40奇点，天网，计算机的未来

计算机速成课第40集奇点，天网，计算机的未来

超喜欢此系列, 整理成文字笔记方便重复阅读.

01:21  普适计算  Ubiquitous Computing
04:55  奇点         Singularity
06:51  把工作分为4个象限，讨论自动化带来的影响
10:15  机器人的存在时间可能长过人类，可以长时间探索宇宙

Hi, I’m Carrie Anne, and welcome to Crash Course Computer Science!
(｡･∀･)ﾉﾞ嗨，我是 Carrie Anne \N 欢迎收看计算机科学速成课！

We’re here: the final episode!
我们到了最后一集！

If you’ve watched the whole series,
如果你看了整个系列，

hopefully you’ve developed a newfound appreciation
希望你对计算机影响的深度和广度 \N 有全新的认知和欣赏

for the incredible breadth of computing applications and topics.
希望你对计算机影响的深度和广度 \N 有全新的认知和欣赏

It’s hard to believe we’ve worked up from mere transistors and logic gates,
难以相信我们从简单的晶体管和逻辑门开始

all the way to computer vision, machine learning, robotics and beyond.
一直到计算机视觉，机器学习，机器人以及更多

We’ve stood on the shoulders of giants
我们站在巨人的肩膀上

like Babbage and Lovelace, Hollerith and Turing,
Charles Babbage \N Ada Lovelac \N Herman Hollerith \N Alan Turing

Eckert and Hopper, Sutherland and Engelbart,
J. Presper Eckert \N Grace Hopper \N Ivan Sutherland \N Douglas Engelbart

Bush and Berners Lee, Gates and the Woz,
Vannevar Bush (Memex) \N Berners-Lee (万维网) \N Bill Gates (微软)\N Steve Wozniak (苹果)

and many other computing pioneers.
和许多其他先驱

My biggest hope is that these episodes have inspired you to
我最大的希望是这些视频能激励你 \N 去了解这些东西如何影响你的人生

learn more about how these subjects affect your life.
我最大的希望是这些视频能激励你 \N 去了解这些东西如何影响你的人生

Maybe you’ll even pick up programming or choose a career in computing.
甚至开始学编程，或找一份计算机职业

It’s awesome!
这很棒！

It’s also a skill of the future.
这是未来的技能

I said in the very first episode that computer science isn’t magic, but it sort of is!
我在第一集说过，计算机科学不是魔法\N 但它有点像魔法

Knowing how to use and program computers is sorcery of the 21st century.
学习使用电脑和编程，是21世纪的巫术

Instead of incantations and spells, it’s scripts and code.
只不过用的不是咒语而是代码

Those who know how to wield that tremendous power will be able to craft great things,
懂得运用的人，能创造出伟大的东西

not just to improve their own lives, but also their communities and humanity at large.
不仅改善自己的生活，还有当地社区乃至整体人类

Computing is also going to be literally everywhere –
计算机会随处可见 -

not just the computers we see today, sitting on desks and countertops,
不仅是放在桌上带在包里

and carried in pockets and bags – but inside every object imaginable.
而是在所有可想象的东西里

Inside all your kitchen appliances, embedded in your walls, nanotagged in your food,
厨房用具里，墙里，食物里

woven into your clothes, and floating around inside your body.
编织进衣服里，在你的血液里

This is the vision of the field of Ubiquitous Computing.
这是"普适计算"的愿景

In some ways, it’s already here, and in other ways, we’ve got many decades to go.
从某种角度来讲它已经来临了\N 而换一个角度还要几十年

Some might view this eventuality as dystopian,
有些人把这种未来看成反乌托邦

with computers everywhere surveilling us and competing for our attention.
到处都有监视器，有无数东西想吸引我们的注意力

