TED2016
Anthony Goldbloom: The jobs we'll lose to machines -- and the ones we won't
Anthony Goldbloom: 我们的工作将被机器取代，但也有例外
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机器学习不再是完成简单的任务，如评估信用风险和检索邮件 - 如今，它能够承担更复杂的工作，如评判作文和诊断疾病。 这些进步带来了一个令人不安的问题：未来机器人会抢走你的工作吗？
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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这是我的侄女。
00:14
Her name is Yahli.
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她叫Yahl。
她只有九个月大。
00:16
She is nine months old.
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00:18
Her mum is a doctor,
and her dad is a lawyer.
and her dad is a lawyer.
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她妈妈是一名医生，
爸爸是一名律师。
爸爸是一名律师。
00:21
By the time Yahli goes to college,
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等到Yahli上大学的时候，
像她父母这样的工作将面目全非。
00:23
the jobs her parents do
are going to look dramatically different.
are going to look dramatically different.
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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年，牛津大学的研究人员
做了一项关于未来就业的研究。
做了一项关于未来就业的研究。
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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他们得出结论：差不多将近
一半的工作都有被机器
一半的工作都有被机器
自动化取代的危险。
00:36
of being automated by machines.
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00:40
Machine learning is the technology
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而机器学习
应对这种颠覆负主要责任。
00:42
that's responsible for most
of this disruption.
of this disruption.
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它是人工智能最强大的分支。
00:44
It's the most powerful branch
of artificial intelligence.
of artificial intelligence.
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允许机器从现有数据中学习，
00:47
It allows machines to learn from data
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00:49
and mimic some of the things
that humans can do.
that humans can do.
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并模仿人类的所作所为。
我的公司Kaggle
专注于尖端的机器学习。
专注于尖端的机器学习。
00:51
My company, Kaggle, operates
on the cutting edge of machine learning.
on the cutting edge of machine learning.
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我们召集了成千上万的专家
00:55
We bring together
hundreds of thousands of experts
hundreds of thousands of experts
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正为工业和学术界
寻找重要问题的答案。
寻找重要问题的答案。
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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因此，我们可以从独特的视角来观察，
机器可以做什么，不可以做什么，
01:04
what they can't do
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哪些工作可以被自动化或受到威胁。
01:05
and what jobs they might
automate or threaten.
automate or threaten.
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01:09
Machine learning started making its way
into industry in the early '90s.
into industry in the early '90s.
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机器学习是在90年代初
进入人们的视野。
进入人们的视野。
一开始，它只是执行
一些相对简单的任务。
一些相对简单的任务。
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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像评估贷款申请的信用风险，
通过识别手写的邮政编码来检索邮件。
01:19
sorting the mail by reading
handwritten characters from zip codes.
handwritten characters from zip codes.
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01:24
Over the past few years, we have made
dramatic breakthroughs.
dramatic breakthroughs.
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在过去几年里，我们取得了突破性进展。
01:27
Machine learning is now capable
of far, far more complex tasks.
of far, far more complex tasks.
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现在，机器学习可以
完成非常复杂的任务。
完成非常复杂的任务。
01:31
In 2012, Kaggle challenged its community
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2012年，Kaggle给当地学校出了个难题，
01:35
to build an algorithm
that could grade high-school essays.
that could grade high-school essays.
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设计一个算法来评判高中作文。
获胜的算法给出的分数居然
01:38
The winning algorithms
were able to match the grades
were able to match the grades
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和真正老师给出的分数相符。
01:40
given by human teachers.
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01:43
Last year, we issued
an even more difficult challenge.
an even more difficult challenge.
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去年，我们出了一道更难的题。
你能从拍摄出的眼睛图像中
诊断出糖尿病性
诊断出糖尿病性
01:46
Can you take images of the eye
and diagnose an eye disease
and diagnose an eye disease
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视网膜病变吗？
01:49
called diabetic retinopathy?
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01:51
Again, the winning algorithms
were able to match the diagnoses
were able to match the diagnoses
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再一次，获胜的演算法给出的诊断
和眼科医生的诊断相符。
01:55
given by human ophthalmologists.
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01:57
Now, given the right data,
machines are going to outperform humans
machines are going to outperform humans
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类似于这样的任务，
只要给定正确的数据，
只要给定正确的数据，
机器将完全超越人类。
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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一位老师在40年的职业生涯中
可能审阅一万篇作文。
可能审阅一万篇作文。
02:06
An ophthalmologist might see 50,000 eyes.
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一名眼科医生，大概可以检查
5万只眼睛。
5万只眼睛。
但在短短几分钟之内，
机器可以审阅百万篇文章
机器可以审阅百万篇文章
02:08
A machine can read millions of essays
or see millions of eyes
or see millions of eyes
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或检查数百万只眼睛。
02:12
within minutes.
