TEDGlobal 2011
Kevin Slavin: How algorithms shape our world
Kevin Slavin: Kako algoritmi oblikuju naš svijet
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Kevin Slavin tvrdi da živimo u svijetu dizajniranom za -- te sve više kontroliranom od -- algoritama. U ovome govoru s TEDGlobal-a koji će vas prikovati, on pokazuje kako kompleksni računalni programi određuju: špijunske taktike, cijene dionica, filmske scenarije i arhitekturu. Upozorava da pišemo kod koji ne možemo razumjeti, s posljedicama koje ne možemo kontrolirati.
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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od umjetnika Michaela Najjara,
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 otišao u Argentinu
00:23
to take the photo.
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kako bi uslikao ovu fotografiju.
00:25
But it's also a fiction. There's a lot of work that went into it after that.
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No ona je i fikcija. Mnogo posla je još uloženo nakon toga.
00:28
And what he's done
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A ono što je učinio
00:30
is he's actually reshaped, digitally,
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je da je zapravo preoblikovao, digitalno,
00:32
all of the contours of the mountains
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sve konture planina
00:34
to follow the vicissitudes of the Dow Jones index.
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kako bi pratio promjene Dow Jones indeksa.
00:37
So what you see,
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Stoga ono što vidite,
00:39
that precipice, that high precipice with the valley,
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da litica, da je visoka litica u dolini,
00:41
is the 2008 financial crisis.
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financijska kriza 2008.
00:43
The photo was made
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Fotografija je uslikana
00:45
when we were deep in the valley over there.
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kada smo bili duboko u dolini ondje.
00:47
I don't know where we are now.
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Neznam gdje smo sada.
00:49
This is the Hang Seng index
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Ovo je Hang Seng index
00:51
for Hong Kong.
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za Hong Kong.
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. Ovo je metafora.
01:00
But I think the point is
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No mislim da je poanta u
01:02
that this is metaphor with teeth,
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tome da su ovo metafore sa zubima.
01:04
and it's with those teeth that I want to propose today
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I s ovim zubima želim danas predložiti
01:07
that we rethink a little bit
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da promislimo malo
01:09
about the role of contemporary math --
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o ulozi suvremene matematike --
01:12
not just financial math, but math in general.
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ne samo financijske matematike, već matematike općenito.
01:15
That its transition
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Da je ona tranzicija
01:17
from being something that we extract and derive from the world
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od nečega što smo istisnuli i izvukli iz svijeta
01:20
to something that actually starts to shape it --
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do nečega što zapravo počinje oblikovati
01:23
the world around us and the world inside us.
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svijet oko nas i svijet unutar nas.
01:26
And it's specifically algorithms,
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To se posebno odnosi na algoritme,
01:28
which are basically the math
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koji su zapravo matematika
01:30
that computers use to decide stuff.
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koju računala koriste kako bi odlučili o nečemu.
01:33
They acquire the sensibility of truth
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Oni stječu senzibilitet 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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Oni se okoštavaju i kalcificiraju,
01:40
and they become real.
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te postaju stvarni.
01:42
And I was thinking about this, of all places,
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O ovome sam razmišljao, od svih mjesta,
01:45
on a transatlantic flight a couple of years ago,
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na prekoatlantskom letu prije nekoliko godina,
01:48
because I happened to be seated
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zato jer sam slučajno dobio mjesto
01:50
next to a Hungarian physicist about my age
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kraj mađarskog fizičara mojih godina
01:52
and we were talking
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i pričali smo o
01:54
about what life was like during the Cold War
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tome kakav je bio život tokom hladnog rata
01:56
for physicists in Hungary.
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za fizičare u Mađarskoj.
01:58
And I said, "So what were you doing?"
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Pitao sam, "I što ste radili?"
02:00
And he said, "Well we were mostly breaking stealth."
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On odgovara, "Više manje smo razbijali nevidljivost."
02:02
And I said, "That's a good job. That's interesting.
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Odgovaram, "To je dobar posao. To je zanimljivo.
02:04
How does that work?"
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Kako to funkcionira?"
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 malo razumjeti kako nevidljivost funkcionira.
