TED2016
Anthony Goldbloom: The jobs we'll lose to machines -- and the ones we won't
Anthony Goldbloom: 機器能夠或不能夠取代我哋嘅工作
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今日機器學習已經唔再限於好似評估信用風險、分揀郵件咁簡單嘅工作。佢可以完成更加複雜嘅任務，例如評分同診斷疾病。一個難題隨著呢啲科技進步而嚟：未來機器人會唔會取代你嘅工作？
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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呢個係我嘅姪女/外甥女
00:14
Her name is Yahli.
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佢叫做 Yahli
00:16
She is nine months old.
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佢依家 9 個月大
00:18
Her mum is a doctor,
and her dad is a lawyer.
and her dad is a lawyer.
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佢嘅媽咪係醫生，爹哋係律師
00:21
By the time Yahli goes to college,
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等到 Yahli 去返大學嗰時
00:23
the jobs her parents do
are going to look dramatically different.
are going to look dramatically different.
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佢父母依家做嘅工作將會有巨變
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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牛津大學嘅研究人員喺2013年
做咗一個關於未來工作嘅研究
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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佢哋推斷差不多每兩份工作
就有一份會面臨畀機器取代嘅危險
00:36
of being automated by machines.
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00:40
Machine learning is the technology
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「機器學習」科技就係呢種威脅嘅元兇
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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佢係人工智能最強勁嘅學科分支
00:47
It allows machines to learn from data
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佢令到機器可以從數據中學習
00:49
and mimic some of the things
that humans can do.
that humans can do.
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模仿有啲人類會做嘅事
00:51
My company, Kaggle, operates
on the cutting edge of machine learning.
on the cutting edge of machine learning.
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我間公司 Kaggle 企喺機器學習嘅最前線
00:55
We bring together
hundreds of thousands of experts
hundreds of thousands of experts
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我哋匯聚咗成千上萬嘅專家
00:57
to solve important problems
for industry and academia.
for industry and academia.
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嚟解決工業、學術嘅重大問題
01:01
This gives us a unique perspective
on what machines can do,
on what machines can do,
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因為咁樣令我哋對機器有獨特嘅見解
知道乜嘢機器可以做
01:04
what they can't do
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同乜嘢唔可以做
01:05
and what jobs they might
automate or threaten.
automate or threaten.
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乜嘢工可以自動化同乜嘢工受到威脅
01:09
Machine learning started making its way
into industry in the early '90s.
into industry in the early '90s.
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機器學習喺90年代初期喺工業起步
01:12
It started with relatively simple tasks.
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一開始做啲比較簡單嘅任務
01:15
It started with things like assessing
credit risk from loan applications,
credit risk from loan applications,
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例如評估貸款申請嘅信用風險
01:19
sorting the mail by reading
handwritten characters from zip codes.
handwritten characters from zip codes.
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識別手寫嘅郵政編碼嚟揀信
01:24
Over the past few years, we have made
dramatic breakthroughs.
dramatic breakthroughs.
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喺過去幾年，我哋取得驚人嘅突破
01:27
Machine learning is now capable
of far, far more complex tasks.
of far, far more complex tasks.
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依家機器學習已經做到更加複雜嘅任務
01:31
In 2012, Kaggle challenged its community
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2012 年， Kaggle 考驗佢嘅團隊
01:35
to build an algorithm
that could grade high-school essays.
that could grade high-school essays.
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要佢哋設計一條批改高中習作嘅算法
01:38
The winning algorithms
were able to match the grades
were able to match the grades
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獲勝算法嘅打分
能夠同人類老師嘅打分相符
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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舊年，我哋提出咗更難嘅挑戰
01:46
Can you take images of the eye
and diagnose an eye disease
and diagnose an eye disease
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你可唔可以僅憑眼睛嘅圖像就診斷出
01:49
called diabetic retinopathy?
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病人患有「糖尿病視網膜病變」？
01:51
Again, the winning algorithms
were able to match the diagnoses
were able to match the diagnoses
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同樣，勝出嘅算法做嘅診斷結果
可以同人類眼科醫生嘅診斷結果符合
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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依家只要輸入正確數據，機器就可以
比人類做好似呢啲工作更加出色
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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喺 40 職業生涯入面
一位老師可以批改一萬份習作
02:06
An ophthalmologist might see 50,000 eyes.
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一位眼科醫生可以為五萬雙眼睛診斷
02:08
A machine can read millions of essays
or see millions of eyes
or see millions of eyes
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而一部機器可以喺幾分鐘之內
批改成千上萬份習作
02:12
within minutes.
