before machines do your job better than you do?

過去自動化指機械只能在工廠內執行

Automation used to mean big stupid machines doing repetitive work in factories.

無須用腦且高重複性的工作

Today they can land aircraft, diagnose cancer and trade stocks.

現在，他們學會了降落飛機，診斷癌症和貿易股票

We are entering a new age of automation unlike anything that's come before.

我們正在進入前所未有的自動化新時代

According to a 2013 study, almost half of all jobs in the

2013年的一項研究表明， 美國幾乎一半的工作可能在未來二十年內實現自動化

US could potentially be automated in the next two decades.

可是等等… 自動化不是已經存在幾十年了嗎？

But wait; Hasn't automation been around for decades?

這一次有什麼不同？

What's different this time?

（以前的創新）

Things used to be simple.

以前一切事物簡單直接

Innovation made human work easier and productivity rose.

創新使人類工作變得更加容易 生產效率也隨之提高

Which means that more staff or services could be produced

這意味著在單位人數和時間內 可以生產更多的產品及服務

per hour using the same amount of human workers.

雖然減少了許多就業機會 不過同時也創造了更多更好的工作機會

This eliminated many jobs, but also created other jobs that were better

為解決人們增長的工作需求提供了重要的幫助

which was important because the growing population needed work.

簡單地說，創新帶來更高的生產效率 減少了舊工作，但同時帶來更多更新更好的工作

So, in a nutshell, innovation, higher productivity,

總體而言，大家都適應了這個模式 生活水準也有所提高

fewer old jobs, and many new and often better jobs.

人類的發展是可以很明顯區分的 在很長一段期間裡，我們大多從事農業的工作

Overall, this worked well for a majority of people and living standards improved.

工業革命後，某些農民走向製造業 而當自動化機械普及後，人類又走向了服務業

There's a clear progression in terms of what humans did for

在不久之前，人類進入了資訊時代

a living. For the longest time, we worked in agriculture.

剎那間，所有的規則都被改變了 我們的工作被比過去更有效的機器給取代了

With the Industrial Revolution, this shift into production jobs and as

這顯然令人擔憂 不過…創新一定會拯救我們的，對吧？

automation became more widespread, humans shifted into service jobs.

雖然新資訊時代產業蓬勃發展 但是他們創造的新工作卻越來越少

And then only a few moments ago in human history, the Information Age happened.

1979年，通用汽車雇用超過80萬工人 賺取約110億美元

Suddenly, the rules were different. Our jobs are now being

在2012年，Google賺取了約140億美元 卻只聘請了58萬人

taken over by machines much faster than they were in the past.

你可能覺得這種比較沒什麼意義 但Google就是一個創造新就業機會的——新興產業

That's worrying of course... but innovation will clearly save us, right?

舊行業逐漸失去動力 單以汽車行業為例 - 當100年前他們新興時

While new information age industries are booming,

他們創造了許多巨大的行業 汽車改變了我們的生活方式

they are creating fewer and fewer new jobs.

我們的基礎設施，和我們的城市規劃

In 1979, General Motors employed more than 800,000

數以百萬計的人也因此直接或間接找到工作 幾十年投資維持了整個趨勢

workers and made about $11 billion US dollars.

如今，這個過程已基本飽和

In 2012, Google made about $14 billion US dollars while employing 58,000 people.

在汽車行業的創新已經不能像新興時創造那麼多就業崗位 雖然電動車還是非常有潛力的

You may not like this comparison, but Google is

那也不會突然創造數百萬個新的就業機會 那等等……網路呢 ?

an example of what created new jobs in the past:

一些資訊專家認為 網路是電力普及衍生的產物

Innovative new industries.

如果用此作為對照，我們可以看出 新時代創新與舊時代創新的區別

Old innovative industries are running out of steam. Just look at cars.

網路創造了新的產業

When they became a thing 100 years ago, they created huge industries.

但它所創造的不足以彌補人口增長的

Cars transformed our way of life, our infrastructure, and our cities.

更不能補足被網路傷害的舊產業

Millions of people found jobs either directly or indirectly.

百視達（一家錄影帶出租公司）在巔峰期 2004年 聘請了 84,000名員工，並獲得 60億美元的收入

Decades of investment kept this momentum going.