02:11
And so -- this is an over-simplification --
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I tako -- ovo je pojednostavljenje --
02:14
but basically, it's not like
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no u osnovi, nije samo da
02:16
you can just pass a radar signal
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može proći radarski signal
02:18
right through 156 tons of steel in the sky.
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kroz 156 tona čelika u zraku.
02:21
It's not just going to disappear.
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Neće samo odjedanput nestati.
02:24
But if you can take this big, massive thing,
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No ukoliko možete uzeti ovu veliku, masivnu stvar,
02:27
and you could turn it into
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i možete ju pretvoriti u
02:30
a million little things --
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milijun 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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onda zapravo radar koji to traži
02:36
has to be able to see
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mora biti u mogućnosti da vidi
02:38
every flock of birds in the sky.
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sva jata ptica u zraku.
02:40
And if you're a radar, that's a really bad job.
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A ukoliko ste radar, to je stvarno težak posao.
02:44
And he said, "Yeah." He said, "But that's if you're a radar.
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On odgovara, "Da." Kaže on, "No to ako si radar."
02:47
So we didn't use a radar;
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Stoga nismo koristili radar;
02:49
we built a black box that was looking for electrical signals,
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izradili smo crnu kutiju koja traži električne signale,
02:52
electronic communication.
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elektroničku komunikaciju.
02:55
And whenever we saw a flock of birds that had electronic communication,
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I svaki put kada smo vidjeli jato ptica koje ima elektroničku komunikaciju,
02:58
we thought, 'Probably has something to do with the Americans.'"
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mislili smo kako vjerovatno ima nekakve veze s amerikancima."
03:01
And I said, "Yeah.
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"Da" kažem ja.
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 efikasno negirali
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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Koji vam je drugi čin?
03:11
What do you do when you grow up?"
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Što radite nakon što odrastete?"
03:13
And he said,
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Odgovara on,
03:15
"Well, financial services."
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"Pa, financijske usluge."
03:17
And I said, "Oh."
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"Oh," kažem ja.
03:19
Because those had been in the news lately.
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Zato jer toga vidimo po vijestima u zadnje vrijeme.
03:22
And I said, "How does that work?"
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Kažem ja, "I kako to funkcionira?"
03:24
And he said, "Well there's 2,000 physicists on Wall Street now,
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Odgovara on, "Trenutno je 2.000 fizičara na Wall Street-u,
03:26
and I'm one of them."
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a ja sam jedan od njih."
03:28
And I said, "What's the black box for Wall Street?"
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Kažem ja, "I što je crna kutija za Wall Street?"
03:31
And he said, "It's funny you ask that,
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Odgovara on, "Smiješno što me tako pitaš,
03:33
because it's actually called black box trading.
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jer se zapravo zove trgovanje crnom kutijom.
03:36
And it's also sometimes called algo trading,
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Nekada se još naziva 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 djelomično evoluiralo
03:44
because institutional traders have the same problems
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iz razloga što su institucionalni 'trejderi' imali iste probleme
03:47
that the United States Air Force had,
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koje je imalo Američko zrakoplovstvo,
03:50
which is that they're moving these positions --
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gdje oni zapravo premještaju ove pozicije --
03:53
whether it's Proctor & Gamble or Accenture, whatever --
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nebitno radi li se o Proctor & Gamble-u ili Accenturu --
03:55
they're moving a million shares of something
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oni premještaju milijun udjela 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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Te ukoliko sve to naprave odjednom,
04:01
it's like playing poker and going all in right away.
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to je kao da igrate poker i odmah sve ulažete.
04:03
You just tip your hand.
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Zapravo ste pokazali svoje karte.
04:05
And so they have to find a way --
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Stoga moraju pronaći način --
04:07
and they use algorithms to do this --
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i koriste algoritme kako bi to učinili --
04:09
to break up that big thing
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da razbijete tu veliku stvar
04:11
into a million little transactions.
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na milijun malih transakcija.
04:13
And the magic and the horror of that
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Magija i horor iza toga
04:15
is that the same math
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je da ista ta matematika
04:17
that you use to break up the big thing
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koju koristite da razbijete tu veliku stvar
04:19
into a million little things
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na milijun malih stvari
04:21
can be used to find a million little things
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može se koristiti za pronalaženje milijuna malih stvari
04:23
and sew them back together
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koje spajate natrag zajedno
04:25
and figure out what's actually happening in the market.
