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机器的崛起 – 为何此次不同 – 译学馆
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机器的崛起 - 为何此次不同

The Rise of the Machines – Why Automation is Different this Time

你认为还要多久
How long do you think it will take
机器将会比你工作得更好?
before machines do your job better than you do?
以前 自动化意味着 在工厂里做着重复工作的大型笨重机器
Automation used to mean big stupid machines doing repetitive work in factories.
如今它们可以使飞机安全着陆 诊断癌症和交易股票
Today they can land aircraft, diagnose cancer and trade stocks.
我们正迈入一个与以前截然不同的自动化时代
We are entering a new age of automation unlike anything that’s come before.
2013年的一项研究表明 在美国几乎一半的
According to a 2013 study, almost half of all jobs in the
工作可能在今后的二十年实现自动化
US could potentially be automated in the next two decades.
但是等一下 自动化不是几十年前就实现了吗?
But wait; Hasn’t automation been around for decades?
这一次又有什么区别呢?
What’s different this time?
以前一切都很简单
Things used to be simple.
创新使得人类工作更加轻松 效率更高
Innovation made human work easier and productivity rose.
这就意味着在人力数量不变的情况下 每小时
Which means that more staff or services could be produced
可以产出更多的服务和产品
per hour using the same amount of human workers.
它淘汰了很多工作 同时也创造了很多更好的工作
This eliminated many jobs, but also created other jobs that were better
这一点很重要 因为增长的人口需要工作岗位
which was important because the growing population needed work.
所以 概括来讲 创新 高生产力
So, in a nutshell, innovation, higher productivity,
更少的旧工作 和更多更好的新工作
fewer old jobs, and many new and often better jobs.
总的来说 大部分人都是受益者 人们的生活水平也提高了
Overall, this worked well for a majority of people and living standards improved.
人类谋生的手段有一个清晰的进程
There’s a clear progression in terms of what humans did for
长期以来 我们一直以农耕谋生
a living. For the longest time, we worked in agriculture.
工业革命之后 我们转向制造业
With the Industrial Revolution, this shift into production jobs and as
由于自动化变得越来越普遍 人们又开始从事服务业
automation became more widespread, humans shifted into service jobs.
不久之前 人们步入了信息时代
And then only a few moments ago in human history, the Information Age happened.
突然 行业的规则改变了 我们的工作正被
Suddenly, the rules were different. Our jobs are now being
机器取代 而且速度比以前快得多
taken over by machines much faster than they were in the past.
这相当令人担忧 但创新一定会拯救我们的 对吗?
That’s worrying of course… but innovation will clearly save us, right?
在信息时代 各行各业欣欣向荣
While new information age industries are booming,
新的工作岗位却越来越少
they are creating fewer and fewer new jobs.
在1979年 通用汽车公司有八十多万名员工
In 1979, General Motors employed more than 800,000
利润达110亿美金
workers and made about $11 billion US dollars.
而在2012年 谷歌凭着五万八千名员工 创造了140亿的利润
In 2012, Google made about $14 billion US dollars while employing 58,000 people.
你可能不喜欢这个对比 但谷歌却是
You may not like this comparison, but Google is
在过去一些年里创造了新工作岗位的一个模范:
an example of what created new jobs in the past:
创新型新产业
Innovative new industries.
就像汽车产业一样 许多传统的创新行业已经失去了活力
Old innovative industries are running out of steam. Just look at cars.
但在100年前问世时 汽车也带动了非常庞大的产业群
When they became a thing 100 years ago, they created huge industries.
汽车改变了我们的生活方式 基础设施以及我们的城市
Cars transformed our way of life, our infrastructure, and our cities.
几百万人因此直接或间接地找到了工作
Millions of people found jobs either directly or indirectly.
数十年的投资让汽车行业保持活力
Decades of investment kept this momentum going.
然而现在 汽车行业的发展达到了饱和 这个行业的创新
Today, this process is largely complete. Innovation in the
无法像以前一样创造那么多的工作机遇了
car industry does not create as many jobs as it used to.
纵使电动汽车异军突起 它也无法创造几百万份新工作岗位
While electric cars are great and all, they won’t create millions of new jobs.
但是等等 互联网产业又如何呢?
