone up
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Nekoliko sati kasnije, cijena se popela
06:59
to 23.6 million dollars,
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na 23.6 miliona dolara,
07:01
plus shipping and handling.
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uz troškove pošiljke i obrade.
07:03
And the question is:
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I pitanje je sljedeće:
07:05
Nobody was buying or selling anything; what was happening?
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Niko nije ništa kupovao ili prodavao; šta se događalo?
07:07
And you see this behavior on Amazon
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Možete uočiti ove pojave na Amazonu
07:09
as surely as you see it on Wall Street.
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isto kao što ih možete vidjeti na Wall Streetu.
07:11
And when you see this kind of behavior,
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I kada vidite ovu vrstu pojave,
07:13
what you see is the evidence
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ono što vidite je manifestacija
07:15
of algorithms in conflict,
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algoritama u konfliktu,
07:17
algorithms locked in loops with each other,
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algoritama zaključanih u beskonačnim krugovima jednih sa drugima,
07:19
without any human oversight,
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bez ikakvog ljudskog nadzora,
07:21
without any adult supervision
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bez roditeljske pažnje
07:24
to say, "Actually, 1.7 million is plenty."
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nekoga ko bi rekao, "Zapravo, 1.7 miliona je više nego dovoljno."
07:27
(Laughter)
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(Smijeh)
07:30
And as with Amazon, so it is with Netflix.
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Sličan primjer Amazonu vidimo i na Netflixu.
07:33
And so Netflix has gone through
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Netflix je prošao kroz nekoliko
07:35
several different algorithms over the years.
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različitih algoritama tokom godina.
07:37
They started with Cinematch, and they've tried a bunch of others --
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Počeli su sa Cinematch, a onda su okušali i niz drugih.
07:40
there's Dinosaur Planet; there's Gravity.
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Imate Planetu dionosaura ("Dinosaur Planet"), Gravitaciju ("Gravity").
07:42
They're using Pragmatic Chaos now.
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Trenutno koriste Pragmatični haos ("Pragmatic Chaos").
07:44
Pragmatic Chaos is, like all of Netflix algorithms,
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Pragmatični haos pokušava učiniti istu stvar
07:46
trying to do the same thing.
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kao i svi drugi Netflix algoritmi.
07:48
It's trying to get a grasp on you,
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Pokušava shvatiti vas,
07:50
on the firmware inside the human skull,
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operativni sistem u vašim glavama,
07:52
so that it can recommend what movie
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kako bi preporučio naredni film
07:54
you might want to watch next --
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koji biste mogle pogledati --
07:56
which is a very, very difficult problem.
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što je veoma, veoma težak problem.
07:59
But the difficulty of the problem
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Ali složenost problema
08:01
and the fact that we don't really quite have it down,
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kao i činjenica da ga i ne razumijemo u potpunosti,
08:04
it doesn't take away
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ne umanjuje učinak
08:06
from the effects Pragmatic Chaos has.
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koji Pragmatični haos ima.
08:08
Pragmatic Chaos, like all Netflix algorithms,
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Pragmatični haos, kao svi Netflix algoritmi,
08:11
determines, in the end,
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određuje, na kraju,
08:13
60 percent
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60 procenata
08:15
of what movies end up being rented.
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svih filmova koji se rentaju.
08:17
So one piece of code
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Dakle, jedan program
08:19
with one idea about you
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sa nekom idejom o vama
08:22
is responsible for 60 percent of those movies.
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je odgovoran za 60 posto filmova koje pogledate.
08:25
But what if you could rate those movies
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Ali šta kada biste mogli ocijeniti filmove
08:27
before they get made?
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i prije no što ih snime?
08:29
Wouldn't that be handy?
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Zar to ne bi bilo korisno?
08:31
Well, a few data scientists from the U.K. are in Hollywood,
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E, pa nekoliko informacijskih naučnika iz Velike Britanije je u Holivudu,
08:34
and they have "story algorithms" --
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i oni imaju algoritme za scenarije i filmske priče --
08:36
a company called Epagogix.
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kompanije koja se zove Epagogix.
08:38
And you can run your script through there,
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I možete provući vaš scenarij kroz njihov program,
08:41
and they can tell you, quantifiably,
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i oni vam mogu reći, kvantificirati,
08:43
that that's a 30 million dollar movie
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da je to film koji će zaraditi 30 miliona dolara
08:45
or a 200 million dollar movie.
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ili 200 miliona dolara.
08:47
And the thing is, is that this isn't Google.
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Stvar je u tome da ovo nije Google.
08:49
This isn't information.
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Ovo nisu informacije.
08:51
These aren't financial stats; this is culture.
