TED2016
Anthony Goldbloom: The jobs we'll lose to machines -- and the ones we won't
Anthony Goldbloom: Poslovi koje će preuzeti strojevi -- i oni koje neće
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Strojno učenje više ne uključuje samo procjenu kreditnog rizika i razvrstavanje pošte -- danas je ono sposobno za daleko složenije primjene, kao što je ocjenjivanje eseja i dijagnosticiranje bolesti. Taj napredak uključuje i jedno neugodno pitanje: Hoće li u budućnosti robot raditi vaš posao?
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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Ovo je moja nećakinja.
00:14
Her name is Yahli.
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Zove se Yahli.
00:16
She is nine months old.
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Ima devet mjeseci.
00:18
Her mum is a doctor,
and her dad is a lawyer.
and her dad is a lawyer.
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Njezina mama je liječnica,
a tata je odvjetnik.
a tata je odvjetnik.
00:21
By the time Yahli goes to college,
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Kada se Yahli upiše 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 njezini roditelji rade
izgledat će potpuno drugačije.
izgledat će potpuno 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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2013. godine, znanstvenici sveučilišta
u Oxfordu istraživali su budućnost rada.
u Oxfordu istraživali su budućnost rada.
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 gotovo svaki
drugi posao ima visok rizik
drugi posao ima visok rizik
00:36
of being automated by machines.
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da bude automatiziran strojevima.
00:40
Machine learning is the technology
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Strojno učenje je tehnologija
00:42
that's responsible for most
of this disruption.
of this disruption.
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koja je odgovorna za većinu
tog remećenja.
tog remećenja.
00:44
It's the most powerful branch
of artificial intelligence.
of artificial intelligence.
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To je najmoćnija grana
umjetne inteligencije.
umjetne inteligencije.
00:47
It allows machines to learn from data
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Strojevi mogu učiti iz podataka
00:49
and mimic some of the things
that humans can do.
that humans can do.
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i oponašati neke radnje
svojstvene ljudima.
svojstvene ljudima.
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 tvrtka Kaggle bavi se
najnaprednijim vidom strojnog učenja.
najnaprednijim vidom strojnog učenja.
00:55
We bring together
hundreds of thousands of experts
hundreds of thousands of experts
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Mi povezujemo
stotine tisuća stručnjaka
stotine tisuća stručnjaka
00:57
to solve important problems
for industry and academia.
for industry and academia.
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radi rješavanja važnih
industrijskih i akademskih problema.
industrijskih i akademskih problema.
01:01
This gives us a unique perspective
on what machines can do,
on what machines can do,
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To nam daje jedinstveni uvid
u sposobnost strojeva,
u sposobnost strojeva,
01:04
what they can't do
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njihove mogućnosti
01:05
and what jobs they might
automate or threaten.
automate or threaten.
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i poslove koje bi mogli
automatizirati ili ugroziti.
automatizirati ili ugroziti.
01:09
Machine learning started making its way
into industry in the early '90s.
into industry in the early '90s.
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Strojno učenje postalo je
dio industrije početkom 90-ih godina.
dio industrije početkom 90-ih godina.
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 procjenjivanjem
kreditnog rizika sa zahtjeva za kredit
kreditnog rizika sa zahtjeva za kredit
01:19
sorting the mail by reading
handwritten characters from zip codes.
handwritten characters from zip codes.
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i razvrstavanjem pošte čitanjem
ručno napisanih poštanskih brojeva.
ručno napisanih poštanskih brojeva.
01:24
Over the past few years, we have made
dramatic breakthroughs.
dramatic breakthroughs.
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Kroz proteklih nekoliko godina,
postigli smo nevjerojatne stvari.
postigli smo nevjerojatne stvari.
01:27
Machine learning is now capable
of far, far more complex tasks.
of far, far more complex tasks.
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Strojno učenje sada postiže
daleko, daleko naprednije rezultate.
daleko, daleko naprednije rezultate.
01:31
In 2012, Kaggle challenged its community
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2012. godine Kaggle je pozvao zajednicu
01:35
to build an algorithm
that could grade high-school essays.
that could grade high-school essays.
