TEDSalon Berlin 2014
Kenneth Cukier: Big data is better data
Kenet Kukir (Kenneth Cukier): Veliki podaci su bolji podaci
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Samoupravljajuća vozila su bila samo početak. Šta je budućnost tehnologije i dizajna potpomognutih velikim podacima? Kroz uzbudljiv naučni govor, Kenet Kukir razmatra šta je sledeće za mašinsko učenje - i ljudsko saznanje.
Kenneth Cukier - Data Editor of The Economist
Kenneth Cukier is the Data Editor of The Economist. From 2007 to 2012 he was the Tokyo correspondent, and before that, the paper’s technology correspondent in London, where his work focused on innovation, intellectual property and Internet governance. Kenneth is also the co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think with Viktor Mayer-Schönberger in 2013, which was a New York Times Bestseller and translated into 16 languages. Full bio
Kenneth Cukier is the Data Editor of The Economist. From 2007 to 2012 he was the Tokyo correspondent, and before that, the paper’s technology correspondent in London, where his work focused on innovation, intellectual property and Internet governance. Kenneth is also the co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think with Viktor Mayer-Schönberger in 2013, which was a New York Times Bestseller and translated into 16 languages. Full bio
Double-click the English transcript below to play the video.
00:12
America's favorite pie is?
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Koja je omiljena američka pita?
00:16
Audience: Apple.
Kenneth Cukier: Apple. Of course it is.
Kenneth Cukier: Apple. Of course it is.
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Publika: Od jabuke.
Kenet Kukir: Od jabuke. Naravno.
Kenet Kukir: Od jabuke. Naravno.
Kako to znamo?
00:20
How do we know it?
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00:21
Because of data.
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Zbog podataka.
00:24
You look at supermarket sales.
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Posmatramo rasprodaju u supermarketima,
00:26
You look at supermarket
sales of 30-centimeter pies
sales of 30-centimeter pies
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prodaju zamrznutih pita prečnika 30 cm,
00:29
that are frozen, and apple wins, no contest.
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i jabuka pobeđuje.
Bez konkurencije.
Najveći deo prodaje je od jabuka.
00:33
The majority of the sales are apple.
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00:38
But then supermarkets started selling
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Zatim su supermarketi
počeli da prodaju manje pite,
počeli da prodaju manje pite,
00:41
smaller, 11-centimeter pies,
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pite prečnika 11 cm.
00:43
and suddenly, apple fell to fourth or fifth place.
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Odjednom, jabuka pada
na četvrto ili peto mesto.
na četvrto ili peto mesto.
00:48
Why? What happened?
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Zašto? Šta se dogodilo?
00:50
Okay, think about it.
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Dobro. Razmislite o tome.
00:53
When you buy a 30-centimeter pie,
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Kada kupite pitu od 30cm,
00:57
the whole family has to agree,
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cela porodica mora da se složi,
00:59
and apple is everyone's second favorite.
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a pita od jabuka je svima
drugi omiljeni izbor.
drugi omiljeni izbor.
01:03
(Laughter)
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(Smeh)
01:05
But when you buy an individual 11-centimeter pie,
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Ali kad kupite zasebnu pitu od 11cm,
01:09
you can buy the one that you want.
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možete da kupite onu koju vi hoćete.
01:12
You can get your first choice.
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Možete da uzmete vaš prvi izbor.
01:16
You have more data.
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Imate više podataka.
01:18
You can see something
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Možete da vidite nešto
01:20
that you couldn't see
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što niste mogli da vidite
kada ste ih imali u manjim količinama.
kada ste ih imali u manjim količinama.
01:21
when you only had smaller amounts of it.
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Dakle, poenta je da više podataka
01:25
Now, the point here is that more data
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01:27
doesn't just let us see more,
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ne samo što nam omogućava da vidimo više,
01:29
more of the same thing we were looking at.
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više o tome što posmatramo.
01:31
More data allows us to see new.
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Više podataka nam omogućava
da vidimo novo.
da vidimo novo.
01:35
It allows us to see better.
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Omogućava nam da vidimo bolje.
01:38
It allows us to see different.
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Omogućava nam da vidimo različito.
01:42
In this case, it allows us to see
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U ovom slučaju, omogućava nam da vidimo
01:45
what America's favorite pie is:
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koja je omiljena američka pita:
01:48
not apple.