但在2016 Netflix公司只有有4、500多名員工 卻可盈利 90億美元

Today, this process is largely complete. Innovation in the

或以我們自己為例，雖然全職的員工只有 12人

car industry does not create as many jobs as it used to.

Kurzgesagt卻可以被百萬人收看

While electric cars are great and all, they won't create millions of new jobs.

一個電視台若要達到如此效果需要更多更多的員工

But wait; what about the internet?

資訊時代的創新並未能夠創造足夠的新工作機會

Some technologists argue that the Internet is an

這已經夠糟糕了 但現在新一代的自動化潮流正在慢慢取代人們的工作

innovation on a par of the introduction of electricity.

（機器的新種類）

If we go with this comparison, we see how our

要了解這一點，我們需要先理解自己 人類的進步是基於勞動的分配

modern innovation differs from the old one.

千年下來，我們的工作愈發地專業化

The Internet created new industries,

即使現在的智慧機械 在處理某些複雜的事情上表現仍不理想

but they're not creating enough jobs to keep up

但它們能在特定、可預測性高的工作環境下完美地工作

with population growth or to compensate for the industries the Internet is killing.

這摧毀了許多工廠的工作崗位

At its peak in 2004,

不過如果我們詳細研究複雜漫長的工作

Blockbuster had 84,000 employees and made $6 billion US dollars in revenue.

我們會發現，其實它們都是由許許多多 簡單重複的小工作一件接一件地串聯下來的

In 2016, Netflix had 4,500 employees and made $9 billion dollars in revenue.

現在的機器已經差不多能夠有效地把大而複雜的事物 打散成各種重複性高的工作

Or take us, for example.

而人類將逐漸地失去專精化這塊領地

With a full-time team of just 12 people, Kurzgesagt reaches millions of people.

我們已在被淘汰的邊緣

A TV station with the same amount of viewers needs way more employees.

3C產品通過機器學習 以大量訊息及通過分析數據獲取技能

Innovation in the Information Age doesn't equate to

它們會因為訊息的串聯而表現更佳

the creation of enough new jobs, which would be bad

機械能夠自我學習

enough on its own but now, a new wave of automation and

欲使電腦專精於某事情，我們只需提供大量有關的數據

a new generation of machines is slowly taking over.

當你在網上購物時 它會慢慢學習並提示一些你可能感興趣的物品 從而讓你買更多東西

To understand this, we need to understand ourselves first.

機器學習的快速發展依賴於這幾年來

Human progress is based on the division of labor.

人類開始收集有關一切事物的數據

As we advanced over thousands of years, our jobs became more and more specialized.

行為、天氣模式、醫療記錄、通訊系統

While even our smartest machines are bad at doing complicated jobs,

旅遊數據，當然還有有關工作習慣的數據

they are extremely good at doing narrowly defined and predictable tasks.

我們已意外的建立了一個巨大的圖書館 而機器可以使用它來學習人類如何做事

This is what destroyed factory jobs.

以及如何做得更好 這些數位化的機械可能是所有工作的最大殺手

But look at a complex job long and hard enough,

它們可以快速的複製 你還可以免費的升級它們

and you'll find that it's really just many narrowly

只需要使用新的代碼，而不需要投入材料

defined and predictable tasks one after another.

這樣他們就有能力工作的更快，有多快呢？

Machines are on the brink of becoming so good at

如果你的工作涉及到使用現今電腦的複雜程式 那麼你可能會早於在工廠工作的人失去工作

breaking down complex jobs into many predictable ones,

這有一個真實世界的例子展示這種過渡是如何發生的

that for a lot of people, there will be no further room to specialize.

一家舊金山公司提供某大公司一款管理軟體

We are on the verge of being outcompeted.

這款軟體可以勝任中層管理人員的職務

Digital machines do this via machine learning,

當它被指派去處理一個新的工作項目時

which enables them to acquire information and skills by analyzing data.

軟體首先會區分哪些工作可以使用自動化機械

This makes them become better at something through the relationships they discover.

而哪些需要專業人士完成 然後在網路上招募一個由自由業者組成的團隊

Machines teach themselves.