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i odgonetnete što se zapravo događa na tržištu.
04:27
So if you need to have some image
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Stoga ako trebate imati neku sliku
04:29
of what's happening in the stock market right now,
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o tome što se trenutno događa na tržištu vrijednosnica,
04:32
what you can picture is a bunch of algorithms
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ono što možete zamisliti je hrpa algoritama
04:34
that are basically programmed to hide,
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koji su u biti programirani da se sakriju,
04:37
and a bunch of algorithms that are programmed to go find them and act.
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i hrpa algoritama koja je programirana da ih pronađe i djeluje.
04:40
And all of that's great, and it's fine.
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I to je sve super, u redu je.
04:43
And that's 70 percent
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I to se odnosi na 70 posto
04:45
of the United States stock market,
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tržišta vrijednosnica u Sjedinjenim Državama.
04:47
70 percent of the operating system
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70 posto od operativnog sustava
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formerly known as your pension,
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poznatog kao vaša mirovina,
04:52
your mortgage.
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vaša hipoteka.
04:55
And what could go wrong?
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Što može poći po zlu?
04:57
What could go wrong
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Ono što može poći po zlu
04:59
is that a year ago,
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je da prije godinu dana,
05:01
nine percent of the entire market just disappears in five minutes,
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devet posto cijelog tržišta samo je nestalo u pet minuta,
05:04
and they called it the Flash Crash of 2:45.
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i nazivaju ga 'flash crash' od 2:45
05:07
All of a sudden, nine percent just goes away,
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Odjednom, devet posto samo nestane,
05:10
and nobody to this day
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i nitko do danas
05:12
can even agree on what happened
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se ne može složiti što se zapravo dogodilo,
05:14
because nobody ordered it, nobody asked for it.
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jer nitko nije to naručio, nitko nije zatražio.
05:17
Nobody had any control over what was actually happening.
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Nitko nije imaju bilo kakvu kontrolu nad onime što se zapravo događalo.
05:20
All they had
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Sve što su imali
05:22
was just a monitor in front of them
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je monitor ispred njih
05:24
that had the numbers on it
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koji je prikazivao brojeve
05:26
and just a red button
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i crveno dugme
05:28
that said, "Stop."
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na kojemu piše, "Stop."
05:30
And that's the thing,
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O tome se radi,
05:32
is that we're writing things,
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da pišemo stvari,
05:34
we're writing these things that we can no longer read.
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pišemo te stvari koje više ne možemo pročitati.
05:37
And we've rendered something
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Napravili smo nešto
05:39
illegible,
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nečitko.
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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što se zapravo događa
05:46
in this world that we've made.
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u ovome svijetu koji smo stvorili.
05:48
And we're starting to make our way.
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Počinjemo stvarati svoj put.
05:50
There's a company in Boston called Nanex,
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Postoji kompanija u Bostonu pod imenom Nanex,
05:53
and they use math and magic
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koja koristi matematiku i magiju
05:55
and I don't know what,
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i tko zna što drugo,
05:57
and they reach into all the market data
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posežu za svim podacima s tržišta
05:59
and they find, actually sometimes, some of these algorithms.
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i pronađu, nekada, neke od ovih algoritama.
06:02
And when they find them they pull them out
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I kada ih nađu izvuku ih van
06:05
and they pin them to the wall like butterflies.
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i zakvače ih za zid kao leptire.
06:08
And they do what we've always done
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I učine, ono što uvijek činimo
06:10
when confronted with huge amounts of data that we don't understand --
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kad se suočavamo s velikom količinom podataka koju ne razumijemo --
06:13
which is that they give them a name
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a to je da im daju 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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Ovo je jedan kojeg su našli,
06:19
they called the Knife,
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zovu ga Nož,
06:23
the Carnival,
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Karneval,
06:25
the Boston Shuffler,
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Bostonski prevrtljivac,
06:29
Twilight.
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Sumrak.
06:31
And the gag is
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I problem je taj,
06:33
that, of course, these aren't just running through the market.
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naravno, da ovi ne prolaze samo kroz tržište.