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nije od jabuka.
01:50
Now, you probably all have heard the term big data.
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Svi ste verovatno čuli izraz
"veliki podaci".
"veliki podaci".
01:54
In fact, you're probably sick of hearing the term
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Verovatno vam je i loše
na pomenu izraza
na pomenu izraza
01:56
big data.
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"veliki podaci".
01:58
It is true that there is a lot of hype around the term,
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Tačno je da se podigla velika buka
oko ovog izraza,
oko ovog izraza,
02:01
and that is very unfortunate,
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što je loše.
02:03
because big data is an extremely important tool
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Zato što su veliki podaci veoma važan alat
02:06
by which society is going to advance.
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pomoću kog će društvo da napreduje.
02:10
In the past, we used to look at small data
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U prošlosti smo posmatrali "male podatke"
02:14
and think about what it would mean
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i razmišljali o tome šta bi značilo
02:15
to try to understand the world,
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da pokušamo da razumemo svet,
02:17
and now we have a lot more of it,
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a sada ih imamo mnogo više,
02:19
more than we ever could before.
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više nego što smo ikada imali.
02:22
What we find is that when we have
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Shvatili smo da kada imamo
mnogo podataka,
mnogo podataka,
02:23
a large body of data, we can fundamentally do things
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u principu možemo uraditi stvari
02:26
that we couldn't do when we
only had smaller amounts.
only had smaller amounts.
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koje nismo mogli sa manje podataka.
02:29
Big data is important, and big data is new,
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Veliki podaci su bitni,
i to je nešto novo,
i to je nešto novo,
02:32
and when you think about it,
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kada razmislimo o tome,
02:34
the only way this planet is going to deal
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jedini način na koji će se planeta suočiti
02:36
with its global challenges —
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sa svojim globalnim izazovima -
02:38
to feed people, supply them with medical care,
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nahraniti ljude, obezbediti im
medicinsku negu,
medicinsku negu,
02:41
supply them with energy, electricity,
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pružiti im energiju, struju,
da se pobrine da ne izgore
02:44
and to make sure they're not burnt to a crisp
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02:46
because of global warming —
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zbog globalnog zagrevanja -
02:47
is because of the effective use of data.
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jeste zbog efikasne upotrebe podataka.
02:51
So what is new about big
data? What is the big deal?
data? What is the big deal?
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Šta je novo u vezi sa velikim podacima?
U čemu je velika caka?
U čemu je velika caka?
02:55
Well, to answer that question, let's think about
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Da bismo odgovorili na to pitanje,
razmislimo kako su informacije izgledale,
02:58
what information looked like,
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03:00
physically looked like in the past.
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fizički izgledale u prošlosti.
03:03
In 1908, on the island of Crete,
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1908. godine na Kritu,
03:06
archaeologists discovered a clay disc.
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arheolozi su pronašli glineni disk.
03:11
They dated it from 2000 B.C., so it's 4,000 years old.
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Smestili su ga oko 2000. g. pre Hrista,
dakle star je 4000 godina.
dakle star je 4000 godina.
03:15
Now, there's inscriptions on this disc,
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Na tom disku postoji zapis,
03:17
but we actually don't know what it means.
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ali ne znamo šta on znači.
03:18
It's a complete mystery, but the point is that
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Potpuna je zagonetka,
ali poenta je u tome
ali poenta je u tome
03:21
this is what information used to look like
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da su tako informacije izgledale
03:22
4,000 years ago.
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pre 4000 godina.
03:25
This is how society stored
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Tako je društvo čuvalo
03:27
and transmitted information.
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i prenosilo informacije.
03:31
Now, society hasn't advanced all that much.
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Društvo nije baš toliko napredovalo.
03:35
We still store information on discs,
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I dalje čuvamo informacije na diskovima,
03:38
but now we can store a lot more information,
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ali danas možemo da čuvamo mnogo više,
03:41
more than ever before.
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više nego ikada.
03:43
Searching it is easier. Copying it easier.
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Pretraživanje je lakše.
Kopiranje je lakše.
Kopiranje je lakše.
03:46
Sharing it is easier. Processing it is easier.
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Deljenje je lakše. Obrada je lakša.
03:49
And what we can do is we can reuse this information
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Možemo da koristimo te informacije iznova,
03:52
for uses that we never even imagined
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na načine na koje nismo ni zamišljali
03:54
when we first collected the data.