But wait; what about the internet?
一些专家认为互联网不愧是
Some technologists argue that the Internet is an
电气化时代的创新产物
innovation on a par of the introduction of electricity.
如果我们继续对比 会发现
If we go with this comparison, we see how our
现代创新与传统创新的差别
modern innovation differs from the old one.
互联网孕育了很多新兴产业
The Internet created new industries,
但是它们创造的工作机会
but they’re not creating enough jobs to keep up
不足以赶上人口增长的步伐 或弥补被互联网淘汰的产业
with population growth or to compensate for the industries the Internet is killing.
在2004年的高峰期
At its peak in 2004,
Blockbuster拥有84000名员工 60亿美元的收入
Blockbuster had 84,000 employees and made $6 billion US dollars in revenue.
在2016年 Netflix有4500名员工 收入达到90亿美元
In 2016, Netflix had 4,500 employees and made $9 billion dollars in revenue.
再比如说 我们
Or take us, for example.
只需要12个人的专职团队 “简而言之”就能拥有百万观众
With a full-time team of just 12 people, Kurzgesagt reaches millions of people.
电视台若想达到相同的收视率 则需要更多的员工
A TV station with the same amount of viewers needs way more employees.
信息时代的创新并不等同于
Innovation in the Information Age doesn’t equate to
创造足够多的新工作岗位 这本身就是不妙的
the creation of enough new jobs, which would be bad
但现在 新一波的自动化浪潮
enough on its own but now, a new wave of automation and
和新一代的机器正逐渐取代我们
a new generation of machines is slowly taking over.
要理解这点 我们要先了解我们自己
To understand this, we need to understand ourselves first.
人类的进步基于劳动分工
Human progress is based on the division of labor.
几千年来人类不断地进步 人类的分工也越来越细化
As we advanced over thousands of years, our jobs became more and more specialized.
虽然我们最智能的机器还不擅长复杂的工作
While even our smartest machines are bad at doing complicated jobs,
它们却非常擅长严密清晰 有规律可循的工作
they are extremely good at doing narrowly defined and predictable tasks.
这就是工厂工作被淘汰的原因
This is what destroyed factory jobs.
但再看看耗时长 困难 复杂的工作
But look at a complex job long and hard enough,
你会发现它仅仅是一个接一个的
and you’ll find that it’s really just many narrowly
严密清晰 有规律可循的小工作而已
defined and predictable tasks one after another.
机器已经极其擅长
Machines are on the brink of becoming so good at
把复杂的工作分解为很多有规律可循的小工作
breaking down complex jobs into many predictable ones,
因此对很多人来说 并没有更多细分的余地
that for a lot of people, there will be no further room to specialize.
我们处在出局的边缘
We are on the verge of being outcompeted.
数字智能通过机器自主学习来完成这一工作
Digital machines do this via machine learning,
他们通过分析数据来获取信息和提升技能
which enables them to acquire information and skills by analyzing data.
它们通过发现的一些规律 在某些方面做的更好
This makes them become better at something through the relationships they discover.
机器能够自学
Machines teach themselves.
要使这成为可能 我们仅需向计算机
We make this possible by giving a computer a lot of
输入大量的我们希望它们改善的相关数据
data about the thing we wanted to become better at.
机器在了解你网购的商品后
Show a machine all the things you bought online,
慢慢地学会给你推荐商品 结果你买的越来越多
and it will slowly learn what to recommend to you, so you buy more things.
机器学习潜能越来越有发挥空间 因为近年来
Machine learning is now meeting more of its potential because in recent years,
人们开始记录生活的方方面面
humans have started to gather data about everything.
日常行为 天气 医药记录 社交系统
Behavior, weather patterns, medical records, communication systems,
旅行信息 当然少不了工作日程
travel data, and of course, data about what we do at work.
我们不经意地创造了巨大的图书馆数据库
What we’ve created by accident is a huge library machines can
机器从中了解到人类是怎样办事的 并学会把事情做地更好
use to learn how humans do things and learn to do them better.
这些数字机器或许是最大的工作杀手
These digital machines might be the biggest job killer of all.
它们可以顷刻间被免费复制
They can be replicated instantly and for free.
你不需要投资大部件来给它们升级
When they improve, you don’t need to invest in
