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  • How long do you think it will take


  • before machines do your job better than you do?


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


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


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


  • 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?


  • What's different this time?


  • Things used to be simple.


  • 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


  • 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


  • 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.


  • 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.


  • 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.


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


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


  • 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.


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


  • But wait; what about the internet?


  • 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.


  • 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.


  • 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


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


  • At its peak in 2004,


  • 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.


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


  • 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


  • the creation of enough new jobs, which would be bad


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


  • 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.


  • Human progress is based on the division of labor.


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


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


  • 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


  • defined and predictable tasks one after another.


  • 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,


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


  • We are on the verge of being outcompeted.


  • Digital machines do this via machine learning,


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


  • 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.


  • We make this possible by giving a computer a lot of


  • 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,


  • 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


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


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


  • 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.


  • A San Francisco company offers a project management software for big


  • corporations, which is supposed to eliminate middle management positions.


  • 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.


  • 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.


  • Okay. This doesn't sound too bad.


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


  • 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


  • the freelancers are teaching a machine how to replace them.


  • On average, this software reduces costs by about 50%


  • 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,


  • 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.


  • 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.


  • 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.


  • In the past we have solved this through innovation.


  • 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


  • 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


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


  • 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.


  • 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.


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


  • 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.