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kada smo počeli da sakupljamo podatke.
03:57
In this respect, the data has gone
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U tom smislu,
podaci su prešli iz skladištenja u protok.
03:59
from a stock to a flow,
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04:03
from something that is stationary and static
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Od nečega što je stacionarno i statično
04:07
to something that is fluid and dynamic.
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do nečega što je fluidno i dinamično.
04:10
There is, if you will, a liquidity to information.
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Ako ćemo tako,
informacija je kao tečnost.
informacija je kao tečnost.
04:14
The disc that was discovered off of Crete
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Disk, koji je otkriven u blizini Krita,
04:18
that's 4,000 years old, is heavy,
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pre 4000 godina, je težak.
04:22
it doesn't store a lot of information,
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Ne sadrži puno informacija,
04:24
and that information is unchangeable.
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i te informacije su nepromenljive.
04:27
By contrast, all of the files
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Nasuprot tome, svi fajlovi
04:31
that Edward Snowden took
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koje je Edvard Snouden uzeo
04:33
from the National Security
Agency in the United States
Agency in the United States
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od Državne bezbednosne agencije u SAD-u
04:35
fits on a memory stick
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staju na memorijski uređaj
04:38
the size of a fingernail,
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veličine nokta,
04:41
and it can be shared at the speed of light.
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i mogu se razmenjivati brzinom svetlosti.
04:45
More data. More.
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Još podataka. Više.
Jedan razlog zašto danas
imamo toliko podataka
imamo toliko podataka
04:51
Now, one reason why we have
so much data in the world today
so much data in the world today
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04:53
is we are collecting things
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je što sakupljamo stvari
04:54
that we've always collected information on,
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o kojima smo uvek skupljali informacije,
04:57
but another reason why is we're taking things
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ali drugi razlog je
zato što uzimamo stvari
zato što uzimamo stvari
05:00
that have always been informational
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koje su uvek bile informativne
05:03
but have never been rendered into a data format
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ali nikad nisu prebačene u oblik podataka
05:05
and we are putting it into data.
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i stavljamo ih u podatke.
05:08
Think, for example, the question of location.
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Zamislite, npr. pitanje lokacije.
05:11
Take, for example, Martin Luther.
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Uzmimo Martina Lutera za primer.
05:13
If we wanted to know in the 1500s
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Da smo 1500. god. želeli da znamo
05:15
where Martin Luther was,
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gde je Martin Luter,
05:18
we would have to follow him at all times,
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morali bismo da ga pratimo
u svakom trenutku,
u svakom trenutku,
05:20
maybe with a feathery quill and an inkwell,
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možda sa perom i mastilom,
05:22
and record it,
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i da to beležimo,
05:23
but now think about what it looks like today.
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ali razmislite kako to izgleda danas.
05:26
You know that somewhere,
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Znate da negde,
05:28
probably in a telecommunications carrier's database,
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verovatno u bazi podataka
telefonskog operatera,
telefonskog operatera,
05:30
there is a spreadsheet or at least a database entry
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postoji tabela ili bar podatak u bazi
05:33
that records your information
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koji beleži informaciju
05:35
of where you've been at all times.
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o tome gde ste bili u svakom momentu.
05:37
If you have a cell phone,
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Ako imate mobilni telefon,
05:39
and that cell phone has GPS,
but even if it doesn't have GPS,
but even if it doesn't have GPS,
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koji ima GPS, čak i ako nema GPS,
05:42
it can record your information.
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on čuva informacije.
05:44
In this respect, location has been datafied.
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U ovom smislu,
lokacija je postala "podatkovana".
lokacija je postala "podatkovana".
05:48
Now think, for example, of the issue of posture,
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Razmislimo, npr. o pitanju držanja,
05:53
the way that you are all sitting right now,
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načinu na koji upravo sedite,
05:54
the way that you sit,
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načinu na koji vi sedite,
05:56
the way that you sit, the way that you sit.
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načinu na koji vi sedite, i vi.