然後軟體給人類分配任務

We make this possible by giving a computer a lot of

監視工作品質，追蹤個人表現

data about the thing we wanted to become better at.

直到這個項目完全完成 好的，這聽起來貌似不算太壞

Show a machine all the things you bought online,

這台機器只取代了一種職業 卻為許多自由業者創造了工作機會，不是嗎？

and it will slowly learn what to recommend to you, so you buy more things.

其實在自由業者完成他們任務時 學習演算法會追蹤他們

Machine learning is now meeting more of its potential because in recent years,

然後收集有關他們工作的數據 以及這些任務實際由什麼組成

humans have started to gather data about everything.

所以實際發生的是 自由業者正在教會機器如何取代他們

Behavior, weather patterns, medical records, communication systems,

這個軟體平均可以在第一年減少50%的成本

travel data, and of course, data about what we do at work.

而在第二年減少25% 這只是許多例子中的一種

What we've created by accident is a huge library machines can

現在在許多領域 機械和程式可以做的與人類一樣好甚至更好

use to learn how humans do things and learn to do them better.

從藥劑師到分析師 記者到放射科醫師

These digital machines might be the biggest job killer of all.

收銀員到銀行櫃員 或是翻漢堡肉的非技術人員

They can be replicated instantly and for free.

所有這些工作都不會一夜消失 但做這些工作的人會越來越少

When they improve, you don't need to invest in

這會導致什麼，讓我們下次再說

big metal things; you can just use the new code.

職業消失是件可怕的事情，但這只是這個故事的一半

And they have the ability to get better fast. How fast?

(要停下來，我們需要進步得非常快)

If your work involves complex work on a computer today, you might be out

一個舊的職業被一個新的職業替代是完全不夠的 我們需要不斷創造新的工作崗位

of work even sooner than the people who still have jobs in factories.

因為世界人口在不斷增長 過去我們透過創新解決了這個

There are actual real-world examples of how this transition might be happening.

但自1973年以來，美國新的就業機會已經開始收縮

A San Francisco company offers a project management software for big

二十一世紀的第一個十年

corporations, which is supposed to eliminate middle management positions.

是美國的工作總量第一次沒有增長的十年

When it's hired for a new project, the software first decides which jobs

為了平衡人口增長 一個國家每個月需要創造150,000個新的就業機會

can be automated and precisely where it needs actual professional humans.

這是一個壞消息 而且它正在影響人類的生活水準

It then helps assemble a team of freelancers over the Internet.

在過去，隨著生產力的提高，顯而易見地

The software then distributes tasks to the humans, and controls the quality

更多更好的就業機會將被創造 但是數據卻告訴我們一個不同的故事

of the work, tracking individual performance until the project is complete.

在1998年美國所有的工人共工作了1940億小時

Okay. This doesn't sound too bad.

在15年後的2013年他們多生產了42%的生產量

While this machine is killing one job, it creates jobs for freelancers, right?

但美國工人依然只工作了1940億小時

Well, as the freelancers complete their tasks,

這意味著儘管生產效率大幅增長 且數以千計的新業務被開拓

learning algorithms track them, and gather data

而美國的人口增長超過4000萬人 工人的工作時間在15年後的今天卻沒有絲毫的增長

about their work, and which tasks it consists of.

與此同時 美國新畢業大學生的工資在過去十年一直在下降

So what's actually happening, is that

高達40%的應屆畢業生被迫接受不需要學位的工作

the freelancers are teaching a machine how to replace them.

（結論）

On average, this software reduces costs by about 50%

生產力正在與人類的勞動分離

in the first year, and by another 25% in the second year.

創新的實質與資訊時代 與我們之前所遇到的不再相同

This is only one example of many.

這個改變在好幾年前就已經開始 並且已經很順利地推展了

There are machines and programs getting as good

即使沒有新的科技出現 像自動駕駛汽車或機械會計師

or better than humans in all kinds of fields.

這次自動化看起來是不同的 這一次機械可能真的會取代我們的工作

From pharmacists to analysts, journalists to radiologists,

我們的經濟體系基於人民消費

cashiers to bank tellers, or the unskilled worker flipping burgers.

但如果越來越少的人有體面的工作 誰來負責消費呢？

All of these jobs won't disappear overnight,

我們的生產將會越來越廉價 當生產廉價到一定程度時

but fewer and fewer humans will be doing them.

