TED2016
Anthony Goldbloom: The jobs we'll lose to machines -- and the ones we won't
Anthony Goldbloom: De arbejdspladser vi mister til maskiner -- og dem vi ikke gør
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Machine learning bliver ikke kun brugt til simple opgaver som risiko bedømmelse og brev sortering -- i dag er det i stand til mere komplekse opgave som at bedømme stile og diagnosticere sygdomme. Med de fremskridt følger der et bekymrende spørgsmål: Kommer en robot til at udføre dit job i fremtiden?
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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Det her er min niece.
00:14
Her name is Yahli.
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Hun hedder Yahli.
00:16
She is nine months old.
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Hun er ni måneder gammel.
00:18
Her mum is a doctor,
and her dad is a lawyer.
and her dad is a lawyer.
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Hendes mor er læge,
og hendes far er advokat.
og hendes far er advokat.
00:21
By the time Yahli goes to college,
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Når Yahli studere på universitetet
00:23
the jobs her parents do
are going to look dramatically different.
are going to look dramatically different.
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vil hendes forældres arbejde
se meget anderledes ud.
se meget anderledes ud.
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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I 2013 lavede forskere fra Oxford
en undersøgelse om fremtidens arbejde.
en undersøgelse om fremtidens arbejde.
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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De konkluderede, at næsten hver andet job
har stor risiko for at
blive automatiseret af maskiner.
blive automatiseret af maskiner.
00:36
of being automated by machines.
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00:40
Machine learning is the technology
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Machine learning er den teknologi
der ligger til grunde
for størstedelen af ændringerne.
for størstedelen af ændringerne.
00:42
that's responsible for most
of this disruption.
of this disruption.
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00:44
It's the most powerful branch
of artificial intelligence.
of artificial intelligence.
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Det er den mest virkningsfulde del
af kunstig intelligens.
af kunstig intelligens.
00:47
It allows machines to learn from data
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Det tillader maskiner at lære fra data
00:49
and mimic some of the things
that humans can do.
that humans can do.
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og efterligne nogen af de ting,
som mennesker kan gøre.
som mennesker kan gøre.
00:51
My company, Kaggle, operates
on the cutting edge of machine learning.
on the cutting edge of machine learning.
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Mit firma, Kaggle, arbejder på det nyeste
inden for machine learning.
inden for machine learning.
00:55
We bring together
hundreds of thousands of experts
hundreds of thousands of experts
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Vi samler flere
hundrede tusinder eksperter
hundrede tusinder eksperter
som skal løse vigtige problemer
for den industrielle og akademiske verden
for den industrielle og akademiske verden
00:57
to solve important problems
for industry and academia.
for industry and academia.
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01:01
This gives us a unique perspective
on what machines can do,
on what machines can do,
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Det giver os et indblik i
hvad maskiner kan,
hvad maskiner kan,
hvad de ikke kan,
01:04
what they can't do
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01:05
and what jobs they might
automate or threaten.
automate or threaten.
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og hvilke job de måske
kommer til at automatisere
kommer til at automatisere
01:09
Machine learning started making its way
into industry in the early '90s.
into industry in the early '90s.
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Første gang machine learning blev brugt
industrielt, var i start 90'erne.
industrielt, var i start 90'erne.
Det startede simpelt.
01:12
It started with relatively simple tasks.
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01:15
It started with things like assessing
credit risk from loan applications,
credit risk from loan applications,
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Det startede med at vurdere
kreditrisiko fra låneansøgninger,
kreditrisiko fra låneansøgninger,
01:19
sorting the mail by reading
handwritten characters from zip codes.
handwritten characters from zip codes.
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brev sortering ved at læse
håndskrevne tegn fra postnumre.
håndskrevne tegn fra postnumre.
01:24
Over the past few years, we have made
dramatic breakthroughs.
dramatic breakthroughs.
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Gennem de sidste par år
er der sket banebrydende gennembrud.
er der sket banebrydende gennembrud.
01:27
Machine learning is now capable
of far, far more complex tasks.
of far, far more complex tasks.
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Machine learning er nu i stand til
langt mere komplekse opgaver.
langt mere komplekse opgaver.
