TED2016
Anthony Goldbloom: The jobs we'll lose to machines -- and the ones we won't
Entoni Goldblum (Anthony Goldbloom): Poslovi koje će nam uzeti mašine - i oni koje neće
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Mašinsko učenje se ne koristi samo za jednostavne zadatke, poput procenjivanja kreditnog rizika i sortiranja pošte - danas je ono sposobno za daleko složenije primene, poput ocenjivanja eseja i dijagnostikovanja bolesti. Ovaj napredak prati neprijatno pitanje: hoće li robot obavljati vaš posao u budućnosti?
Anthony Goldbloom - Machine learning expert
Anthony Goldbloom crowdsources solutions to difficult problems using machine learning. Full bio
Anthony Goldbloom crowdsources solutions to difficult problems using machine learning. Full bio
Double-click the English transcript below to play the video.
00:12
So this is my niece.
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Dakle, ovo je moja nećaka.
00:14
Her name is Yahli.
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Zove se Jali.
00:16
She is nine months old.
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Stara je devet meseci.
00:18
Her mum is a doctor,
and her dad is a lawyer.
and her dad is a lawyer.
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Njena majka je doktor,
a njen otac je advokat.
a njen otac je advokat.
00:21
By the time Yahli goes to college,
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Kad Jali pođe na fakultet,
00:23
the jobs her parents do
are going to look dramatically different.
are going to look dramatically different.
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poslovi koje njeni roditelji obavljaju
izgledaće drastično drugačije.
izgledaće drastično drugačije.
00:27
In 2013, researchers at Oxford University
did a study on the future of work.
did a study on the future of work.
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Istraživači sa Oksforda su 2013.
uradili istraživanje o budućnosti poslova.
uradili istraživanje o budućnosti poslova.
00:32
They concluded that almost one
in every two jobs have a high risk
in every two jobs have a high risk
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Zaključili su da je gotovo jedan
od svaka dva posla pod velikim rizikom
od svaka dva posla pod velikim rizikom
00:36
of being automated by machines.
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da bude mašinski automatizovan.
00:40
Machine learning is the technology
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Mašinsko učenje je tehnologija
00:42
that's responsible for most
of this disruption.
of this disruption.
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koja je najodgovornija za ovaj raskol.
00:44
It's the most powerful branch
of artificial intelligence.
of artificial intelligence.
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To je najmoćnija grana
veštačke inteligencije.
veštačke inteligencije.
00:47
It allows machines to learn from data
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Omogućuje mašinama da uče iz podataka
00:49
and mimic some of the things
that humans can do.
that humans can do.
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i da oponašaju neke stvari
koje ljudi mogu da rade.
koje ljudi mogu da rade.
00:51
My company, Kaggle, operates
on the cutting edge of machine learning.
on the cutting edge of machine learning.
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Moja firma, Kagle, se bavi
najnaprednijim vidom mašinskog učenja.
najnaprednijim vidom mašinskog učenja.
00:55
We bring together
hundreds of thousands of experts
hundreds of thousands of experts
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Spajamo na stotine hiljada eksperata
00:57
to solve important problems
for industry and academia.
for industry and academia.
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kako bismo rešili važne probleme
u industriji i akademiji.
u industriji i akademiji.
01:01
This gives us a unique perspective
on what machines can do,
on what machines can do,
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To nam pruža jedinstvenu perspektivu
na to šta mašine mogu,
na to šta mašine mogu,
01:04
what they can't do
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šta ne mogu
01:05
and what jobs they might
automate or threaten.
automate or threaten.
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i koje poslove mogu
da automatizuju ili ugroze.
da automatizuju ili ugroze.
01:09
Machine learning started making its way
into industry in the early '90s.
into industry in the early '90s.
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Mašinsko učenje se počelo probijati
u industriji tokom ranih '90-ih.
u industriji tokom ranih '90-ih.
01:12
It started with relatively simple tasks.
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Počelo je relativno jednostavnim zadacima.
01:15
It started with things like assessing
credit risk from loan applications,
credit risk from loan applications,
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Počelo je stvarima poput bavljenja
kreditnim rizikom kod molbi za zajam,
kreditnim rizikom kod molbi za zajam,
01:19
sorting the mail by reading
handwritten characters from zip codes.
handwritten characters from zip codes.
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sortiranjem pošte čitanjem
ručno pisanih slova zip kodova.
ručno pisanih slova zip kodova.
01:24
Over the past few years, we have made
dramatic breakthroughs.
dramatic breakthroughs.
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Tokom proteklih nekoliko godina
imali smo drastična dostignuća.
imali smo drastična dostignuća.
01:27
Machine learning is now capable
of far, far more complex tasks.
of far, far more complex tasks.
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Mašinsko učenje je sada sposobno
za daleko, daleko složenije zadatke.
za daleko, daleko složenije zadatke.
01:31
In 2012, Kaggle challenged its community
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Godine 2012. Kagle je izazvao
njegovu zajednicu
njegovu zajednicu
01:35
to build an algorithm
that could grade high-school essays.
that could grade high-school essays.