But the late Mark Weiser, who articulated this idea in the 1990s,
但 1990 年代提出这个想法的马克·维泽尔

saw the potential very differently:
看到了非常不同的潜力：

"For [fifty] years, most interface design, and most computer design,
"[五十]年来，大多数界面和计算机设计，

has been headed down the path of the “dramatic” machine.
都是朝"戏剧性"方向前进

Its highest idea is to make a computer so exciting, so wonderful,
想把计算机做得超好，让人一刻也不想离开

so interesting, that we never want to be without it.
想把计算机做得超好，让人一刻也不想离开

A less-traveled path I call the “invisible”;
另一条少有人走的路是"无形"的

its highest idea is to make a computer so imbedded, so fitting,
把计算机整合到所有东西里 \N 用的时候很自然完全注意不到

so natural, that we use it without even thinking about it …
把计算机整合到所有东西里 \N 用的时候很自然完全注意不到

The most profound technologies are those that disappear.
最厉害的科技是看不见的科技

They weave themselves into the fabric of everyday life
它们融入到日常生活的每一部分直到无法区分"

until they are indistinguishable from it."
它们融入到日常生活的每一部分直到无法区分"

That doesn’t describe computing of today
如今我们还没达到这样

– where people sit for hours upon end in front of computer monitors,

人们在电脑前连续坐好几小时

and social media notifications interrupt us at dinner.
吃晚餐被手机推送通知打扰

But, it could describe computing of the future, our final topic.
但它可以描述计算的未来 \N 本系列最后一个主题

When people think of computing in the future,
人们思考计算机的未来时经常会直接想到人工智能

they often jump right to Artificial Intelligence.
人们思考计算机的未来时经常会直接想到人工智能

No doubt there will be tremendous strides made in AI in the coming years,
毫无疑问，接下来几十年人工智能会有巨大进步

but not everything will be, or need to be, AI-powered.
但不是所有东西都要做成 AI ，或需要 AI

Your car might have an AI to self-drive, but the door locks
车有自动驾驶AI，但门锁依然会很简单

might continue to be powered by what are essentially if-statements.
车有自动驾驶AI，但门锁依然会很简单

AI technology is just as likely to enhance existing devices,
人工智能可能只是增强现有设备

like cars, as it is to open up entirely new product categories.
比如汽车，AI 带来了一个全新的产品种类

The exact same thing happened with the advent of electrical power – lightbulbs replaced candles.
刚出现电力时也是这样，灯泡取代了蜡烛.

But electrification also led to the creation of hundreds of new electrically-powered gadgets.
但电气化也导致上百种新的电动小工具诞生

And of course, we still have candles today.
当然我们如今仍然有蜡烛

It’s most likely that AI will be yet another tool
最可能的情况是 AI 变成 \N 计算机科学家手中的另一门新工具

that computer scientists can draw upon to tackle problems.
最可能的情况是 AI 变成 \N 计算机科学家手中的另一门新工具

What really gets people thinking, and sometimes sweating,
但真正让人深思和担忧的是

is whether Artificial Intelligence will surpass human intelligence.
人工智能是否会超越人类智能？

This is a really tricky question for a multitude of reasons,
这个问题很难有多方面原因

including most immediately: “what is intelligence?”
比如 “智能的准确定义是什么？”

On one hand, we have computers that can drive cars,
一方面，有会开车的计算机

recognize songs with only a few seconds of audio,
几秒就能识别歌的 App

translate dozens of languages, and totally dominate at games like chess, Jeopardy, and Go.
翻译几十种语言，\N 还称霸了一些游戏，比如象棋，知识竞答和围棋

That sounds pretty smart!
听起来很聪明！

But on the other hand, computers fail at some basic tasks,
但另一方面，计算机连一些简单事情都做不了

like walking up steps, folding laundry,
比如走楼梯，叠衣服，

understanding speech at a cocktail party, and feeding themselves.
在鸡尾酒派对和人聊天，喂饱自己

We’re a long way from Artificial Intelligence that’s as general purpose and capable as a human.
人工智能成长到和人类一样通用，还有很长的路