只需要使用新的代码
big metal things; you can just use the new code.
它们升级的速度很快 有多快呢?
And they have the ability to get better fast. How fast?
如果你的工作涉及计算机的复杂运行 你将会很快地被取代
If your work involves complex work on a computer today, you might be out
速度不亚于工厂里的工人
of work even sooner than the people who still have jobs in factories.
一些真实案例表明 这一转变可能正在发生
There are actual real-world examples of how this transition might be happening.
旧金山的一家公司向一些大企业提供了一种项目管理软件
A San Francisco company offers a project management software for big
目的是淘汰一些中级管理岗位
corporations, which is supposed to eliminate middle management positions.
每当有新项目的时候 软件会先决定
When it’s hired for a new project, the software first decides which jobs
哪些工作可以自动化 再准确定位哪里需要专业人员
can be automated and precisely where it needs actual professional humans.
接着组织一支互联网自由工作者队伍
It then helps assemble a team of freelancers over the Internet.
然后给人员分配任务 控制工作质量
The software then distributes tasks to the humans, and controls the quality
并追踪个人表现直至完成任务
of the work, tracking individual performance until the project is complete.
这听起来没那么糟糕
Okay. This doesn’t sound too bad.
机器只是在扼杀一种工作 而给自由工作者创造工作岗位 是这样吗?
While this machine is killing one job, it creates jobs for freelancers, right?
自由工作者在工作的时候
Well, as the freelancers complete their tasks,
自动学习的算法会追踪他们的任务轨迹 搜集相关信息
learning algorithms track them, and gather data
以此细化分析他们是如何完成任务的
about their work, and which tasks it consists of.
实际上
So what’s actually happening, is that
自由工作者在教机器如何取代自己
the freelancers are teaching a machine how to replace them.
总体看来 在使用的第一年 该软件降低了一半的成本
On average, this software reduces costs by about 50%
第二年又降低了1/4
in the first year, and by another 25% in the second year.
这只是众多例子中的一个
This is only one example of many.
在各类领域 机器或程序
There are machines and programs getting as good
表现得不比人类差
or better than humans in all kinds of fields.
从药剂师到分析师 记者到放射学专家
From pharmacists to analysts, journalists to radiologists,
收银员 银行出纳员 或是做汉堡的非技术工人
cashiers to bank tellers, or the unskilled worker flipping burgers.
这些工作不会一夜消失
All of these jobs won’t disappear overnight,
但需要的人力却越来越少
but fewer and fewer humans will be doing them.
我们会在续集中讨论一些案例
We’ll discuss a few cases in a follow-up video.
工作岗位的消失很糟糕 这只是问题的一半
But while jobs disappearing is bad, it’s only half of the story.
工作的新旧更替是远远不够的
It’s not enough to substitute old jobs with new ones.
我们需要不断地创造更多的工作岗位
We need to be generating new jobs constantly
因为世界人口在不断地增长
because the world population is growing.
过去我们通过创新解决了这一问题
In the past we have solved this through innovation.
然而自1973年来 美国的新增工作岗位开始减少
But, since 1973, the generation of new jobs in the US has begun to shrink.
21世纪的头十年见证了
And the first decade of the 21st century, was the first one, where
美国工作岗位的总数量 首次实现零增长
the total amount of jobs in the US, did not grow for the first time.
对美国而言 每个月必须创造15万个工作岗位
In a country that needs to create up to 150,000 new jobs per
才能与人口增长保持一致 “零增长”确实不是好消息
month, just to keep up with population growth, this is bad news.
人类的生活水平也开始受到影响
This is also starting to affect standards of living.
以前 不断增长的生产率
In the past, it was seen as obvious that with rising
显然可以创造更多更好的工作岗位
productivity, more and better jobs would be created.
然而数据却表示抗议
But the numbers tell a different story.
在1998年 美国工人总工时达到一千九百四十亿
In 1998, US workers worked a total of 194 billion hours.
在接下来的15年 产量增加了42个百分点
Over the course of the next 15 years, their output increased by 42 percent.
但在2013年 美国工人的总工时却保持原样
But in 2013, the amount of hours worked by US workers was still 194 billion hours.