01:31
In 2012, Kaggle challenged its community
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I 2012 udfordrede Kaggle sit lokalsamfund
01:35
to build an algorithm
that could grade high-school essays.
that could grade high-school essays.
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til at programmere en algoritme
til at bedømme gymnasie stile.
til at bedømme gymnasie stile.
01:38
The winning algorithms
were able to match the grades
were able to match the grades
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Algoritmen der vandt,
gav den samme karakter
gav den samme karakter
01:40
given by human teachers.
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som den rigtige lærer gjorde.
01:43
Last year, we issued
an even more difficult challenge.
an even more difficult challenge.
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Sidste år lavede vi en
sværere udfordring.
sværere udfordring.
01:46
Can you take images of the eye
and diagnose an eye disease
and diagnose an eye disease
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Kan man tage billeder af øjet
og diagnostisere en øjensygdom
og diagnostisere en øjensygdom
01:49
called diabetic retinopathy?
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kaldet diabetisk retinopati?
01:51
Again, the winning algorithms
were able to match the diagnoses
were able to match the diagnoses
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Igen gav algoritmen der vandt
den samme diagnose
den samme diagnose
01:55
given by human ophthalmologists.
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som var givet af en øjenlæge.
01:57
Now, given the right data,
machines are going to outperform humans
machines are going to outperform humans
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Maskiner udkonkurrerer altid mennesker
ved opgaver som denne,
ved opgaver som denne,
givet at den får de rigtige data.
02:00
at tasks like this.
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02:01
A teacher might read 10,000 essays
over a 40-year career.
over a 40-year career.
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En lærer læser måske 10.000 stile
over en 40-årig karriere.
over en 40-årig karriere.
02:06
An ophthalmologist might see 50,000 eyes.
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En øjenlæge ser måske 50.000 øjne
02:08
A machine can read millions of essays
or see millions of eyes
or see millions of eyes
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En maskine kan læse millioner af stile
eller se millioner af øjne
eller se millioner af øjne
02:12
within minutes.
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på få minutter.
02:14
We have no chance of competing
against machines
against machines
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Vi kan ikke hamle op med maskinerne,
02:17
on frequent, high-volume tasks.
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når det gælder mængde opgaver.
02:20
But there are things we can do
that machines can't do.
that machines can't do.
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Men der er ting, vi kan,
som maskiner ikke kan.
som maskiner ikke kan.
02:24
Where machines have made
very little progress
very little progress
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Maskiner er ikke blevet særlig meget bedre
02:27
is in tackling novel situations.
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til at takle unikke og nye situationer
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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De kan ikke arbejde med ting,
de ikke har set en masse gange før.
de ikke har set en masse gange før.
02:33
The fundamental limitations
of machine learning
of machine learning
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De grundlæggende begrænsninger
for machine learning
for machine learning
02:35
is that it needs to learn
from large volumes of past data.
from large volumes of past data.
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er at de skal lære fra store
mængder tidligere data.
mængder tidligere data.
02:39
Now, humans don't.
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Det skal mennesker ikke.
02:41
We have the ability to connect
seemingly disparate threads
seemingly disparate threads
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Vi har evnen til at finde sammenhængen
i forskellige situationer,
i forskellige situationer,
02:44
to solve problems we've never seen before.
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og løse problemer vi ikke har set før.
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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Mens fysikeren Percy Spenser arbejdede
med radarer under 2. verdenskrig,
med radarer under 2. verdenskrig,
02:51
when he noticed the magnetron
was melting his chocolate bar.
was melting his chocolate bar.
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opdagede han at magnetronen
smeltede hans chokolade bar.
smeltede hans chokolade bar.
02:54
He was able to connect his understanding
of electromagnetic radiation
of electromagnetic radiation
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Han forenede sin forståelse
for elektromagnetisk stråling
for elektromagnetisk stråling
med sit kendskab til madlavning
02:58
with his knowledge of cooking
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02:59
in order to invent -- any guesses? --
the microwave oven.
the microwave oven.
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for at opfinde -- nogen gæt? --
mikrobølgeovnen.
mikrobølgeovnen.
03:03
Now, this is a particularly remarkable
example of creativity.
example of creativity.