With intelligence being somewhat hard to quantify,
因为"智能"是难以量化的指标

people prefer to characterize computers and creatures
人们更喜欢用处理能力来区分

by their processing power instead,
人们更喜欢用处理能力来区分

but that’s a pretty computing-centric view of intelligence.
但这种衡量智能的方法比较"以计算为中心"

Nonetheless, if we do this exercise,
但如果把视频中出现过的电脑和处理器画张图

plotting computers and processors we’ve talked about in this series,
但如果把视频中出现过的电脑和处理器画张图

we find that computing today has very roughly equivalence in calculating
可以看到如今的计算能力粗略等同于一只老鼠

power to that of a mouse…
可以看到如今的计算能力粗略等同于一只老鼠

which, to be fair, also can’t fold laundry, although that would be super cute!
公平点说，老鼠也不会叠衣服\N 但如果真的会叠就太可爱了

Human calculating power is up here, another 10 to the 5,
人类的计算能力在这儿，多10的5次方

or 100,000 times more powerful than computers today.
也就是比如今电脑强10万倍

That sounds like a big gap, but with the rate of change in computing technologies,
听起来差距很大，但按如今的发展速度，

we might meet that point in as early as a decade,
也许十几年就可以赶上了

even though processor speeds are no longer following Moore’s Law,
虽然现在处理器的速度不再按摩尔定律增长了

like we discussed in Episode 17.
我们在第17集讨论过

If this trend continues, computers would have more processing power/intelligence,
假设趋势继续保持下去，在本世纪结束前

than the sum total of all human brains combined before the end of this century.
计算机的处理能力/智能会比全人类加起来还多

And this could snowball as such systems need less human input,
然后人的参与会越来越少，人工超级智能会开始改造自己

with an artificial superintelligence designing and training new versions of itself.
然后人的参与会越来越少，人工超级智能会开始改造自己

This runaway technological growth, especially with respect to an intelligence explosion,
智能科技的失控性发展叫 “奇点”

is called the singularity.
智能科技的失控性发展叫 “奇点”

The term was first used by our old friend from Episode 10,
第10集约翰·冯·诺伊曼最早用这个词

John von Neumann, who said:
他说：

"The accelerating progress of technology and changes in the mode of human life,
"越来越快的技术发展速度和人类生活方式的改变，

give the appearance of approaching some essential singularity
看起来会接近人类历史中某些重要的奇点

in the history of the race beyond which human affairs,
看起来会接近人类历史中某些重要的奇点

as we know them, could not continue."
这个势头不会永远继续下去"

And Von Neumann suggested this back in the 1950s,
冯诺依曼在 1950 年代说的这话.

when computers were trillions of times slower than they are today.
那时计算机比现在慢得多

Sixty years later, though, the singularity is
六十年后的今天，奇点仍然在遥远的地平线上

still just a possibility on the horizon.
六十年后的今天，奇点仍然在遥远的地平线上

Some experts believe this progress is going to level off,
一些专家认为发展趋势会更平缓一些

and be more of an S curve than an exponential one,
更像是S型，而不是指数型

where as complexity increases, it becomes more difficult to make additional progress.
而随着复杂度增加，进步会越来越难

Microsoft co-founder Paul Allen calls it a “complexity brake”.
微软联合创始人保罗·艾伦叫这个"复杂度刹车"

But, as a thought experiment,
但当作思维练习

let’s just say that superintelligent computers will emerge.
我们假设超智能计算机会出现。

What that would mean for humanity is a hotly debated topic.
这对人类意味着什么，是个讨论激烈的话题

There are people who eagerly await it,
有些人迫不及待

and those who are already working to stop it from happening.
有些人则努力阻止它

Probably the most immediate effect would be technological unemployment,
最直接的影响可能是"技术性失业"

where workers in many job sectors are rendered obsolete
很多工作被计算机，比如AI和机器人，给代替掉了

by computers – like AIs and Robots –
很多工作被计算机，比如AI和机器人，给代替掉了

that can do their work better and for less pay.
它们的效率更高，成本更低

Although computers are new, this effect is not.
虽然计算机出现没多久，但"技术性失业"不是新事

Remember Jacquard’s Loom from Episode 10?
还记得第10集里雅卡尔的织布机吗？

That automated the task of skilled textile workers back in the 1800s, which led to riots.
它让1800年代的纺织工人失业，导致了骚乱