05:59
It's all different, and it's a function of your leg length
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Svi se razlikuju, i zavise od dužine nogu
06:01
and your back and the contours of your back,
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i leđa i od konture leđa,
06:03
and if I were to put sensors,
maybe 100 sensors
maybe 100 sensors
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i, ako bih postavio senzore,
možda 100 senzora
možda 100 senzora
06:05
into all of your chairs right now,
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u sve vaše stolice,
06:07
I could create an index that's fairly unique to you,
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našao bih indeks koji je
jedinstven za svakoga,
jedinstven za svakoga,
06:11
sort of like a fingerprint, but it's not your finger.
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kao otisak prsta, ali nije od prsta.
06:15
So what could we do with this?
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Međutim, šta bismo mogli sa tim?
06:18
Researchers in Tokyo are using it
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Istraživači u Tokiju ga koriste
06:21
as a potential anti-theft device in cars.
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kao potencijalni alarmni uređaj u kolima.
06:25
The idea is that the carjacker sits behind the wheel,
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Ideja je da ako za volan sedne lopov,
06:28
tries to stream off, but the car recognizes
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pokuša da pobegne,
ali automobil prepozna
ali automobil prepozna
06:30
that a non-approved driver is behind the wheel,
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da za volanom nije odobreni vozač,
06:32
and maybe the engine just stops, unless you
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možda zaustavi motor, osim ako vozač
06:35
type in a password into the dashboard
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ne unese šifru u kontrolnu tablu
06:38
to say, "Hey, I have authorization to drive." Great.
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da kaže: "Hej, imam dozvolu da vozim".
Odlično!
Odlično!
06:42
What if every single car in Europe
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Šta ako bi svaki automobil u Evropi
06:45
had this technology in it?
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imao ovu tehnologiju?
06:46
What could we do then?
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Šta bismo mogli tada?
06:50
Maybe, if we aggregated the data,
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Možda, kada bismo nagomilali podatke,
06:52
maybe we could identify telltale signs
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mogli bismo da uočimo znakove upozorenja
06:56
that best predict that a car accident
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koji najbolje predviđaju
da će se dogoditi automobilska nesreća
u narednih pet sekundi.
u narednih pet sekundi.
06:58
is going to take place in the next five seconds.
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Tada bismo u obliku podataka beležili
07:04
And then what we will have datafied
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zamor vozača,
07:07
is driver fatigue,
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07:09
and the service would be when the car senses
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i svrha bi bila da kada kola osete
07:11
that the person slumps into that position,
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da je vozač upao u određeni položaj,
07:14
automatically knows, hey, set an internal alarm
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automatski kaže:
"Hej, pusti interni alarm."
"Hej, pusti interni alarm."
kojim bi zavibrirao volan,
zatrubio iznutra i rekao
zatrubio iznutra i rekao
07:18
that would vibrate the steering wheel, honk inside
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07:20
to say, "Hey, wake up,
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"Hej, budi se!
07:22
pay more attention to the road."
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obrati više pažnje na put."
07:24
These are the sorts of things we can do
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To su neke stvari koje možemo da uradimo
07:26
when we datafy more aspects of our lives.
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kada prebacimo u podatke
više aspekata naših života.
više aspekata naših života.
07:29
So what is the value of big data?
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Koja je vrednost velikih podataka?
07:32
Well, think about it.
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Pa, razmislite o tome.
07:35
You have more information.
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Imate više informacija.
Možete da uradite
ono što niste mogli ranije.
ono što niste mogli ranije.
07:37
You can do things that you couldn't do before.
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07:40
One of the most impressive areas
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Jedna od najimpresivnijih oblasti
07:42
where this concept is taking place
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u kojoj ovaj koncept igra ulogu
07:44
is in the area of machine learning.
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jeste mašinsko učenje.
07:47
Machine learning is a branch of artificial intelligence,
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Mašinsko učenje je
grana veštačke inteligencije,
grana veštačke inteligencije,
07:50
which itself is a branch of computer science.
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koja je grana računarskih nauka.
07:53
The general idea is that instead of
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Glavna ideja je da umesto
07:55
instructing a computer what do do,
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da kažemo računaru šta da radi,
07:57
we are going to simply throw data at the problem
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jednostavno ubacimo podatke u problem
08:00
and tell the computer to figure it out for itself.
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i kažemo računaru da ga reši sam.
Pomoći će vam da ga razumete
08:03
And it will help you understand it
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08:05
by seeing its origins.
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gledajući u njegove korene.