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da naprave algoritam
koji bi ocenjivao srednjoškolske eseje.
koji bi ocenjivao srednjoškolske eseje.
01:38
The winning algorithms
were able to match the grades
were able to match the grades
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Pobednički algoritmi
su mogli da daju podudarne ocene
su mogli da daju podudarne ocene
01:40
given by human teachers.
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kao i ljudski profesori.
01:43
Last year, we issued
an even more difficult challenge.
an even more difficult challenge.
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Prošle godine smo napravili
čak i komplikovaniji izazov.
čak i komplikovaniji izazov.
Možete li da uzmete snimak oka
i da dijagnostikujete očnu bolest
i da dijagnostikujete očnu bolest
01:46
Can you take images of the eye
and diagnose an eye disease
and diagnose an eye disease
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01:49
called diabetic retinopathy?
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pod nazivom dijabetička retinopatija?
01:51
Again, the winning algorithms
were able to match the diagnoses
were able to match the diagnoses
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Opet su pobednički algoritmi
mogli da daju podudarnu dijagnozu
mogli da daju podudarnu dijagnozu
01:55
given by human ophthalmologists.
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kao i ljudski oftalmolozi.
01:57
Now, given the right data,
machines are going to outperform humans
machines are going to outperform humans
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Sad, uz odgovarajuće podatke
mašine će da nadmaše ljude
mašine će da nadmaše ljude
02:00
at tasks like this.
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u sličnim zadacima.
02:01
A teacher might read 10,000 essays
over a 40-year career.
over a 40-year career.
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Nastavnik može da pročita 10.000 eseja
tokom 40-ogodišnje karijere.
tokom 40-ogodišnje karijere.
02:06
An ophthalmologist might see 50,000 eyes.
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Oftalmolog može da pregleda 50.000 očiju.
02:08
A machine can read millions of essays
or see millions of eyes
or see millions of eyes
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Mašina može da pročita na milione eseja
ili da pregleda na milione očiju
ili da pregleda na milione očiju
02:12
within minutes.
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za nekoliko minuta.
02:14
We have no chance of competing
against machines
against machines
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Nemamo nikakve šanse
u takmičenju s mašinama
u takmičenju s mašinama
02:17
on frequent, high-volume tasks.
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na učestalim zadacima velikog obima.
02:20
But there are things we can do
that machines can't do.
that machines can't do.
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Ali ima nešto što mi možemo,
a mašine ne mogu.
a mašine ne mogu.
02:24
Where machines have made
very little progress
very little progress
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Mašine su postigle veoma mali napredak
02:27
is in tackling novel situations.
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kod bavljenja novim situacijama.
02:28
They can't handle things
they haven't seen many times before.
they haven't seen many times before.
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Ne mogu da savladaju nešto
što nisu videle mnogo puta ranije.
što nisu videle mnogo puta ranije.
02:33
The fundamental limitations
of machine learning
of machine learning
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Temeljno ograničenje mašinskog učenja
02:35
is that it needs to learn
from large volumes of past data.
from large volumes of past data.
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je što mašine moraju da uče
iz obilja prethodnih podataka.
iz obilja prethodnih podataka.
02:39
Now, humans don't.
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A ljudi ne moraju.
02:41
We have the ability to connect
seemingly disparate threads
seemingly disparate threads
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Sposobni smo da povežemo
naoko nepovezane niti
naoko nepovezane niti
02:44
to solve problems we've never seen before.
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kako bismo rešili za nas nov problem.
02:46
Percy Spencer was a physicist
working on radar during World War II,
working on radar during World War II,
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Persi Spenser je bio fizičar koji je radio
na radaru tokom II svetskog rata,
na radaru tokom II svetskog rata,
02:51
when he noticed the magnetron
was melting his chocolate bar.
was melting his chocolate bar.
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kad je primetio kako magnetron
topi njegovu tablu čokolade.
topi njegovu tablu čokolade.
02:54
He was able to connect his understanding
of electromagnetic radiation
of electromagnetic radiation
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Mogao je da poveže sopstveno razumevanje
elektromagnetne radijacije
elektromagnetne radijacije
02:58
with his knowledge of cooking
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sa poznavanjem kuvanja
02:59
in order to invent -- any guesses? --
the microwave oven.
the microwave oven.
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kako bi izumeo - pretpostavljate li šta? -
mikrotalasnu pećnicu.
mikrotalasnu pećnicu.
03:03
Now, this is a particularly remarkable
example of creativity.
example of creativity.
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Sad, ovo je izrazito upečatljiv
primer kreativnosti.
primer kreativnosti.
03:06
But this sort of cross-pollination
happens for each of us in small ways
happens for each of us in small ways
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Ali ovakva plodna ukrštanja
nam se dešavaju na mikroplanu
nam se dešavaju na mikroplanu
03:10
thousands of times per day.
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hiljadama puta tokom dana.