Also, back then, most of the population of the US and Europe were farmers.
当时美国和欧洲大部分人都是农民

That’s dropped to under 5% today,
如今农民占人口比例<5％

due to advances like synthetic fertilizers and tractors.
因为有合成肥料和拖拉机等等技术

More modern examples include telephone switchboard operators
时间更近一些的例子是"电话接线员"

being replaced with automatic switchboards in 1960,
在1960年被自动接线板代替了

and robotic arms replacing human painters in car factories in the 1980s.
还有1980年代的"机器喷漆臂"替代了人工喷漆

And the list goes on and on.
这样的例子还有很多.

On one hand, these were jobs lost to automation.
一方面，因为自动化失去了工作

And on the other hand, clothes, food, bicycles, toys,
另一方面，我们有大量产品，\N 衣服，食物，自行车，玩具等

and a myriad of other products are all plentiful today
另一方面，我们有大量产品，\N 衣服，食物，自行车，玩具等

because they can be cheaply produced thanks to computing.
因为可以廉价生产

But, experts argue that AI, robots and computing technologies in general,
但专家认为人工智能，机器人以及更广义的计算

are going to be even more disruptive than these historical examples.
比之前更有破坏性

Jobs, at a very high level, can be summarized along two dimensions.
工作可以用两个维度概括

First, jobs can be either more manual – like assembling toys
首先，手工型工作，比如组装玩具

– or more cognitive – like picking stocks.

或思维型工作 - 比如选股票

These jobs can also be routine – the same tasks over and over again
还有重复性工作，一遍遍做相同的事

or non-routine, where tasks vary and workers need to problem solve and be creative.
或非重复性，需要创造性的解决问题

We already know that routine-manual jobs can be automated by machines.
我们知道重复性手工工作，可以让机器自动化

It has already happened for some jobs and is happening right now for others.
现在有些已经替代了，剩下的在逐渐替代

What’s getting people worried is that non-routine manual jobs,
让人担心的是"非重复性手工型工作"

like cooks, waiters and security guards, may get automated too.
比如厨师，服务员，保安。

And the same goes for routine cognitive work,
思维型工作也一样

like customer service agents, cashiers, bank tellers, and office assistants.
比如客服，收银员，银行柜员和办公室助理

That leaves us with just one quadrant that might be safe,
剩下一个暂时比较安全的象限

at least for a little while:
剩下一个暂时比较安全的象限

non-routine cognitive work,
非重复性思维型工作

which includes professions like teachers and artists,
包括教师和艺术家，

novelists and lawyers, and doctors and scientists.
小说家和律师，医生和科学家

These types of jobs encompass roughly 40% of the US workforce.
这类工作占美国劳动力大概40％

That leaves 60% of jobs vulnerable to automation.
意味着剩下60％工作容易受自动化影响

People argue that technological unemployment at this scale
有人认为这种规模的技术失业

would be unprecedented and catastrophic,
是前所未有的，会导致灾难性的后果，

with most people losing their jobs.
大部分人会失业

Others argue that this will be great,
其他人则认为很好，

freeing people from less interesting jobs to pursue better ones,
让人们从无聊工作解脱，去做更好的工作，

all while enjoying a higher standard of living with the bounty of food and products
同时享受更高生活水平，有更多食物和物品

that will result from computers and robots doing most of the hard work.
都是计算机和机器人生产的.