08:08
In the 1950s, a computer scientist
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U 1950-im, informatičar u IBM-u,
08:11
at IBM named Arthur Samuel liked to play checkers,
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Artur Semjuel, voleo je da igra "Damu",
te je napisao kompjuterski program
08:14
so he wrote a computer program
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08:16
so he could play against the computer.
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2813
kako bi igrao protiv računara.
08:18
He played. He won.
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Igrao je. Pobedio je.
Igrao je. Pobedio je.
08:21
He played. He won.
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08:23
He played. He won,
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Igrao, pobedio.
08:26
because the computer only knew
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Jer je računar znao dozvoljene poteze.
08:28
what a legal move was.
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Artur Semjuel je znao nešto drugo.
08:30
Arthur Samuel knew something else.
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Artur Semjuel je poznavao strategiju.
08:32
Arthur Samuel knew strategy.
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08:37
So he wrote a small sub-program alongside it
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Napisao je mali potprogram, pored ovog,
08:39
operating in the background, and all it did
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1974
koji je radio u pozadini,
i samo računao verovatnoću
08:41
was score the probability
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1817
da data situacija na tabli pre vodi
08:43
that a given board configuration would likely lead
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2563
08:46
to a winning board versus a losing board
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ka pobedničkoj tabli nego ka gubitničkoj,
08:49
after every move.
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2508
nakon svakog poteza.
08:51
He plays the computer. He wins.
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Igra protiv računara. Pobeđuje.
Igra protiv računara.
08:54
He plays the computer. He wins.
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2508
Pobeđuje.
Igra protiv računara. Pobeđuje.
08:57
He plays the computer. He wins.
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Zatim je Artur Semjuel pustio računar
09:01
And then Arthur Samuel leaves the computer
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2277
da igra protiv sebe.
09:03
to play itself.
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2227
09:05
It plays itself. It collects more data.
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3509
Igrao je. Sakupljao je više podataka.
09:09
It collects more data. It increases
the accuracy of its prediction.
the accuracy of its prediction.
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Sakupljajući više podataka,
povećavao je tačnost svog predviđanja.
povećavao je tačnost svog predviđanja.
09:13
And then Arthur Samuel goes back to the computer
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2104
Zatim se Artur Semjuel vratio do računara.
09:15
and he plays it, and he loses,
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2318
Igra, i gubi.
09:17
and he plays it, and he loses,
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2069
Igra, i gubi.
09:19
and he plays it, and he loses,
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2047
Igra, i gubi.
09:21
and Arthur Samuel has created a machine
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2599
I tako je Artur Semjuel stvorio mašinu
09:24
that surpasses his ability in a task that he taught it.
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6288
koja prevazilazi njegove mogućnosti
u igri kojoj ju je naučio.
u igri kojoj ju je naučio.
09:30
And this idea of machine learning
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2498
Ova ideja mašinskog učenja
09:33
is going everywhere.
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3927
se širi na sve strane.
09:37
How do you think we have self-driving cars?
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565239
3149
Šta mislite,
odakle nam samoupravljajuća vozila?
odakle nam samoupravljajuća vozila?
09:40
Are we any better off as a society
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2137
Da li napredujemo kao društvo
09:42
enshrining all the rules of the road into software?
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3285
ubacivanjem svih pravila vožnje u softver?
09:45
No. Memory is cheaper. No.
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2598
Ne. Memorija je jeftinija. Ne.
09:48
Algorithms are faster. No. Processors are better. No.
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576408
3994
Algoritmi su brži. Ne.
Procesori su brži. Ne.
Procesori su brži. Ne.
Sve to je bitno, ali ne zbog toga.
09:52
All of those things matter, but that's not why.
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2772
09:55
It's because we changed the nature of the problem.
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3141
Nego zato što smo promenili
koren problema.
koren problema.
09:58
We changed the nature of the problem from one
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1530
Promenili smo prirodu problema
09:59
in which we tried to overtly and explicitly
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2245
od one u kojoj smo direktno
objasnili računaru kako da vozi,
10:02
explain to the computer how to drive
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2581
do one u kojoj kažemo:
10:04
to one in which we say,
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1316
10:05
"Here's a lot of data around the vehicle.
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1876
"Evo ti mnogo podataka u vezi sa vozilom.
10:07
You figure it out.
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1533
Shvati sam.
10:09
You figure it out that that is a traffic light,
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1867
Shvati da je ovo svetlo na semaforu.
