The jobs we'll lose to machines -- and the ones we won't | Anthony Goldbloom

Translator: 潘 可儿 Reviewer: Wink Wong

So this is my niece.

呢個係我嘅姪女/外甥女

Her name is Yahli.

佢叫做 Yahli

She is nine months old.

佢依家 9 個月大

Her mum is a doctor, and her dad is a lawyer.

佢嘅媽咪係醫生，爹哋係律師

By the time Yahli goes to college,

等到 Yahli 去返大學嗰時

the jobs her parents do are going to look dramatically different.

佢父母依家做嘅工作將會有巨變

In 2013, researchers at Oxford University did a study on the future of work.

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

They concluded that almost one in every two jobs have a high risk

佢哋推斷差不多每兩份工作

of being automated by machines.

就有一份會面臨畀機器取代嘅危險

Machine learning is the technology

「機器學習」科技就係呢種威脅嘅元兇

that's responsible for most of this disruption.

It's the most powerful branch of artificial intelligence.

佢係人工智能最強勁嘅學科分支

It allows machines to learn from data

佢令到機器可以從數據中學習

and mimic some of the things that humans can do.

模仿有啲人類會做嘅事

My company, Kaggle, operates on the cutting edge of machine learning.

我間公司 Kaggle 企喺機器學習嘅最前線

We bring together hundreds of thousands of experts

我哋匯聚咗成千上萬嘅專家

to solve important problems for industry and academia.

嚟解決工業、學術嘅重大問題

This gives us a unique perspective on what machines can do,

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

what they can't do

同乜嘢唔可以做

and what jobs they might automate or threaten.

乜嘢工可以自動化同乜嘢工受到威脅

Machine learning started making its way into industry in the early '90s.

機器學習喺90年代初期喺工業起步

It started with relatively simple tasks.

一開始做啲比較簡單嘅任務

It started with things like assessing credit risk from loan applications,

例如評估貸款申請嘅信用風險

sorting the mail by reading handwritten characters from zip codes.

識別手寫嘅郵政編碼嚟揀信

Over the past few years, we have made dramatic breakthroughs.

喺過去幾年，我哋取得驚人嘅突破

Machine learning is now capable of far, far more complex tasks.

依家機器學習已經做到更加複雜嘅任務

In 2012, Kaggle challenged its community

2012 年， Kaggle 考驗佢嘅團隊

to build an algorithm that could grade high-school essays.

要佢哋設計一條批改高中習作嘅算法

The winning algorithms were able to match the grades

獲勝算法嘅打分

能夠同人類老師嘅打分相符

given by human teachers.

Last year, we issued an even more difficult challenge.

舊年，我哋提出咗更難嘅挑戰

Can you take images of the eye and diagnose an eye disease

你可唔可以僅憑眼睛嘅圖像就診斷出

called diabetic retinopathy?

病人患有「糖尿病視網膜病變」？

Again, the winning algorithms were able to match the diagnoses

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

given by human ophthalmologists.

Now, given the right data, machines are going to outperform humans

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

at tasks like this.

A teacher might read 10,000 essays over a 40-year career.

喺 40 職業生涯入面

一位老師可以批改一萬份習作

An ophthalmologist might see 50,000 eyes.

一位眼科醫生可以為五萬雙眼睛診斷

A machine can read millions of essays or see millions of eyes

而一部機器可以喺幾分鐘之內

批改成千上萬份習作

within minutes.

或者檢查數以百萬對嘅眼睛

We have no chance of competing against machines

對於頻繁、大量嘅工作

on frequent, high-volume tasks.

我哋簡直無可能同機器競爭

But there are things we can do that machines can't do.

但係有啲嘢係機器無法取代我哋嘅

Where machines have made very little progress

就係當要處理新嘅情況時

is in tackling novel situations.

機器往往一籌莫展

They can't handle things they haven't seen many times before.

佢哋只可以處理多次出現嘅情況

The fundamental limitations of machine learning

機器學習嘅基本限制在於

is that it needs to learn from large volumes of past data.

佢需要通過以前大量嘅數據嚟學習

Now, humans don't.

但係，人類唔需要

We have the ability to connect seemingly disparate threads

我哋有能力串連看似無關嘅線索

to solve problems we've never seen before.

嚟解決我哋從未遇見嘅情況

Percy Spencer was a physicist working on radar during World War II,

Percy Spencer 係一名研究 雷達嘅物理學家

二戰時期佢發現磁電管可以融化朱古力

when he noticed the magnetron was melting his chocolate bar.

He was able to connect his understanding of electromagnetic radiation

佢將自己對電磁輻射嘅理解

with his knowledge of cooking

同烹飪知識結合起嚟

in order to invent -- any guesses? -- the microwave oven.

發明咗..要唔要估下？就係微波爐

Now, this is a particularly remarkable example of creativity.

呢個發明嘅例子，令人拍案叫絕

But this sort of cross-pollination happens for each of us in small ways

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

thousands of times per day.

Machines cannot compete with us

要處理未知情況嗰陣

when it comes to tackling novel situations,

機器比唔上我哋

and this puts a fundamental limit on the human tasks

咁樣做成咗機器取代

that machines will automate.

人類工作嘅基本限制

So what does this mean for the future of work?

所以佢對未來工作嘅意義係乜嘢？

The future state of any single job lies in the answer to a single question:

任何工作嘅前景都取決於一個問題

To what extent is that job reducible to frequent, high-volume tasks,

呢份工可以減輕頻密又 大量嘅任務到乜嘢程度

and to what extent does it involve tackling novel situations?

又喺幾大程度上會遇到未知情況？

On frequent, high-volume tasks, machines are getting smarter and smarter.

機器處理頻繁又大量嘅任務越來越叻

Today they grade essays. They diagnose certain diseases.

依家佢哋可以批改習作、診斷一啲疾病

Over coming years, they're going to conduct our audits,

幾年之後，雖然機器可以幫我哋做審計

and they're going to read boilerplate from legal contracts.

閱讀法律合同中嘅樣板

Accountants and lawyers are still needed.

但我哋依然需要會計師同律師

They're going to be needed for complex tax structuring,

分析複雜嘅稅務架構

探索訴訟法律

for pathbreaking litigation.

但係機器會降低工作對人嘅要求

But machines will shrink their ranks

and make these jobs harder to come by.

令人更難就業

Now, as mentioned,

依家，好似之前所講

machines are not making progress on novel situations.

機器喺處理未知情況方面毫無進展

The copy behind a marketing campaign needs to grab consumers' attention.

市場營銷為了捉住消費者嘅眼球

It has to stand out from the crowd.

需要脫穎而出

佢哋嘅策略係要喺市場夾縫中搵到商機

Business strategy means finding gaps in the market,

things that nobody else is doing.

尋找獨一無二之處

It will be humans that are creating the copy behind our marketing campaigns,

只有人類才能喺幕後策劃市場營銷

and it will be humans that are developing our business strategy.

只有人類才能不斷升級商業戰略

So Yahli, whatever you decide to do,

所以 Yahli，無論你決定做乜嘢

let every day bring you a new challenge.

請你每日都要面對新挑戰

If it does, then you will stay ahead of the machines.

咁樣你就可以比機器遙遙領先

Thank you.

多謝

(Applause)

（掌聲）