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Det er et eksempel på
særlig kreativitet.
særlig kreativitet.
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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Den forenende måde at tænke på,
sker for as alle på mindre stadier
sker for as alle på mindre stadier
03:10
thousands of times per day.
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tusinder af gange om dagen.
03:12
Machines cannot compete with us
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Maskiner kan ikke hamle op med os,
03:14
when it comes to tackling
novel situations,
novel situations,
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når det kommer til
unikke situationer,
unikke situationer,
03:16
and this puts a fundamental limit
on the human tasks
on the human tasks
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og det skaber en begrænsning
for hvilke opgaver
for hvilke opgaver
03:19
that machines will automate.
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maskiner komme til at automatisere.
03:22
So what does this mean
for the future of work?
for the future of work?
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Så hvilken betydning har det
for fremtidens arbejde?
for fremtidens arbejde?
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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Om et job er sikret for fremtiden,
kan besvares med et spørgsmål:
kan besvares med et spørgsmål:
03:29
To what extent is that job reducible
to frequent, high-volume tasks,
to frequent, high-volume tasks,
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I hvor stor en grad kan jobbet
nedskrives til mængde opgaver,
nedskrives til mængde opgaver,
03:34
and to what extent does it involve
tackling novel situations?
tackling novel situations?
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og i hvor stor grad indebærer det
unikke situationer?
unikke situationer?
03:37
On frequent, high-volume tasks,
machines are getting smarter and smarter.
machines are getting smarter and smarter.
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Når det gælder mængde opgaver,
bliver maskiner klogere og klogere.
bliver maskiner klogere og klogere.
03:42
Today they grade essays.
They diagnose certain diseases.
They diagnose certain diseases.
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I dag bedømmer de stile.
De diagnosticere visse sygdomme.
De diagnosticere visse sygdomme.
03:44
Over coming years,
they're going to conduct our audits,
they're going to conduct our audits,
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De følgende år,
vil de foretage vores regnskaber.
vil de foretage vores regnskaber.
03:47
and they're going to read boilerplate
from legal contracts.
from legal contracts.
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De kommer til at læse standard tekst
fra juridiske kontrakter
fra juridiske kontrakter
03:50
Accountants and lawyers are still needed.
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Revisorer og advokater skal stadig bruges.
03:52
They're going to be needed
for complex tax structuring,
for complex tax structuring,
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De skal bruges til
kompleks skatte-strukturering,
kompleks skatte-strukturering,
til banebrydende retstvister.
03:55
for pathbreaking litigation.
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Men maskiner vil mindske deres omdømme,
03:57
But machines will shrink their ranks
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03:58
and make these jobs harder to come by.
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og gøre den type jobs mindre hyppige.
04:00
Now, as mentioned,
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Som jeg har nævnt,
laver maskiner ingen fremskridt
når det gælder unikke situationer.
når det gælder unikke situationer.
04:01
machines are not making progress
on novel situations.
on novel situations.
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04:04
The copy behind a marketing campaign
needs to grab consumers' attention.
needs to grab consumers' attention.
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Rammen for en marketing kampagne
skal fange folks opmærksomhed,
skal fange folks opmærksomhed,
04:08
It has to stand out from the crowd.
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den skal skille sig ud.
Erhvervs strategi går ud på
at finde mangler i markedet,
at finde mangler i markedet,
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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noget som ingen andre gør.
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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Det kommer til at være mennesker som
skaber rammen for en marketing kampagne,
skaber rammen for en marketing kampagne,
04:18
and it will be humans that are developing
our business strategy.
our business strategy.
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og det vil være mennesker, som
vil udvikle vores erhvervs strategi.
vil udvikle vores erhvervs strategi.
04:21
So Yahli, whatever you decide to do,
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Så hvad end du beslutter
dig for at lave, Yahli,
dig for at lave, Yahli,
04:24
let every day bring you a new challenge.
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lad hver dag bringe nye udfordringer.
04:27
If it does, then you will stay
ahead of the machines.
ahead of the machines.
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For så vil du forblive foran maskinerne
04:31
Thank you.
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Tak.
04:32
(Applause)
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(Klapsalve)
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