10:11
that that traffic light is red and not green,
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2081
Da je crveno, a ne zeleno.
10:13
that that means that you need to stop
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601344
2014
Da to znači da moraš da staneš,
10:15
and not go forward."
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3083
a ne da nastaviš."
Mašinsko učenje je u osnovi
10:18
Machine learning is at the basis
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1518
10:19
of many of the things that we do online:
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607959
1991
mnogih stvari na mreži.
10:21
search engines,
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1857
Pretraživači,
Amazonov personalizovani algoritam,
10:23
Amazon's personalization algorithm,
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računarsko prevođenje,
10:27
computer translation,
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2212
10:29
voice recognition systems.
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sistemi za prepoznavanje glasa.
Istraživači su skoro posmatrali
10:34
Researchers recently have looked at
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10:36
the question of biopsies,
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3195
problem biopsije.
Biopsije raka.
10:40
cancerous biopsies,
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2767
10:42
and they've asked the computer to identify
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630907
2315
Pitali su računar da ustanovi
posmatrajući podatke
i stopu preživljavanja,
i stopu preživljavanja,
10:45
by looking at the data and survival rates
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2471
10:47
to determine whether cells are actually
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4667
da odluči da li su ćelije zapravo
10:52
cancerous or not,
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2544
kancerogene ili ne.
10:54
and sure enough, when you throw the data at it,
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1778
Zasigurno, kada ubacite podatke,
10:56
through a machine-learning algorithm,
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2047
pomoću algoritma mašinskog učenja,
10:58
the machine was able to identify
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1877
mašina je postala
sposobna da prepozna
sposobna da prepozna
11:00
the 12 telltale signs that best predict
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648606
2262
12 znakova koji najbolje predviđaju
11:02
that this biopsy of the breast cancer cells
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da je biopsija raka ćelija dojke
11:06
are indeed cancerous.
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zaista zahvaćena rakom.
11:09
The problem: The medical literature
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2498
Problem?
Medicinska literatura je poznavala
samo devet od njih.
samo devet od njih.
11:11
only knew nine of them.
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2789
Tri od tih simptoma su bili oni
11:14
Three of the traits were ones
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662672
1800
11:16
that people didn't need to look for,
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2975
koje ljudi nisu trebali da traže,
11:19
but that the machine spotted.
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5531
ali ih je mašina uočila.
11:24
Now, there are dark sides to big data as well.
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Ali, postoji loša strana velikih podataka.
Unaprediće naše živote,
11:30
It will improve our lives, but there are problems
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2074
ali postoje problemi
kojih moramo biti svesni.
kojih moramo biti svesni.
11:32
that we need to be conscious of,
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2640
11:35
and the first one is the idea
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2623
Prvi od njih je ideja
da možemo biti kažnjeni za predviđanja,
11:38
that we may be punished for predictions,
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11:40
that the police may use big data for their purposes,
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3870
da policija može koristiti velike podatke
u svoje svrhe,
u svoje svrhe,
11:44
a little bit like "Minority Report."
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2351
nešto poput fima "Suvišni izveštaj".
Ovaj izraz zovemo
sposobnost predviđanja
sposobnost predviđanja
11:47
Now, it's a term called predictive policing,
251
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2441
11:49
or algorithmic criminology,
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2363
ili algoritamska kriminologija,
i ideja je da ako uzmemo mnogo podataka
11:51
and the idea is that if we take a lot of data,
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699951
2036
11:53
for example where past crimes have been,
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2159
npr. mesta prošlih zločina,
znamo gde da pošaljemo patrole.
11:56
we know where to send the patrols.
255
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2543
11:58
That makes sense, but the problem, of course,
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706689
2115
To ima smisla, ali problem je, naravno,
u tome što se neće završiti samo
na podacima o lokaciji.
na podacima o lokaciji.
12:00
is that it's not simply going to stop on location data,
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4544
12:05
it's going to go down to the level of the individual.
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2959
Ići će do ličnog nivoa.
Zašto ne koristimo podatke
12:08
Why don't we use data about the person's
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716307
2250
o nečijim ocenama iz srednje škole?
12:10
high school transcript?