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Ovo nisu finansijski podaci; ovo je kultura.
08:53
And what you see here,
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I ono što vidimo ovdje,
08:55
or what you don't really see normally,
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ili bolje rečeno, što obično ne vidimo jer ostane skriveno,
08:57
is that these are the physics of culture.
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jest da je ovo fizika kulture.
09:01
And if these algorithms,
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I ako ovi algoritmi,
09:03
like the algorithms on Wall Street,
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kao algoritmi na Wall Streetu
09:05
just crashed one day and went awry,
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krahiraju jednog dana i pođu po zlu,
09:08
how would we know?
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kako ćemo znati,
09:10
What would it look like?
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kako će to izgledati?
09:12
And they're in your house. They're in your house.
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A oni su u vašoj kući. Oni su u vašoj kući.
09:15
These are two algorithms competing for your living room.
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Ovo su dva algoritma koja se bore za vašu dnevnu sobu.
09:17
These are two different cleaning robots
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Ovo su dva robotizirana usisivača
09:19
that have very different ideas about what clean means.
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koja imaju vrlo različita shvatanja čistoće.
09:22
And you can see it
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I vi to možete i vidjeti
09:24
if you slow it down and attach lights to them,
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ako ih usporite i dodate im svjetlo.
09:27
and they're sort of like secret architects in your bedroom.
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A oni su kao neki tajni arhitekti u vašoj spavaćoj sobi.
09:30
And the idea that architecture itself
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Čak i ideja da je i sama arhitektura
09:33
is somehow subject to algorithmic optimization
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na neki način podređena algoritamskoj optimizaciji
09:35
is not far-fetched.
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nije nerealna.
09:37
It's super-real and it's happening around you.
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Ona je vrlo stvarna i to se već dešava oko vas.
09:40
You feel it most
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Najviše ćete je osjetiti
09:42
when you're in a sealed metal box,
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kad se budete nalazili u zatvorenoj metalnoj kutiji,
09:44
a new-style elevator;
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liftu nove generacije,
09:46
they're called destination-control elevators.
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kojeg zovu lift sa kontrolom odredišta.
09:48
These are the ones where you have to press what floor you're going to go to
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To su oni liftovi kod kojih morate odabrati sprat na koji idete
09:51
before you get in the elevator.
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prije negoli uđete u lift.
09:53
And it uses what's called a bin-packing algorithm.
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I on koristi ono što se naziva algoritmom za pakovanje kanti.
09:55
So none of this mishegas
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Znači ništa od ove ludosti
09:57
of letting everybody go into whatever car they want.
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da puštamo ljude da ulaze u lift koji žele.
09:59
Everybody who wants to go to the 10th floor goes into car two,
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Svi koji žele na 10-ti sprat ulaze u lift dva,
10:01
and everybody who wants to go to the third floor goes into car five.
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a svi koji žele na treći sprat ulaze u lift pet.
10:04
And the problem with that
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Problem s ovim
10:06
is that people freak out.
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je da se ljudi prestrave.
10:08
People panic.
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Ljudi se uspaniče.
10:10
And you see why. You see why.
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A možete i razumjeti zašto. Vidite zašto.
10:12
It's because the elevator
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Liftu nedostaje
10:14
is missing some important instrumentation, like the buttons.
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nekoliko važnih instrumenata, kao na primjer dugmad.
10:17
(Laughter)
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(Smijeh)
10:19
Like the things that people use.
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Stvari koje ljudi koriste.
10:21
All it has
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Sve što lift ima
10:23
is just the number that moves up or down
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je broj koji se pomjera gore ili dole
10:26
and that red button that says, "Stop."
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i crveno dugme na kojem piše "Stop".
10:29
And this is what we're designing for.
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Tako dizajniramo stvari.
10:32
We're designing
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Dizajniramo
10:34
for this machine dialect.
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za ovaj mašinski dijalekt.
10:36
And how far can you take that? How far can you take it?
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I, dokle možemo ići tako? Dokle ovo možemo dovesti?
10:39
You can take it really, really far.
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Možemo ga dovesti stvarno, stvarno daleko.
10:41
So let me take it back to Wall Street.
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Dopustite mi da se vratim na Wall Street.
10:45
Because the algorithms of Wall Street
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Jer algoritmi na Wall Streetu zavise
10:47
are dependent on one quality above all else,
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od jedne stvari više no od bilo čega drugog,
10:50
which is speed.
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a to je brzina.
10:52
And they operate on milliseconds and microseconds.
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A oni operišu u milisekundama i mikrosekundama.