No one really knows how this is going to shake out,
没人知道未来到底会怎样

but if history is any guide, it’ll probably be ok in the long run.
但如果历史有指导意义，长远看一切会归于平静

Afterall, no one is advocating that 90% of people
毕竟，现在没人嚷嚷着让90％的人回归耕田和纺织

go back to farming and weaving textiles by hand.
毕竟，现在没人嚷嚷着让90％的人回归耕田和纺织

The tough question, which politicians are now discussing,
政界在讨论的棘手问题是

is how to handle hopefully-short-term economic disruption,
怎么处理数百万人突然失业 \N 造成的短期经济混乱

for millions of people that might be suddenly out of a job.
怎么处理数百万人突然失业 \N 造成的短期经济混乱

Beyond the workplace, computers are also very likely to change our bodies.
除了工作，计算机很可能会改变我们的身体

For example, futurist Ray Kurzweil believes that
举个例子, 未来学家 Ray Kurzweil 认为

"The Singularity will allow us to transcend
"奇点会让我们超越肉体和大脑的局限性

[the] limitations of our biological bodies and brains.
"奇点会让我们超越肉体和大脑的局限性

We will gain power over our fates.
我们能掌控自己的命运

… We will be able to live as long as we want.
可以想活多久活多久我们能完全理解并扩展大脑思维

We will fully understand human thinking and will vastly extend and expand its reach."
可以想活多久活多久我们能完全理解并扩展大脑思维

Transhumanists see this happening in the form of cyborgs,
超人类主义者认为会出现"改造人"

where humans and technology merge, enhancing our intellect and physiology.
人类和科技融合在一起，增强智力和身体

There are already brain computer interfaces in use today.
如今已经有脑电接口了

And wearable computers, like Google Glass and Microsoft Hololens,
而 Google Glass 和微软 Hololens \N 这样的穿戴式计算机也在模糊这条界线

are starting to blur the line too.
而 Google Glass 和微软 Hololens \N 这样的穿戴式计算机也在模糊这条界线

There are also people who foresee “Digital Ascension”,
也有人预见到"数字永生"

which, in the words of Jaron Lanier,
Jaron Lanier 的说法是

“would involve people dying in the flesh and being uploaded into a computer and remaining conscious”.
“人类的肉体死去，意识上传到计算机”

This transition from biological to digital beings
从生物体变成数字体可能是下一次进化跨越

might end up being our next evolutionary step…
从生物体变成数字体可能是下一次进化跨越

and a new level of abstraction.
一层新的抽象

Others predict humans staying largely human,
其他人则预测人类大体会保持原样

but with superintelligent computers as a benevolent force,
但超智能电脑会照顾我们，帮我们管农场

emerging as a caretaker for humanity – running all the farms,
但超智能电脑会照顾我们，帮我们管农场

curing diseases, directing robots to pick-up trash,
治病，指挥机器人收垃圾，

building new homes and many other functions.
建房子以及很多其他事情

This would allow us to simply enjoy our time on this lovely pale blue dot.
让我们在这个可爱蓝点上(地球) 好好享受

Still others view AI with more suspicion –
另一些人对 AI 持怀疑态度 -

why would a superintelligent AI waste its time taking care of us?
为什么超级人工智能会费时间照顾我们？

It’s not like we’ve taken on the role of being the benevolent caretaker of ants.
人类不也没照顾蚂蚁吗？

So maybe this play out like so many Sci-Fi movies
也许会像许多科幻电影一样，和计算机开战

where we’re at war with computers, our own creation having turned on us.
也许会像许多科幻电影一样，和计算机开战

It’s impossible to know what the future holds,
我们无法知道未来到底会怎样

but it’s great that this discussion and debate is already happening,
但现在已经有相关讨论了，这非常好

so as these technologies emerge, we can plan and react intelligently.
所以等这些技术出现后，我们可以更好地计划

What’s much more likely, regardless of whether you see computers as future friend or foe,
不论你把计算机视为未来的朋友或敌人

is that they will outlive humanity.
更有可能的是，它们的存在时间会超过人类

Many futurists and science fiction writers have speculated
许多未来学家和科幻作家猜测

that computers will head out into space and colonize the galaxy,
机器人会去太空殖民

ambivalent to time scales, radiation,
无视时间，辐射 \N 以及一些其他让人类难以长时间太空旅行的因素.

and all that other stuff that makes
无视时间，辐射 \N 以及一些其他让人类难以长时间太空旅行的因素.

long-distance space travel difficult for us humans.
无视时间，辐射 \N 以及一些其他让人类难以长时间太空旅行的因素.