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da napravi algoritam
koji će ocjenjivati srednjoškolske eseje.
koji će ocjenjivati srednjoškolske eseje.
01:38
The winning algorithms
were able to match the grades
were able to match the grades
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Najbolji algoritmi dali su istu ocjenu
01:40
given by human teachers.
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kao i profesori.
01:43
Last year, we issued
an even more difficult challenge.
an even more difficult challenge.
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Prošle smo godine zadali
još jedan zahtjevniji zadatak.
još jedan zahtjevniji zadatak.
01:46
Can you take images of the eye
and diagnose an eye disease
and diagnose an eye disease
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Možete li pomoću snimke oka
dijagnosticirati očnu bolest
dijagnosticirati očnu bolest
01:49
called diabetic retinopathy?
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zvanu dijabetička retinopatija?
01:51
Again, the winning algorithms
were able to match the diagnoses
were able to match the diagnoses
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I ponovno, najbolji algoritmi
dali su istu dijagnozu
dali su istu dijagnozu
01:55
given by human ophthalmologists.
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kao i oftalmolozi.
01:57
Now, given the right data,
machines are going to outperform humans
machines are going to outperform humans
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Pomoću pravilnih podataka,
strojevi mogu prestići ljude
strojevi mogu prestići ljude
02:00
at tasks like this.
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u zadacima poput ovih.
02:01
A teacher might read 10,000 essays
over a 40-year career.
over a 40-year career.
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Profesor može pročitati 10.000 eseja
kroz 40-godišnju karijeru.
kroz 40-godišnju karijeru.
02:06
An ophthalmologist might see 50,000 eyes.
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Oftalmolog može pregledati 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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Stroj može pročitati milijune eseja
ili pregledati milijune očiju
ili pregledati milijune očiju
02:12
within minutes.
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u roku od par minuta.
02:14
We have no chance of competing
against machines
against machines
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Jednostavno se ne možemo
natjecati protiv strojeva
natjecati protiv strojeva
02:17
on frequent, high-volume tasks.
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u čestim zadacima s mnogo podataka.
02:20
But there are things we can do
that machines can't do.
that machines can't do.
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No, postoje stvari koje mi možemo,
a koje strojevi ne mogu.
a koje strojevi ne mogu.
02:24
Where machines have made
very little progress
very little progress
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Područje gdje su strojevi
vrlo malo napredovali
vrlo malo napredovali
02:27
is in tackling novel situations.
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je rješavanje novonastalih situacija.
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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Oni se ne mogu nositi sa stvarima
koje nisu vidjeli puno puta u prošlosti.
koje nisu vidjeli puno puta u prošlosti.
02:33
The fundamental limitations
of machine learning
of machine learning
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Osnovno ograničenje strojnog 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 to do mora učiti
iz velike količine prijašnjih podataka.
iz velike količine prijašnjih podataka.
02:39
Now, humans don't.
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Ljudi ne moraju.
02:41
We have the ability to connect
seemingly disparate threads
seemingly disparate threads
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Mi imamo sposobnost spojiti
naizgled nepovezane niti
naizgled nepovezane niti
02:44
to solve problems we've never seen before.
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i riješiti novonastale probleme.
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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Percy Spencer bio je fizičar koji je
radio na radaru tijekom 2. svjetskog rata
radio na radaru tijekom 2. svjetskog rata
02:51
when he noticed the magnetron
was melting his chocolate bar.
was melting his chocolate bar.
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kada je primijetio da mu
magnetron otapa čokoladu.
magnetron otapa čokoladu.
02:54
He was able to connect his understanding
of electromagnetic radiation
of electromagnetic radiation
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On je povezao svoje razumijevanje
elektromagnetske radijacije
elektromagnetske radijacije
02:58
with his knowledge of cooking
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sa svojim znanjem o kuhanju
02:59
in order to invent -- any guesses? --
the microwave oven.
the microwave oven.
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da bi na kraju izumio -- možete pogoditi?
-- mikrovalnu pećnicu.
-- mikrovalnu pećnicu.
03:03
Now, this is a particularly remarkable
example of creativity.
example of creativity.