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02:14
We have no chance of competing
against machines
against machines
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对于频繁，大批量的任务
我们无法与机器抗衡。
02:17
on frequent, high-volume tasks.
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02:20
But there are things we can do
that machines can't do.
that machines can't do.
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但有些事情机器却无能为力。
02:24
Where machines have made
very little progress
very little progress
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机器在解决新情况方面
进展甚微。
02:27
is in tackling novel situations.
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它们还不能处理未曾反复接触的事情。
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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02:33
The fundamental limitations
of machine learning
of machine learning
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机器学习致命的局限性在于
02:35
is that it needs to learn
from large volumes of past data.
from large volumes of past data.
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它需要从大量已知的数据中总结经验。
人类则不然。
02:39
Now, humans don't.
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我们有一种能把看似毫不相关的事物
联系起来的能力，
联系起来的能力，
02:41
We have the ability to connect
seemingly disparate threads
seemingly disparate threads
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从而解决从未见过的问题
02:44
to solve problems we've never seen before.
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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是一个物理学家，
在二战期间从事雷达的研究工作，
在二战期间从事雷达的研究工作，
他注意到磁控管融化了他的巧克力。
02:51
when he noticed the magnetron
was melting his chocolate bar.
was melting his chocolate bar.
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02:54
He was able to connect his understanding
of electromagnetic radiation
of electromagnetic radiation
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他从对电磁辐射的理解
联想到烹饪，
02:58
with his knowledge of cooking
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因此发明了——猜猜是什么？——
微波炉。
微波炉。
02:59
in order to invent -- any guesses? --
the microwave oven.
the microwave oven.
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03:03
Now, this is a particularly remarkable
example of creativity.
example of creativity.
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这是个非常杰出的创新例子。
但这种跨界转型，每天正以
难以察觉的方式在我们身边
难以察觉的方式在我们身边
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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发生成千上百次。
03:10
thousands of times per day.
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03:12
Machines cannot compete with us
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在创新方面
03:14
when it comes to tackling
novel situations,
novel situations,
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机器无法与我们抗衡。
这将使机器自动化取代人工
03:16
and this puts a fundamental limit
on the human tasks
on the human tasks
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03:19
that machines will automate.
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受到限制。
03:22
So what does this mean
for the future of work?
for the future of work?
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那么这对未来的工作意味着什么呢？
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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未来工作的状态
完全取决于一个问题：
完全取决于一个问题：
这种工作在多大程度上可以简化为
频繁，大批量的任务，
频繁，大批量的任务，
03:29
To what extent is that job reducible
to frequent, high-volume tasks,
to frequent, high-volume tasks,
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又涉及多少对创新能力的要求？
03:34
and to what extent does it involve
tackling novel situations?
tackling novel situations?
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03:37
On frequent, high-volume tasks,
machines are getting smarter and smarter.
machines are getting smarter and smarter.
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对于那些频繁，大批量的任务，
机器变得越来越智能。
机器变得越来越智能。
如今， 它们可以评判作文，
诊断某些疾病。
诊断某些疾病。
03:42
Today they grade essays.
They diagnose certain diseases.
They diagnose certain diseases.
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再过几年，它们将可以进行审计，
03:44
Over coming years,
they're going to conduct our audits,
they're going to conduct our audits,
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将能审阅法律合同样本。
03:47
and they're going to read boilerplate
from legal contracts.
from legal contracts.
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尽管会计师和律师还是需要的。
03:50
Accountants and lawyers are still needed.
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但他们只需要研究复杂的税收结构，
03:52
They're going to be needed
for complex tax structuring,
for complex tax structuring,
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或无先例的诉讼过程。
03:55
for pathbreaking litigation.
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但机器将会挤占他们的位置，
03:57
But machines will shrink their ranks
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增加就业难度。
03:58
and make these jobs harder to come by.
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如上所述，
04:00
Now, as mentioned,
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在创新方面机器没有取得太大进展。
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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营销文案需要抓住消费者的心理。
脱颖而出是关键。
04:08
It has to stand out from the crowd.
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商业策略需要找到市场上
04:10
Business strategy means
finding gaps in the market,
finding gaps in the market,
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还无人问津的空白。
04:12
things that nobody else is doing.
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人类将是营销文案的创造者，
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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人类才能推动商业战略发展。
04:18
and it will be humans that are developing
our business strategy.
our business strategy.
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所以Yahli，无论你将来决定做什么，
04:21
So Yahli, whatever you decide to do,
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让每一天都带给你新的挑战。
04:24
let every day bring you a new challenge.
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04:27
If it does, then you will stay
ahead of the machines.
ahead of the machines.
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如果是那样，
你的未来将无法被机器取代。
你的未来将无法被机器取代。
04:31
Thank you.
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谢谢。
（掌声 ）
04:32
(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