TED2016
Anthony Goldbloom: The jobs we'll lose to machines -- and the ones we won't
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Machine learning isn't just for simple tasks like assessing credit risk and sorting mail anymore -- today, it's capable of far more complex applications, like grading essays and diagnosing diseases. With these advances comes an uneasy question: Will a robot do your job in the future?
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.
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So this is my niece.
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Her name is Yahli.
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She is nine months old.
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Her mum is a doctor,
and her dad is a lawyer.
and her dad is a lawyer.
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By the time Yahli goes to college,
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the jobs her parents do
are going to look dramatically different.
are going to look dramatically different.
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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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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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of being automated by machines.
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Machine learning is the technology
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that's responsible for most
of this disruption.
of this disruption.
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It's the most powerful branch
of artificial intelligence.
of artificial intelligence.
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It allows machines to learn from data
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and mimic some of the things
that humans can do.
that humans can do.
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My company, Kaggle, operates
on the cutting edge of machine learning.
on the cutting edge of machine learning.
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We bring together
hundreds of thousands of experts
hundreds of thousands of experts
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to solve important problems
for industry and academia.
for industry and academia.
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This gives us a unique perspective
on what machines can do,
on what machines can do,
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what they can't do
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and what jobs they might
automate or threaten.
automate or threaten.
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Machine learning started making its way
into industry in the early '90s.
into industry in the early '90s.
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It started with relatively simple tasks.
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It started with things like assessing
credit risk from loan applications,
credit risk from loan applications,
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sorting the mail by reading
handwritten characters from zip codes.
handwritten characters from zip codes.
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Over the past few years, we have made
dramatic breakthroughs.
dramatic breakthroughs.
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Machine learning is now capable
of far, far more complex tasks.
of far, far more complex tasks.
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In 2012, Kaggle challenged its community
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to build an algorithm
that could grade high-school essays.
that could grade high-school essays.
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The winning algorithms
were able to match the grades
were able to match the grades
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given by human teachers.
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Last year, we issued
an even more difficult challenge.
an even more difficult challenge.
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Can you take images of the eye
and diagnose an eye disease
and diagnose an eye disease
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called diabetic retinopathy?
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Again, the winning algorithms
were able to match the diagnoses
were able to match the diagnoses
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given by human ophthalmologists.
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Now, given the right data,
machines are going to outperform humans
machines are going to outperform humans
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at tasks like this.
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A teacher might read 10,000 essays
over a 40-year career.
over a 40-year career.
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An ophthalmologist might see 50,000 eyes.
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A machine can read millions of essays
or see millions of eyes
or see millions of eyes
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within minutes.
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We have no chance of competing
against machines
against machines
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on frequent, high-volume tasks.
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But there are things we can do
that machines can't do.
that machines can't do.
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Where machines have made
very little progress
very little progress
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is in tackling novel situations.
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They can't handle things
they haven't seen many times before.
they haven't seen many times before.
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The fundamental limitations
of machine learning
of machine learning
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is that it needs to learn
from large volumes of past data.
from large volumes of past data.
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Now, humans don't.
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We have the ability to connect
seemingly disparate threads
seemingly disparate threads
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to solve problems we've never seen before.
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Percy Spencer was a physicist
working on radar during World War II,
working on radar during World War II,
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when he noticed the magnetron
was melting his chocolate bar.
was melting his chocolate bar.
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He was able to connect his understanding
of electromagnetic radiation
of electromagnetic radiation
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with his knowledge of cooking
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in order to invent -- any guesses? --
the microwave oven.
the microwave oven.
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Now, this is a particularly remarkable
example of creativity.
example of creativity.
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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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thousands of times per day.
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Machines cannot compete with us
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when it comes to tackling
novel situations,
novel situations,
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and this puts a fundamental limit
on the human tasks
on the human tasks
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that machines will automate.
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So what does this mean
for the future of work?
for the future of work?
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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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To what extent is that job reducible
to frequent, high-volume tasks,
to frequent, high-volume tasks,
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and to what extent does it involve
tackling novel situations?
tackling novel situations?
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On frequent, high-volume tasks,
machines are getting smarter and smarter.
machines are getting smarter and smarter.
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Today they grade essays.
They diagnose certain diseases.
They diagnose certain diseases.
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Over coming years,
they're going to conduct our audits,
they're going to conduct our audits,
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and they're going to read boilerplate
from legal contracts.
from legal contracts.
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Accountants and lawyers are still needed.
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They're going to be needed
for complex tax structuring,
for complex tax structuring,
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for pathbreaking litigation.
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But machines will shrink their ranks
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and make these jobs harder to come by.
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Now, as mentioned,
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machines are not making progress
on novel situations.
on novel situations.
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The copy behind a marketing campaign
needs to grab consumers' attention.
needs to grab consumers' attention.
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It has to stand out from the crowd.
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Business strategy means
finding gaps in the market,
finding gaps in the market,
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things that nobody else is doing.
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It will be humans that are creating
the copy behind our marketing campaigns,
the copy behind our marketing campaigns,
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and it will be humans that are developing
our business strategy.
our business strategy.
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So Yahli, whatever you decide to do,
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let every day bring you a new challenge.
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If it does, then you will stay
ahead of the machines.
ahead of the machines.
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Thank you.
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(Applause)
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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