只有非常少數人可以買得起我們現在所有的產品和服務

We'll discuss a few cases in a follow-up video.

或者未來我們將要看到 少數擁有機械的大富翁主宰其他剩餘的人

But while jobs disappearing is bad, it's only half of the story.

我們的未來真的那麼黑暗嗎？

It's not enough to substitute old jobs with new ones.

這部影片的基調是比較黑暗的 在現實中完全無法確定事情會朝悲觀那面發展

We need to be generating new jobs constantly

資訊時代和現代自動化技術 可能是一個巨大的機會

because the world population is growing.

去改變人類社會，大幅減少貧困和不平等現象

In the past we have solved this through innovation.

這可能是人類歷史上的一個開創性時刻

But, since 1973, the generation of new jobs in the US has begun to shrink.

我們將在這系列的影片中的第二部分 討論這種潛力和可能性，如全民基本收入

And the first decade of the 21st century, was the first one, where

我們應該仔細思考，因為有一件事是確定的

the total amount of jobs in the US, did not grow for the first time.

機械不會慢慢走進我們的生活 因為他們已經在我們的生活中了

In a country that needs to create up to 150,000 new jobs per

我們用了900小時左右的時間來製作這個影片

month, just to keep up with population growth, this is bad news.

製作週期超過九個月

This is also starting to affect standards of living.

沒有您在patreon.com的贊助 製作這樣的影片是不可能的

In the past, it was seen as obvious that with rising

如果您想支持我們並獲得Kurzgesagt客製化小鳥作為禮物， 您的贊助能大大地幫助我們

productivity, more and better jobs would be created.

這部影片參考了兩本非常棒的書籍

But the numbers tell a different story.

《The Rise of the Robots》 以及《The Second Machine Age》

In 1998, US workers worked a total of 194 billion hours.

您可以在影片下方的簡介中找到它們的購買網址

Over the course of the next 15 years, their output increased by 42 percent.

我們製作了一個小的機器人海報

But in 2013, the amount of hours worked by US workers was still 194 billion hours.

您可以在我們的DFTBA商店中 購買這款海報和許多其他的商品

What this means, is that despite productivity growing

這部影片是一個大的、 講述科技已經或將永久改變人類生活的系列的其中一部

drastically, thousands of new businesses opening up, and the

如果你想繼續了解這方面的知識 這裡有一個小的播放列表

US population growing by over 40 million, there was no

growth at all in the number of hours worked in 15 years.

At the same time, wages for new university graduates

in the US, have been declining for the past decade,

while up to 40 percent of new graduates, are forced

to take on jobs that don't require a degree.

Productivity is separating from human labor.

The nature of innovation in the Information Age is

different from everything we've encountered before.

This process started years ago and is already well underway.

Even without new disruptions like self-driving cars, or robot accountants.

It looks like automation is different this time.

This time, the machines might really take our jobs.

Our economies are based on the premise that people consume.

But if fewer and fewer people have decent work, who will be doing all the consuming?

Are we producing ever more cheaply only to arrive at a point where

too few people can actually buy all our stuff and services?

Or, will the future see a tiny minority of the super rich who own the machines...

dominating the rest of us?

And does our future really have to be that grim?

While we were fairly dark in this video, it's far

from certain that things will turn out negatively.

The Information Age and modern automation, could be a huge opportunity

to change human society, and reduce poverty and inequality drastically.

It could be a seminal moment in human history.

We'll talk about this potential, and possible solutions like

a universal basic income, in part 2 of this video series.

We need to think big, and fast.

Because one thing's for sure, the machines are not coming;

They are already here.

This video took us about 900 hours to make,

and we've been working on it for over nine months.

Projects like this one would not be possible

without your support on patreon.com.

If you want to help us out and get a personal

Kurzgesagt bird in return, that would be really useful.

We based much of this video on two very good books:

and

You can find links to both of them in the video description; highly recommended!

Also, we made a little robot poster.

You can buy it and a lot of other stuff in our DFTBA shop.

This video is part of a larger series about how technology

is already changing and will change human life forever.

If you want to continue watching, we have a few playlists.