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2228
Možda da iskoristimo činjenice
12:12
Maybe we should use the fact that
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1561
12:14
they're unemployed or not, their credit score,
262
722346
2028
o zaposlenosti, o kreditnom stanju,
12:16
their web-surfing behavior,
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724374
1552
o ponašanju na internetu,
da li su budni noću.
12:17
whether they're up late at night.
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725926
1878
Ako njihov Fitbit može da prepozna
njihove biohemijske parametre,
njihove biohemijske parametre,
12:19
Their Fitbit, when it's able
to identify biochemistries,
to identify biochemistries,
265
727804
3161
pokazaće kada imaju agresivne misli.
12:22
will show that they have aggressive thoughts.
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4236
Možemo imati algoritme
12:27
We may have algorithms that are likely to predict
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735201
2221
koji bi mogli predviđati
šta ćemo uraditi,
šta ćemo uraditi,
12:29
what we are about to do,
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1633
12:31
and we may be held accountable
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1244
i mogu nas smatrati odgovornim
12:32
before we've actually acted.
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2590
pre nego što delamo.
12:34
Privacy was the central challenge
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1732
Privatnost je bila centralni izazov
12:36
in a small data era.
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2880
u eri malih podataka.
U danima velikih podataka,
12:39
In the big data age,
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2149
12:41
the challenge will be safeguarding free will,
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4523
izazov će biti zaštita slobodne volje,
moralnih izbora, ljudske volje,
12:46
moral choice, human volition,
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754173
3779
ljudske odlučnosti.
12:49
human agency.
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3068
12:54
There is another problem:
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2225
Postoji još jedan problem.
12:56
Big data is going to steal our jobs.
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764765
3556
Veliki podaci će nam ukrasti poslove.
Veliki podaci i algoritmi će izazvati
13:00
Big data and algorithms are going to challenge
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3512
kancelarijske, visoko obrazovane radnike
13:03
white collar, professional knowledge work
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3061
13:06
in the 21st century
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1653
dvadeset prvog veka
slično kao što su automatizacija
13:08
in the same way that factory automation
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2434
13:10
and the assembly line
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2189
i pokretna traka
izazvale radničku klasu u 20. veku.
13:13
challenged blue collar labor in the 20th century.
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3026
13:16
Think about a lab technician
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2092
Setimo se laboratorijskog tehničara,
13:18
who is looking through a microscope
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786288
1409
koji pod mikroskopom posmatra
13:19
at a cancer biopsy
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787697
1624
biopsiju raka
da bi zaključio da li je zahvaćena rakom.
13:21
and determining whether it's cancerous or not.
288
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2637
13:23
The person went to university.
289
791958
1972
Ova osoba je završila fakultet.
13:25
The person buys property.
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1430
Ona kupuje imovinu.
13:27
He or she votes.
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1741
On ili ona glasa.
On ili ona je član društva.
13:29
He or she is a stakeholder in society.
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3666
Posao ove osobe,
13:32
And that person's job,
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1394
i celog niza stručnjaka
13:34
as well as an entire fleet
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1609
13:35
of professionals like that person,
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1969
kao što je ova osoba,
13:37
is going to find that their jobs are radically changed
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3150
shvatiće da se njihov posao znatno menja
ili da će potpuno nestati.
13:40
or actually completely eliminated.
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2357
13:43
Now, we like to think
298
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1284
Volimo da mislimo
da će vremenom tehnologija praviti poslove
13:44
that technology creates jobs over a period of time
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3187
iza kratkog, privremenog doba dislokacije,
13:47
after a short, temporary period of dislocation,
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815717
3465
13:51
and that is true for the frame of reference
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1941
što je i tačno za taj referentni okvir
u kom svi živimo, industrijsku revoluciju,
13:53
with which we all live, the Industrial Revolution,
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821123
2142
13:55
because that's precisely what happened.
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2328
jer tako se tačno i dogodilo.
13:57
But we forget something in that analysis:
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2333
Međutim, u toj analizi zaboravljamo
13:59
There are some categories of jobs
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1830
da postoje kategorije poslova
14:01
that simply get eliminated and never come back.
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3420
koje će jednostavno nestati
i neće se vratiti.
i neće se vratiti.
14:05
The Industrial Revolution wasn't very good
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833176
2004
Industrijska revolucija nije bila dobra
14:07
if you were a horse.
308
835180
4002
ako ste bili konj.