And when the sun is burned up and the Earth is space dust,
亿万年后太阳燃尽地球成为星尘 \N 也许我们的机器人孩子会继续努力探索宇宙每一个角落

maybe our technological children will be hard at work
亿万年后太阳燃尽地球成为星尘 \N 也许我们的机器人孩子会继续努力探索宇宙每一个角落

exploring every nook and cranny of the universe,
亿万年后太阳燃尽地球成为星尘 \N 也许我们的机器人孩子会继续努力探索宇宙每一个角落

hopefully in honor of their parents’ tradition to build knowledge,
以纪念它们的父母，同时让宇宙变得更好，

improve the state of the universe,
以纪念它们的父母，同时让宇宙变得更好，

and to boldly go where no one has gone before!
大胆探索无人深空

In the meantime, computers have a long way to go,
与此同时，计算机还有很长的路要走

and computer scientists are hard at work advancing
计算机科学家们在努力推进过去40集谈到的话题

all of the topics we talked about over the past forty episodes.
计算机科学家们在努力推进过去40集谈到的话题

In the next decade or so,
在接下来的十几年

we’ll likely see technologies like virtual and augmented reality,
VR 和 AR，无人驾驶车，无人机，可穿戴计算机，

self-driving vehicles, drones, wearable computers,
VR 和 AR，无人驾驶车，无人机，可穿戴计算机，

and service robots go mainstream.
和服务型机器人会变得主流

The internet will continue to evolve new services,
互联网会继续诞生新服务

stream new media, and connect people in different ways.
在线看新媒体. 用新方式连接人们

New programming languages and paradigms will be developed
会出现新的编程语言和范例，帮助创造令人惊叹的新软件

to facilitate the creation of new and amazing software.
会出现新的编程语言和范例，帮助创造令人惊叹的新软件

And new hardware will make complex operations blazingly fast,
而新硬件能让复杂运算快如闪电 \N 比如神经网络和3D图形

like neural networks and 3D graphics.
而新硬件能让复杂运算快如闪电 \N 比如神经网络和3D图形

Personal computers are also ripe for innovation,
个人电脑也会创新

perhaps shedding their forty-year old desktop metaphor
不像过去40年着重宣传 “桌面” 电脑

and being reborn as omnipresent and lifelong virtual assistants.
而是变成无处不在的虚拟助手

And there’s so much we didn’t get to talk about in this series,
这个系列我们还有很多话题没谈

like cryptocurrencies, wireless communication,
比如加密货币，无线通讯，3D打印，生物信息学和量子计算

3D printing, bioinformatics, and quantum computing.
比如加密货币，无线通讯，3D打印，生物信息学和量子计算

We’re in a golden age of computing
我们正处于计算机的黄金时代

and there’s so much going on, it’s impossible to summarize.
有很多事情在发生，全部总结是不可能的

But most importantly, you can be a part of this amazing transformation and challenge,
但最重要的是你可以学习计算机 \N 成为这个惊人转型的一部分

by learning about computing, and taking what’s arguably humanity’s greatest invention,
但最重要的是你可以学习计算机 \N 成为这个惊人转型的一部分

to make the world a better place.
把世界变得更好

Thanks for watching.
感谢收看

参考链接 :

视频YouTube 直达

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读者 CSDN 直达

读者 GitHub直达

译者 GitHub 直达

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