06:36
You can find these kinds of things wherever you look,
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Možete naći ovakve stvari gdjegod pogledate,
06:39
once you learn how to look for them.
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jednom kad naučite kako ih pronaći.
06:41
You can find it here: this book about flies
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Možete ih naći ovdje: na ovoj knjizi o muhama
06:44
that you may have been looking at on Amazon.
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koju ste možda tražili na Amazonu.
06:46
You may have noticed it
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Možda ste primjetili
06:48
when its price started at 1.7 million dollars.
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kada joj je početna cijena bila 1,7 milijuna dolara.
06:50
It's out of print -- still ...
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Još uvijek je izvan tiska --
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 ju kupili za 1,7, to bi bilo jeftino.
06:57
A few hours later, it had gone up
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Nekoliko sati poslije, porasla je
06:59
to 23.6 million dollars,
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na 23,6 milijuna dolara,
07:01
plus shipping and handling.
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plus troškovi transporta i rukovanja.
07:03
And the question is:
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I pitanje je:
07:05
Nobody was buying or selling anything; what was happening?
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Nitko nije kupovao ni prodavao ništa; što se događalo?
07:07
And you see this behavior on Amazon
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Vidite ovo ponašanje na Amazonu
07:09
as surely as you see it on Wall Street.
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jednako kao što vidite na Wall Streetu.
07:11
And when you see this kind of behavior,
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I kada vidite ovakvo ponašanje,
07:13
what you see is the evidence
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vidite dokaz
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 zatvorenih u petlje jedne s drugima,
07:19
without any human oversight,
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bez ljudskog nadzora,
07:21
without any adult supervision
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bez nadzora odrasle osobe
07:24
to say, "Actually, 1.7 million is plenty."
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koja kaže, "Zapravo, 1,7 milijuna je puno."
07:27
(Laughter)
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(Smijeh)
07:30
And as with Amazon, so it is with Netflix.
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Kao i s Amazonom, tako je i s Netflixom.
07:33
And so Netflix has gone through
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Netflix je prošao kroz
07:35
several different algorithms over the years.
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nekoliko različitih algoritama tokom godina.
07:37
They started with Cinematch, and they've tried a bunch of others --
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Počeli su s 'Cinematch', a postoje i mnogi drugi.
07:40
there's Dinosaur Planet; there's Gravity.
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Imate Dinaosaurov planet, tu je Gravitacija.
07:42
They're using Pragmatic Chaos now.
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Trenutno koriste Pragmatični Kaos.
07:44
Pragmatic Chaos is, like all of Netflix algorithms,
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Pragmatični Kaos, kao i svi Netflixovi algoritmi,
07:46
trying to do the same thing.
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pokušava činiti istu stvar.
07:48
It's trying to get a grasp on you,
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Pokušava vas dokučiti,
07:50
on the firmware inside the human skull,
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sistem unutar ljudske lubanje,
07:52
so that it can recommend what movie
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tako da bi mogao preporučiti koji film
07:54
you might want to watch next --
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možda želite gledati --
07:56
which is a very, very difficult problem.
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što je vrlo, vrlo težak problem.
07:59
But the difficulty of the problem
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No težina problema
08:01
and the fact that we don't really quite have it down,
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i činjenica da zapravo još nismo na čisto,
08:04
it doesn't take away
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ne osporava
08:06
from the effects Pragmatic Chaos has.
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efekte koje ima Pragmatični Kaos.
08:08
Pragmatic Chaos, like all Netflix algorithms,
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Pragmatični Kaos, kao svi Netflixovi algoritmi,
08:11
determines, in the end,
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određuje, u konačnici,
08:13
60 percent
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60 posto
08:15
of what movies end up being rented.
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filmova koji se iznajme.
08:17
So one piece of code
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Stoga jedan komad koda
08:19
with one idea about you
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s jednom idejom o vama
08:22
is responsible for 60 percent of those movies.
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je odgovoran za 60 posto tih filmova.
08:25
But what if you could rate those movies
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No što ukoliko biste mogli ocjeniti te filmove
08:27
before they get made?
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prije nego što se naprave?
08:29
Wouldn't that be handy?
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Ne bi li to bilo zgodno?