Dakle, moramo biti pažljivi,
14:11
So we're going to need to be careful
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839182
2055
14:13
and take big data and adjust it for our needs,
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841237
3514
i moramo velike podatke
prilagoditi našim potrebama,
prilagoditi našim potrebama,
14:16
our very human needs.
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844751
3185
našim ljudskim potrebama.
Moramo biti gospodari tehnologije,
14:19
We have to be the master of this technology,
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847936
1954
14:21
not its servant.
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849890
1656
a ne njene sluge.
14:23
We are just at the outset of the big data era,
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851546
2958
Na samom smo početku
doba velikih podataka,
doba velikih podataka,
i iskreno, za sada ne rukujemo dobro
14:26
and honestly, we are not very good
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854504
3150
podacima koje sada možemo da prikupimo.
14:29
at handling all the data that we can now collect.
316
857654
4207
To nije problem samo
Državne bezbednosne agencije.
Državne bezbednosne agencije.
14:33
It's not just a problem for
the National Security Agency.
the National Security Agency.
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3330
Firme sakupljaju dosta podataka,
i takođe ih ne koriste dobro,
i takođe ih ne koriste dobro,
14:37
Businesses collect lots of
data, and they misuse it too,
data, and they misuse it too,
318
865191
3038
moramo ovladati time,
a za to je potrebno vreme.
a za to je potrebno vreme.
14:40
and we need to get better at
this, and this will take time.
this, and this will take time.
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3667
14:43
It's a little bit like the challenge that was faced
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871896
1822
Podseća na situaciju
kada se primitivni čovek suočio sa vatrom.
14:45
by primitive man and fire.
321
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2407
14:48
This is a tool, but this is a tool that,
322
876125
1885
To je alat, ali alat koji će nas opeći
ako ne budemo pažljivi.
14:50
unless we're careful, will burn us.
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3559
14:56
Big data is going to transform how we live,
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884008
3120
Veliki podaci će promeniti
naš način života,
naš način života,
14:59
how we work and how we think.
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887128
2801
način rada i razmišljanja.
15:01
It is going to help us manage our careers
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1889
Pomoći će nam
da organizujemo svoje karijere
da organizujemo svoje karijere
15:03
and lead lives of satisfaction and hope
327
891818
3634
i da živimo zadovoljno i sa nadom,
u sreći i zdravlju.
15:07
and happiness and health,
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895452
2992
15:10
but in the past, we've often
looked at information technology
looked at information technology
329
898444
3306
Ranije smo često
od informacionih tehnologija
od informacionih tehnologija
gledali samo u T,
15:13
and our eyes have only seen the T,
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901750
2208
15:15
the technology, the hardware,
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903958
1686
u tehnologiju, u hardver,
zato što je to ono što je opipljivo.
15:17
because that's what was physical.
332
905644
2262
15:19
We now need to recast our gaze at the I,
333
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2924
Sada moramo da bacimo oko na I,
15:22
the information,
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1380
na informacije,
na ono manje uočljivo,
15:24
which is less apparent,
335
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1373
ali na određeni način mnogo bitnije.
15:25
but in some ways a lot more important.
336
913583
4109
Čovečanstvo konačno uči iz informacija
15:29
Humanity can finally learn from the information
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3465
15:33
that it can collect,
338
921157
2418
koje može da prikupi,
15:35
as part of our timeless quest
339
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2115
kao deo našeg vanvremenskog zadatka
15:37
to understand the world and our place in it,
340
925690
3159
da shvatimo svet i naše mesto u njemu
i zato veliki podaci jesu velika stvar.
15:40
and that's why big data is a big deal.
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(Aplauz)
15:46
(Applause)
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3568
ABOUT THE SPEAKER
Kenneth Cukier - Data Editor of The EconomistKenneth Cukier is the Data Editor of The Economist. From 2007 to 2012 he was the Tokyo correspondent, and before that, the paper’s technology correspondent in London, where his work focused on innovation, intellectual property and Internet governance. Kenneth is also the co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think with Viktor Mayer-Schönberger in 2013, which was a New York Times Bestseller and translated into 16 languages.
Why you should listen
As Data Editor of The Economist and co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think, Kenneth Cukier has spent years immersed in big data, machine learning -- and the impact of both. What's the future of big data-driven technology and design? To find out, watch this talk.
Kenneth Cukier | Speaker | TED.com