03:12
Machines cannot compete with us
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Mašine ne mogu da se takmiče s nama
03:14
when it comes to tackling
novel situations,
novel situations,
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kad je u pitanju
bavljenje novim situacijama,
bavljenje novim situacijama,
03:16
and this puts a fundamental limit
on the human tasks
on the human tasks
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a ovo postavlja temeljno ograničenje
na ljudske zadatke
na ljudske zadatke
03:19
that machines will automate.
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koje mašine mogu da automatizuju.
03:22
So what does this mean
for the future of work?
for the future of work?
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Pa, šta ovo znači za budućnost rada?
03:24
The future state of any single job lies
in the answer to a single question:
in the answer to a single question:
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Budućnost svakog posla počiva
u odgovoru na samo jedno pitanje:
u odgovoru na samo jedno pitanje:
03:29
To what extent is that job reducible
to frequent, high-volume tasks,
to frequent, high-volume tasks,
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do koje mere je taj posao svodiv
na učestale zadatke velikog obima
na učestale zadatke velikog obima
03:34
and to what extent does it involve
tackling novel situations?
tackling novel situations?
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i u kojoj meri uključuje
bavljenje novim situacijama?
bavljenje novim situacijama?
03:37
On frequent, high-volume tasks,
machines are getting smarter and smarter.
machines are getting smarter and smarter.
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Kod učestalih zadataka velikog obima
mašine postaju sve pametnije i pametnije.
mašine postaju sve pametnije i pametnije.
03:42
Today they grade essays.
They diagnose certain diseases.
They diagnose certain diseases.
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Danas one ocenjuju eseje.
Dijagnostikuju određene bolesti.
Dijagnostikuju određene bolesti.
03:44
Over coming years,
they're going to conduct our audits,
they're going to conduct our audits,
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U narednim godinama
radiće revizije poreza
radiće revizije poreza
03:47
and they're going to read boilerplate
from legal contracts.
from legal contracts.
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i čitaće opšta mesta u pravnim ugovorima.
03:50
Accountants and lawyers are still needed.
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I dalje ćemo trebati
računovođe i advokate.
računovođe i advokate.
03:52
They're going to be needed
for complex tax structuring,
for complex tax structuring,
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Trebaće nam za složeno
struktuiranje poreza,
struktuiranje poreza,
03:55
for pathbreaking litigation.
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za pionirske parnice.
03:57
But machines will shrink their ranks
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No, mašine će suziti njihovo zvanje
i učiniće ove poslove težim za nalaženje.
03:58
and make these jobs harder to come by.
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04:00
Now, as mentioned,
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Sad, kao što sam pomenuo
04:01
machines are not making progress
on novel situations.
on novel situations.
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mašine ne postižu napredak
kod novih situacija.
kod novih situacija.
04:04
The copy behind a marketing campaign
needs to grab consumers' attention.
needs to grab consumers' attention.
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Poruka marketinške kampanje
mora da zgrabi pažnju potrošača.
mora da zgrabi pažnju potrošača.
04:08
It has to stand out from the crowd.
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Mora da se ističe u gomili.
Poslovna strategija znači
nalaženje rupa u tržištu,
nalaženje rupa u tržištu,
04:10
Business strategy means
finding gaps in the market,
finding gaps in the market,
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04:12
things that nobody else is doing.
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stvari koje niko drugi ne radi.
04:14
It will be humans that are creating
the copy behind our marketing campaigns,
the copy behind our marketing campaigns,
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Ljudi su ti koji će da stvaraju
poruke marketinških kampanja,
poruke marketinških kampanja,
04:18
and it will be humans that are developing
our business strategy.
our business strategy.
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i ljudi su ti koji će razvijati
naše poslovne strategije.
naše poslovne strategije.
04:21
So Yahli, whatever you decide to do,
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Pa, Jali, čime god odlučiš da se baviš,
04:24
let every day bring you a new challenge.
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neka ti svaki dan donese novi izazov.
04:27
If it does, then you will stay
ahead of the machines.
ahead of the machines.
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Ako bude tako, bićeš ispred mašina.
04:31
Thank you.
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Hvala vam.
04:32
(Applause)
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(Aplauz)
ABOUT THE SPEAKER
Anthony Goldbloom - Machine learning expertAnthony Goldbloom crowdsources solutions to difficult problems using machine learning.
Why you should listen
Anthony Goldbloom is the co-founder and CEO of Kaggle. Kaggle hosts machine learning competitions, where data scientists download data and upload solutions to difficult problems. Kaggle has a community of over 600,000 data scientists and has worked with companies ranging Facebook to GE on problems ranging from predicting friendships to flight arrival times.
Before Kaggle, Anthony worked as an econometrician at the Reserve Bank of Australia, and before that the Australian Treasury. In 2011 and 2012, Forbes named Anthony one of the 30 under 30 in technology; in 2013 the MIT Tech Review named him one of top 35 innovators under the age of 35, and the University of Melbourne awarded him an Alumni of Distinction Award. He holds a first call honors degree in Econometrics from the University of Melbourne.
Anthony Goldbloom | Speaker | TED.com