08:31
Well, a few data scientists from the U.K. are in Hollywood,
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Pa, nekolicina podatkovnih znanstvenika iz U.K. su u Hollywoodu,
08:34
and they have "story algorithms" --
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i imaju algoritme priče --
08:36
a company called Epagogix.
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kompanija pod imenom Epagogix.
08:38
And you can run your script through there,
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I možete provući svoj scenario ovdje,
08:41
and they can tell you, quantifiably,
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i mogu vam reći, kvantificirano,
08:43
that that's a 30 million dollar movie
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da je to film od 30 milijuna dolara
08:45
or a 200 million dollar movie.
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ili film od 200 milijuna dolara.
08:47
And the thing is, is that this isn't Google.
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A radi se o tome da ovo nije Google.
08:49
This isn't information.
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Ovo nije informacija.
08:51
These aren't financial stats; this is culture.
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Ovo nisu financijski pokazatelji; ovo je kultura.
08:53
And what you see here,
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Ono što ovdje vidite,
08:55
or what you don't really see normally,
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ili što normalno ne vidite,
08:57
is that these are the physics of culture.
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je da je ovo fizika kulture.
09:01
And if these algorithms,
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I ukoliko se ovi algoritmi,
09:03
like the algorithms on Wall Street,
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kao algoritmi na Wall Street-u,
09:05
just crashed one day and went awry,
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jednoga dana sruše i odu naopako,
09:08
how would we know?
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kako bismo znali,
09:10
What would it look like?
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kako bi to izgledalo?
09:12
And they're in your house. They're in your house.
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I oni su u vašim kućama. Oni su u vašim kućama.
09:15
These are two algorithms competing for your living room.
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Ovo su dva algoritma koji se natječu za vaš dnevni boravak.
09:17
These are two different cleaning robots
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Ovo su dva različita robota spremača
09:19
that have very different ideas about what clean means.
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koji imaju vrlo različite ideje o tome što znači čisto.
09:22
And you can see it
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I možete to vidjeti
09:24
if you slow it down and attach lights to them,
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ukoliko ih usporite i zakvačite svijetlo na njih.
09:27
and they're sort of like secret architects in your bedroom.
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I oni su kao tajni arhitekti u vašoj spavačoj sobi.
09:30
And the idea that architecture itself
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Te ideja da je arhitektura sama po sebi
09:33
is somehow subject to algorithmic optimization
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na neki način subjekt algoritamske optimizacije
09:35
is not far-fetched.
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nije daleka.
09:37
It's super-real and it's happening around you.
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To je super stvarno i događa se oko vas.
09:40
You feel it most
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Najviše možete osjetiti
09:42
when you're in a sealed metal box,
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kada ste u zatvorenoj metalnoj kutiji,
09:44
a new-style elevator;
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dizala novog stila,
09:46
they're called destination-control elevators.
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nazivaju se dizala kontrole destinacije.
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 ona na kojima morate prisitsnuti na koji kat želite ići
09:51
before you get in the elevator.
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prije nego uđete u dizalo.
09:53
And it uses what's called a bin-packing algorithm.
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I koristi tzv. algoritam pakiranja kutije.
09:55
So none of this mishegas
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Stoga ništa od ovih besmislica
09:57
of letting everybody go into whatever car they want.
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dopuštanja svima ulazak u vozilo koje žele.
09:59
Everybody who wants to go to the 10th floor goes into car two,
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Svatko to želi ići na 10 kat ulazi u vozilo dva,
10:01
and everybody who wants to go to the third floor goes into car five.
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a svatko tko želi ići na treći kat ulazi u vozilo pet.
10:04
And the problem with that
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A problem s time je
10:06
is that people freak out.
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da ljudi polude.
10:08
People panic.
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Ljudi paniče.
10:10
And you see why. You see why.
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I možete vidjeti zašto. Vidite zašto.
10:12
It's because the elevator
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To je zato jer dizalu
10:14
is missing some important instrumentation, like the buttons.
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nedostaju neki važni instrumenti, kao dugmad.
10:17
(Laughter)
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(Smijeh)
10:19
Like the things that people use.
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Nešto što ljudi koriste.
10:21
All it has
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Jedino što ima
10:23
is just the number that moves up or down
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je broj koji se kreće gore ili dolje
10:26
and that red button that says, "Stop."