这表明 尽管大幅度提高了效率
What this means, is that despite productivity growing
涌现了几千家新公司
drastically, thousands of new businesses opening up, and the
美国人口增长了四千多万
US population growing by over 40 million, there was no
15年的总工时却不见一丁点增长
growth at all in the number of hours worked in 15 years.
同时 美国大学应届生的工资
At the same time, wages for new university graduates
在过去的十年里也只减不增
in the US, have been declining for the past decade,
多达四成的毕业生
while up to 40 percent of new graduates, are forced
被迫从事与文凭不符的工作
to take on jobs that don’t require a degree.
生产率正脱离人力
Productivity is separating from human labor.
在信息时代 创新的本质
The nature of innovation in the Information Age is
已不同以往
different from everything we’ve encountered before.
几年前已经开始的转变正进行地如火如荼
This process started years ago and is already well underway.
即使没被无人驾驶汽车 会计机器人这类新事物打断
Even without new disruptions like self-driving cars, or robot accountants.
当今的自动化已不同以往
It looks like automation is different this time.
这次 机器也许真的要取代人类的工作了
This time, the machines might really take our jobs.
人类经济发展的前提是消费
Our economies are based on the premise that people consume.
但如果越来越多的人没有像样的工作 那谁来消费?
But if fewer and fewer people have decent work, who will be doing all the consuming?
我们的产品越来越廉价
Are we producing ever more cheaply only to arrive at a point where
可是到头来还是只有极少数的人能消费的起这些产品和服务?
too few people can actually buy all our stuff and services?
或者未来将出现极少数拥有机器的超级富豪
Or, will the future see a tiny minority of the super rich who own the machines…
统治着其他人
dominating the rest of us?
人类的未来真的会那么灰暗吗?
And does our future really have to be that grim?
虽然视频中的未来很灰暗
While we were fairly dark in this video, it’s far
却也并非铁板钉钉
from certain that things will turn out negatively.
信息时代和当今的自动化也许是
The Information Age and modern automation, could be a huge opportunity
改变人类社会的良好机遇 能极大地减少贫穷和不平等的现象
to change human society, and reduce poverty and inequality drastically.
也可能是人类历史的重要时刻
It could be a seminal moment in human history.
在本系列视频的第二部分
We’ll talk about this potential, and possible solutions like
我们将讨论可行的方案 比如:全球基本收入
a universal basic income, in part 2 of this video series.
我们要抓紧时间 大胆地思考
We need to think big, and fast.
因为能确定的是 机器不是即将出现
Because one thing’s for sure, the machines are not coming;
而是已经出现了
They are already here.
该视频制作耗时900小时
This video took us about 900 hours to make,
历经九个多月的时间
and we’ve been working on it for over nine months.
没有你们在“众筹网”的支持
Projects like this one would not be possible
我们将无法完成类似视频
without your support on patreon.com.
您的帮助是对我们最大的支持
If you want to help us out and get a personal
作为回报 您将得到一只定制的“简而言之”小鸟
Kurzgesagt bird in return, that would be really useful.
该视频的大部分观点摘自两本优秀的书籍:
We based much of this video on two very good books:
《机器人的崛起》和《第二次机器时代》
The rise of the robots and the second machine age
在视频描述的“强烈推荐”一栏 您能找到两本书的链接
You can find links to both of them in the video description; highly recommended!
我们还制作了小张机器人海报
Also, we made a little robot poster.
您可以在我们的“永不平庸”商城中购买海报和其它产品
You can buy it and a lot of other stuff in our DFTBA shop.
该视频一个大系列视频之一
This video is part of a larger series about how technology
该系列讲述的是机器正将永远改变人类生活
is already changing and will change human life forever.
如果你想继续观看 欢迎点击播放列表
If you want to continue watching, we have a few playlists.

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视频概述

机器人正在取代人类的工作,人类的未来是否会变的相当灰暗,又或者这是人类社会发展的契机呢?

听录译者

收集自网络

翻译译者

Goahead

审核员

祐子祐

视频来源

https://www.youtube.com/watch?v=WSKi8HfcxEk

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