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或者檢查數以百萬對嘅眼睛
02:14
We have no chance of competing
against machines
against machines
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對於頻繁、大量嘅工作
02:17
on frequent, high-volume tasks.
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我哋簡直無可能同機器競爭
02:20
But there are things we can do
that machines can't do.
that machines can't do.
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但係有啲嘢係機器無法取代我哋嘅
02:24
Where machines have made
very little progress
very little progress
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就係當要處理新嘅情況時
02:27
is in tackling novel situations.
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機器往往一籌莫展
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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佢哋只可以處理多次出現嘅情況
02:33
The fundamental limitations
of machine learning
of machine learning
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機器學習嘅基本限制在於
02:35
is that it needs to learn
from large volumes of past data.
from large volumes of past data.
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佢需要通過以前大量嘅數據嚟學習
02:39
Now, humans don't.
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但係，人類唔需要
02:41
We have the ability to connect
seemingly disparate threads
seemingly disparate threads
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我哋有能力串連看似無關嘅線索
02:44
to solve problems we've never seen before.
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嚟解決我哋從未遇見嘅情況
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 係一名研究
雷達嘅物理學家
雷達嘅物理學家
二戰時期佢發現磁電管可以融化朱古力
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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佢將自己對電磁輻射嘅理解
02:58
with his knowledge of cooking
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同烹飪知識結合起嚟
02:59
in order to invent -- any guesses? --
the microwave oven.
the microwave oven.
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發明咗..要唔要估下？就係微波爐
呢個發明嘅例子，令人拍案叫絕
03:03
Now, this is a particularly remarkable
example of creativity.
example of creativity.
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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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但係呢種「異花傳粉」每一天都會
喺我哋生活細微處發生無數次
03:10
thousands of times per day.
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03:12
Machines cannot compete with us
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要處理未知情況嗰陣
03:14
when it comes to tackling
novel situations,
novel situations,
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機器比唔上我哋
03:16
and this puts a fundamental limit
on the human tasks
on the human tasks
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咁樣做成咗機器取代
人類工作嘅基本限制
03:19
that machines will automate.
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03:22
So what does this mean
for the future of work?
for the future of work?
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所以佢對未來工作嘅意義係乜嘢？
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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任何工作嘅前景都取決於一個問題
03:29
To what extent is that job reducible
to frequent, high-volume tasks,
to frequent, high-volume tasks,
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呢份工可以減輕頻密又
大量嘅任務到乜嘢程度
大量嘅任務到乜嘢程度
03:34
and to what extent does it involve
tackling novel situations?
tackling novel situations?
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又喺幾大程度上會遇到未知情況？
03:37
On frequent, high-volume tasks,
machines are getting smarter and smarter.
machines are getting smarter and smarter.
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機器處理頻繁又大量嘅任務越來越叻
03:42
Today they grade essays.
They diagnose certain diseases.
They diagnose certain diseases.
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依家佢哋可以批改習作、診斷一啲疾病
03:44
Over coming years,
they're going to conduct our audits,
they're going to conduct our audits,
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幾年之後，雖然機器可以幫我哋做審計
03:47
and they're going to read boilerplate
from legal contracts.
from legal contracts.
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閱讀法律合同中嘅樣板
03:50
Accountants and lawyers are still needed.
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但我哋依然需要會計師同律師
分析複雜嘅稅務架構
03:52
They're going to be needed
for complex tax structuring,
for complex tax structuring,
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探索訴訟法律
03:55
for pathbreaking litigation.
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但係機器會降低工作對人嘅要求
03:57
But machines will shrink their ranks
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令人更難就業
03:58
and make these jobs harder to come by.
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依家，好似之前所講
04:00
Now, as mentioned,
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機器喺處理未知情況方面毫無進展
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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市場營銷為了捉住消費者嘅眼球
04:08
It has to stand out from the crowd.
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需要脫穎而出
佢哋嘅策略係要喺市場夾縫中搵到商機
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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尋找獨一無二之處
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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只有人類才能喺幕後策劃市場營銷
04:18
and it will be humans that are developing
our business strategy.
our business strategy.
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只有人類才能不斷升級商業戰略
04:21
So Yahli, whatever you decide to do,
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所以 Yahli，無論你決定做乜嘢
04:24
let every day bring you a new challenge.
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請你每日都要面對新挑戰
04:27
If it does, then you will stay
ahead of the machines.
ahead of the machines.
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咁樣你就可以比機器遙遙領先
04:31
Thank you.
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多謝
04:32
(Applause)
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（掌聲）
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