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i crveno dugme koje kaže, "Stop."
10:29
And this is what we're designing for.
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I zbog toga dizajniramo.
10:32
We're designing
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Mi dizajniramo
10:34
for this machine dialect.
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za ovaj dijalekt strojeva.
10:36
And how far can you take that? How far can you take it?
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I koliko daleko možete ići s time? Koliko daleko možete otići?
10:39
You can take it really, really far.
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Možete otići jako, jako daleko.
10:41
So let me take it back to Wall Street.
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Vratimo se nazad na Wall Street.
10:45
Because the algorithms of Wall Street
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Jer algoritmi s Wall Streeta
10:47
are dependent on one quality above all else,
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ovisni su o jednoj kvaliteti iznad svega,
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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I oni rade na milisekundama i mikrosekundama.
10:55
And just to give you a sense of what microseconds are,
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Da dobijete osjećaj što su mikrosekunde,
10:57
it takes you 500,000 microseconds
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potrebno je 500.000 mikrosekundi
10:59
just to click a mouse.
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kako bi kliknuli miša.
11:01
But if you're a Wall Street algorithm
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No ako ste algoritam s Wall Street-a
11:03
and you're five microseconds behind,
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i zaostajete pet 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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Stoga da ste algoritam,
11:09
you'd look for an architect like the one that I met in Frankfurt
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potražili bi arhitekta kao onoga što sam sreo u Frankfurtu
11:12
who was hollowing out a skyscraper --
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koji je ispražnjavao neboder --
11:14
throwing out all the furniture, all the infrastructure for human use,
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izbacujući sav namještaj, svu infrastrukturu potrebnu čovjeku,
11:17
and just running steel on the floors
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i samo ostavljajući čelik na podovima
11:20
to get ready for the stacks of servers to go in --
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spremajući ga za postavljanje servera --
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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bio bliže Internetu.
11:28
And you think of the Internet as this kind of distributed system.
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A vi zamišljate Internet kao sistem distribucije.
11:31
And of course, it is, but it's distributed from places.
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Naravno, on to je, ali se distribuira s određenog mjesta.
11:34
In New York, this is where it's distributed from:
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U New Yorku, odavde se distribuira:
11:36
the Carrier Hotel
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Carrier Hotel
11:38
located on Hudson Street.
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smješten u Hudson ulici.
11:40
And this is really where the wires come right up into the city.
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I ovdje je zapravo mjesto gdje žice izlaze van u grad.
11:43
And the reality is that the further away you are from that,
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I stvarnost je ta da što ste dalje od toga,
11:47
you're a few microseconds behind every time.
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vi ste nekoliko mikrosekundi iza svaki put.
11:49
These guys down on Wall Street,
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Ovi momci dolje s Wall Streeta,
11:51
Marco Polo and Cherokee Nation,
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Marko Polo i Cherokee Nacija,
11:53
they're eight microseconds
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oni su osam mikrosekundi
11:55
behind all these guys
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iza svih ovih drugih
11:57
going into the empty buildings being hollowed out
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koji se nalaze u ovim praznim ispraznjenim zgradama
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 se nastaviti događati.
12:06
We're going to keep hollowing them out,
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Nastavit ćemo ih ispražnjavati
12:08
because you, inch for inch
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zato jer, inč po inč
12:11
and pound for pound and dollar for dollar,
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i funtu za funtu i dolar za dolar,
12:14
none of you could squeeze revenue out of that space
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nitko od vas ne može iscjediti prihod iz prostora
12:17
like the Boston Shuffler could.
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kao što to može Bostonski Prevrtljivac.
12:20
But if you zoom out,
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No ako odzumirate
12:22
if you zoom out,
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ako odzumirate,
12:24
you would see an 825-mile trench
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vidjeli bi rov od 825 milja
12:28
between New York City and Chicago
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između New Yorka i Chicaga
12:30
that's been built over the last few years
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koji se gradi zadnjih nekoliko godina
12:32
by a company called Spread Networks.
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od kompanije Spread Networks.