10:55
And just to give you a sense of what microseconds are,
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Da bih vam dao osjećaj šta je mikrosekunda,
10:57
it takes you 500,000 microseconds
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potrebno vam je 500,000 mikrosekundi
10:59
just to click a mouse.
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da biste kliknuli mišem.
11:01
But if you're a Wall Street algorithm
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Ali ako ste algoritam na Wall Streetu
11:03
and you're five microseconds behind,
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i ako kasnite 5 mikrosekundi,
11:05
you're a loser.
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vi ste gubitnik.
11:07
So if you were an algorithm,
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Dakle ako ste algoritam,
11:09
you'd look for an architect like the one that I met in Frankfurt
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tražili biste arhitektu kao jednoga kojeg sam upoznao u Frankfurtu
11:12
who was hollowing out a skyscraper --
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koji ispražnjuje cijeli neboder --
11:14
throwing out all the furniture, all the infrastructure for human use,
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izbacuje sav namještaj, svu infrastrukturu koju koriste ljudi,
11:17
and just running steel on the floors
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i samo postavlja čelik na podove
11:20
to get ready for the stacks of servers to go in --
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kako bi ih pripremio za nizove servera koji će ići unutra --
11:23
all so an algorithm
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sve kako bi algoritam
11:25
could get close to the Internet.
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mogao prići bliže Internetu.
11:28
And you think of the Internet as this kind of distributed system.
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Obično razmišljamo o Internetu kao o distribuiranom sistemu.
11:31
And of course, it is, but it's distributed from places.
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I naravno, on to i jeste, ali je distribuiran iz različitih lokacija.
11:34
In New York, this is where it's distributed from:
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U New Yorku, distribuiran je odavde:
11:36
the Carrier Hotel
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Carrier hotel
11:38
located on Hudson Street.
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u ulici Hudson.
11:40
And this is really where the wires come right up into the city.
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Odavde žice ulaze direktno u grad.
11:43
And the reality is that the further away you are from that,
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I realnost je da što ste dalje od ove lokacije
11:47
you're a few microseconds behind every time.
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uvijek ste par mikrosekundi sporiji.
11:49
These guys down on Wall Street,
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Ovi momci s Wall Streeta,
11:51
Marco Polo and Cherokee Nation,
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Marco Polo i Cherokee Nation
11:53
they're eight microseconds
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oni su osam mikrosekundi
11:55
behind all these guys
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sporiji od ovih algoritama
11:57
going into the empty buildings being hollowed out
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koje lociraju u ispražnjene zgrade
12:01
up around the Carrier Hotel.
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oko Carrier hotela.
12:03
And that's going to keep happening.
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I to će nastaviti da se dešava.
12:06
We're going to keep hollowing them out,
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Nastavićemo da ih ispražnjujemo,
12:08
because you, inch for inch
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jer vi, centimetar po centimetar,
12:11
and pound for pound and dollar for dollar,
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dolar po dolar, nitko od vas
12:14
none of you could squeeze revenue out of that space
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ne može iscijediti veći prihod iz tog prostora
12:17
like the Boston Shuffler could.
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od Bostonskog mješača.
12:20
But if you zoom out,
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Ali, ako se malo izdvojite iz slike
12:22
if you zoom out,
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i pogledate krupni plan,
12:24
you would see an 825-mile trench
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vidjećete kanal dug 1,330 kilometara
12:28
between New York City and Chicago
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između New Yorka i Čikaga
12:30
that's been built over the last few years
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koji izgrađen tijekom posljednjih par godina
12:32
by a company called Spread Networks.
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od kompanije koja se zove Spread Networks.
12:35
This is a fiber optic cable
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Ovo je optički kabl
12:37
that was laid between those two cities
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koji je pružen između ova dva grada
12:39
to just be able to traffic one signal
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za saobraćaj samo jednog signala
12:42
37 times faster than you can click a mouse --
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37 puta brže no što vi možete kliknuti mišem --
12:45
just for these algorithms,
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izgrađen samo za ove algoritme,
12:48
just for the Carnival and the Knife.
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samo za Karneval, za Nož.
12:51
And when you think about this,
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I kada pomislite na to,
12:53
that we're running through the United States
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da trčimo kroz Sjedinjene Države
12:55
with dynamite and rock saws
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sa dinamitom i razbijačima stijena
12:58
so that an algorithm can close the deal
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samo kako bi algoritam mogao zaključiti transakciju
13:00
three microseconds faster,
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tri mikrosekunde brže,
13:03
all for a communications framework
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sve za komunikacijski okvir
13:05
that no human will ever know,
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koji nijedno ljudsko biće neće upoznati
13:09
that's a kind of manifest destiny;
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to je neka vrsta manifestne sudbine
13:12
and we'll always look for a new frontier.