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Ovo je jedan izvanredan
primjer kreativnosti.
primjer 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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No, ovakva se povezivanja,
u malim omjerima, kod svakoga od nas
u malim omjerima, kod svakoga od nas
03:10
thousands of times per day.
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događaju tisućama puta dnevno.
03:12
Machines cannot compete with us
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Strojevi se ne mogu mjeriti s nama
03:14
when it comes to tackling
novel situations,
novel situations,
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u rješavanju tih
novonastalih situacija,
novonastalih situacija,
03:16
and this puts a fundamental limit
on the human tasks
on the human tasks
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i to uvelike ograničava
broj poslova u kojima
broj poslova u kojima
03:19
that machines will automate.
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strojevi mogu zamijeniti ljude.
03:22
So what does this mean
for the future of work?
for the future of work?
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I što to onda znači
za budućnost rada?
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 bilo kojeg posla
ovisi o odgovoru na pitanje:
ovisi o odgovoru na 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 se mjere taj posao može svesti
na ponavljajuće zadatke s mnogo podataka,
na ponavljajuće zadatke s mnogo podataka,
03:34
and to what extent does it involve
tackling novel situations?
tackling novel situations?
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a koliko uključuje
rješavanje novonastalih situacija.
rješavanje novonastalih situacija.
03:37
On frequent, high-volume tasks,
machines are getting smarter and smarter.
machines are getting smarter and smarter.
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U čestim zadacima s mnogo podataka,
strojevi postaju sve pametniji.
strojevi postaju sve pametniji.
03:42
Today they grade essays.
They diagnose certain diseases.
They diagnose certain diseases.
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Danas oni ocjenjuju eseje.
Dijagnosticiraju neke bolesti.
Dijagnosticiraju neke bolesti.
03:44
Over coming years,
they're going to conduct our audits,
they're going to conduct our audits,
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S godinama će
biti u stanju vršiti revizije
biti u stanju vršiti revizije
03:47
and they're going to read boilerplate
from legal contracts.
from legal contracts.
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i čitati standardne tekstove
na ugovorima.
na ugovorima.
03:50
Accountants and lawyers are still needed.
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Računovođe i odvjetnici još su potrebni.
03:52
They're going to be needed
for complex tax structuring,
for complex tax structuring,
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Oni će biti potrebni
za složene porezne sustave
za složene porezne sustave
03:55
for pathbreaking litigation.
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i u pravnim sporovima.
03:57
But machines will shrink their ranks
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Međutim, strojevi će to promijeniti
03:58
and make these jobs harder to come by.
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i smanjiti dostupnost tih poslova.
04:00
Now, as mentioned,
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Kao što sam spomenuo,
04:01
machines are not making progress
on novel situations.
on novel situations.
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strojevi ne napreduju
u rješavanju novonastalih situacija.
u rješavanju novonastalih situacija.
04:04
The copy behind a marketing campaign
needs to grab consumers' attention.
needs to grab consumers' attention.
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Marketinška kampanja
mora privući pažnju potrošača.
mora privući pažnju potrošača.
04:08
It has to stand out from the crowd.
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Mora se isticati.
04:10
Business strategy means
finding gaps in the market,
finding gaps in the market,
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Poslovna strategija uključuje
nalaženje rupa,
nalaženje rupa,
04:12
things that nobody else is doing.
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stvari koje nitko 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 će biti ti koji će stvarati
marketinške kampanje
marketinške kampanje
04:18
and it will be humans that are developing
our business strategy.
our business strategy.
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i ljudi će biti ti koji će razvijati
poslovne strategije.
poslovne strategije.
04:21
So Yahli, whatever you decide to do,
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Tako da, Yahli, što god odlučila raditi,
04:24
let every day bring you a new challenge.
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neka ti svaki dan donese neki novi izazov.
04:27
If it does, then you will stay
ahead of the machines.
ahead of the machines.
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Ako tako bude, uvijek ćeš biti
ispred strojeva.
ispred strojeva.
04:31
Thank you.
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Hvala.
04:32
(Applause)
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(Pljesak)
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