12:35
This is a fiber optic cable
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Ovo je svjetlosni optički kabel
12:37
that was laid between those two cities
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koji je položen između ova dva grada
12:39
to just be able to traffic one signal
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kako bi bili u mogućnosti poslati samo jedan signal
12:42
37 times faster than you can click a mouse --
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37 puta brže nego što možete kliknuti mišem --
12:45
just for these algorithms,
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samo za ove algoritme,
12:48
just for the Carnival and the Knife.
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samo za Karneval i Nož.
12:51
And when you think about this,
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I kada razmislite o ovome,
12:53
that we're running through the United States
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da prolazimo kroz Sjedinjene Države
12:55
with dynamite and rock saws
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s dinamitom i pilama za kamen
12:58
so that an algorithm can close the deal
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kako bi algoritam mogao zatvoriti poziciju
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 sistem
13:05
that no human will ever know,
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za koji čovjek nikad neće znati,
13:09
that's a kind of manifest destiny;
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to je kao manifest sudbine
13:12
and we'll always look for a new frontier.
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i uvijek će tražiti nove granice.
13:15
Unfortunately, we have our work cut out for us.
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Nažalost, mi smo izostavljeni iz ovog posla.
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 neki matematičari sa MIT-a.
13:22
And the truth is I don't really understand
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Istina je da nažalost ne razumijem
13:24
a lot of what they're talking about.
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mnogo toga o čemu pričaju.
13:26
It involves light cones and quantum entanglement,
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Uključuje svjetlosni stožac i kvantnu zapreku,
13:29
and I don't really understand any of that.
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i ne razumijem ništa od toga.
13:31
But I can read this map,
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Ali mogu čitati ovu kartu.
13:33
and what this map says
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A ono o čemu ova karta govori je da
13:35
is that, if you're trying to make money on the markets where the red dots are,
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ukoliko želite zaraditi novac na tržištima gdje se nalaze crvene točke,
13:38
that's where people are, where the cities are,
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tamo gdje se nalaze ljudi, gradovi,
13:40
you're going to have to put the servers where the blue dots are
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morat ćete postaviti servere tamo gdje se nalaze plave točke
13:43
to do that most effectively.
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kako bi bili što efikasniji.
13:45
And the thing that you might have noticed about those blue dots
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Ono što ste mogli zamjetiti na plavim točkama
13:48
is that a lot of them are in the middle of the ocean.
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je da se mnogo njih nalaze usred oceana.
13:51
So that's what we'll do: we'll build bubbles or something,
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I to je što ćemo napraviti, sagradit ćemo balone ili nešto drugo,
13:54
or platforms.
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ili platforme.
13:56
We'll actually part the water
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Mi ćemo zapravo razdvajati vodu
13:58
to pull money out of the air,
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kako bi izvukli novac iz zraka
14:00
because it's a bright future
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zato jer je to sjajna 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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I ne radi se o novcu da je toliko interesantan zapravo.
14:09
It's what the money motivates,
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Radi se o tome što novac motivira.
14:11
that we're actually terraforming
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Mi zapravo teraformiramo
14:13
the Earth itself
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samu Zemlju
14:15
with this kind of algorithmic efficiency.
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s ovom algoritamskom efikasnošću.
14:17
And in that light,
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U tome svjetlu,
14:19
you go back
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vratite se nazad
14:21
and you look at Michael Najjar's photographs,
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i pogledajte fotografije Michaela Najjara,
14:23
and you realize that they're not metaphor, they're prophecy.
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i shvatite da one nisu metafora, one su proročanstvo.
14:26
They're prophecy
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One su proročanstvo
14:28
for the kind of seismic, terrestrial effects
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za ove seizmičke, zemaljske učinke
14:32
of the math that we're making.
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matematike koju stvaramo.
14:34
And the landscape was always made
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I pejzaž je uvijek bio načinjen
14:37
by this sort of weird, uneasy collaboration
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od ove čudne, nelagodne suradnje
14:40
between nature and man.
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između prirode i čovjeka.
14:43
But now there's this third co-evolutionary force: algorithms --
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No sada imamo i treću ko-evolucijsku silu: algoritme --
14:46
the Boston Shuffler, the Carnival.
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Bostonskog Prevrtljivca, Karneval.
14:49
And we will have to understand those as nature,
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I morat ćemo ih shvatiti kao prirodu.
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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(Pljesak)
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