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koja će uvijek pomjerati nove granice.
13:15
Unfortunately, we have our work cut out for us.
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Nažalost, posao pred nama je vrlo ambiciozan.
13:18
This is just theoretical.
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Ovo je samo teoretski.
13:20
This is some mathematicians at MIT.
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Ovo su pripremili neki matematičari sa MIT-a.
13:22
And the truth is I don't really understand
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I, da budem iskren, ni ja ne razumijem mnogo
13:24
a lot of what they're talking about.
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od ovoga o čemu su pričali.
13:26
It involves light cones and quantum entanglement,
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Uključujući neke svjetlosne kupole i kvantnu zamršenost,
13:29
and I don't really understand any of that.
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i je ne razumijem ništa od toga.
13:31
But I can read this map,
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Ali mogu se snaći na ovoj mapi.
13:33
and what this map says
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I ono što nam ova mapa kaže
13:35
is that, if you're trying to make money on the markets where the red dots are,
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je da, ukoliko mislite zaraditi novac na tržištima gdje su ove crvene tačke,
13:38
that's where people are, where the cities are,
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gdje se nalaze ljudi, u gradovima,
13:40
you're going to have to put the servers where the blue dots are
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moraćete postaviti servere na mjesta označena plavim tačkama
13:43
to do that most effectively.
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kako biste bili najučinkovitiji.
13:45
And the thing that you might have noticed about those blue dots
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Možda ste primjetili da se mnoge od ovih plavih tačaka
13:48
is that a lot of them are in the middle of the ocean.
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nalaze na sredini okeana.
13:51
So that's what we'll do: we'll build bubbles or something,
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Znači to ćemo raditi, pravićemo neke balone ili nešto slično,
13:54
or platforms.
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ili platforme.
13:56
We'll actually part the water
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bukvalno ćemo razdvajati more
13:58
to pull money out of the air,
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kako bismo uzimali novac iz zraka,
14:00
because it's a bright future
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jer pred vama je blistava budućnost,
14:02
if you're an algorithm.
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ukoliko ste algoritam.
14:04
(Laughter)
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(Smijeh)
14:06
And it's not the money that's so interesting actually.
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Novac sam i nije toliko interesatan.
14:09
It's what the money motivates,
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Interesantno je kako nas novac motiviše.
14:11
that we're actually terraforming
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Tako mijenjamo oblik i izgled
14:13
the Earth itself
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površine Zemlje
14:15
with this kind of algorithmic efficiency.
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u cilju algoritamske efikasnosti.
14:17
And in that light,
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I kada se u tom svjetlu
14:19
you go back
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vratite
14:21
and you look at Michael Najjar's photographs,
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i pogledate fotografije Michaela Najjara,
14:23
and you realize that they're not metaphor, they're prophecy.
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shvatite da to nisu metafore, već predskazanja.
14:26
They're prophecy
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To su predskazanja
14:28
for the kind of seismic, terrestrial effects
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seizmičkih, reljefno mijenjajučih efekata
14:32
of the math that we're making.
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matematike.
14:34
And the landscape was always made
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Pejsaž je uvijek bio proizvod
14:37
by this sort of weird, uneasy collaboration
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čudne, nespokojne saradnje
14:40
between nature and man.
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prirode i čovjeka.
14:43
But now there's this third co-evolutionary force: algorithms --
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Ali sada imamo i treću ko-evolucijsku snagu: algoritme --
14:46
the Boston Shuffler, the Carnival.
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Bostonskog mješača, Karneval.
14:49
And we will have to understand those as nature,
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I moraćemo ih početi razumijevati kao dio prirode.
14:52
and in a way, they are.
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Na neki način, oni to i jesu.
14:54
Thank you.
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Hvala vam.
14:56
(Applause)
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(Aplauz)
ABOUT THE SPEAKER
Kevin Slavin - Algoworld expertKevin Slavin navigates in the algoworld, the expanding space in our lives that’s determined and run by algorithms.
Why you should listen
Are you addicted to the dead-simple numbers game Drop 7 or Facebook’s Parking Wars? Blame Kevin Slavin and the game development company he co-founded in 2005, Area/Code, which makes clever game entertainments that enter the fabric of reality.
All this fun is powered by algorithms -- as, increasingly, is our daily life. From the Google algorithms to the algos that give you “recommendations” online to those that automatically play the stock markets (and sometimes crash them): we may not realize it, but we live in the algoworld.
He says: "The quickest way to find out what the boundaries of reality are is to figure where they break."
Kevin Slavin | Speaker | TED.com