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[第1课]未来没有工作我们如何赚钱？
可以思考、学习和适应的机器时代即将到来，这可能意味着我们人类将最终失业。我们该怎么办？未来主义者马丁·福特直截了当的谈到一个备受争议的观点：将收入从传统工作中分离开来，并建立一个通用的收入机制。
讲师：马丁·福特
授课语言：英文
类型：TED 人物 科技
课程简介：可以思考、学习和适应的机器时代即将到来，这可能意味着我们人类将最终失业。我们该怎么办？未来主义者马丁·福特直截了当的谈到一个备受争议的观点：将收入从传统工作中分离开来，并建立一个通用的收入机制。
扫描左侧二维码下载客户端人工智能最受欢迎的10大TED演讲
云栖君导读：当我们过于关注机器学习的技术实现时，往往就会忽略技术在未来的应用及其政治后果。本文并没有讨论最适合解决某个问题可以用什么语言或算法，而是从最受欢迎的非营利组织TED中收集了一系列演讲。
在这一系列的演讲中，让我们从“全局”的角度聆听关于人工智能（AI）和机器学习的有趣讨论和会议，你将会听到关于该领域内未来的发展、意义、优势和对全世界范围内影响的不同立场。演讲主题涵盖广泛：从AI的政治和技术责任，到AI对未来就业市场的影响，甚至它在艺术领域中的作用。
1. 当电脑比我们聪明时，将会发生什么？
观看地址：
https://www.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are?spm=.blogcont.379d55b8adOavt
演讲者：尼克·博斯特罗姆（Nick Bostrom）
本世纪内，AI发展越来越迅速，也变的越来越聪明——研究表明，计算机AI可以和人类一样“聪明”。在思维机器的驱使下，哲学家兼AI技术专家尼克·博斯特罗姆要求我们认真思考我们正在建设的世界。我们的智能机器能够帮助我们保存人性和价值观，还是有属于它们自己的价值？
2. 我们可以建造AI，而不会失去对它的控制吗？
观看地址：
https://www.ted.com/talks/sam_harris_can_we_build_ai_without_losing_control_over_it?spm=.blogcont.379d55b8adOavt
演讲者：萨姆·哈里斯（Sam Harris）
害怕超级智能AI吗？神经学家兼哲学家萨姆·哈里斯说，你应该感到恐慌，而不仅仅在一些理论上感到恐慌。我们将要建造超人类机器，但是还没办法应对和这有关的问题：创造一些对待人类就人类像对待蚂蚁一样的东西。
3. 面对机器人，我们将要失去的工作以及不会失去的工作
观看地址：
https://www.ted.com/talks/anthony_goldbloom_the_jobs_we_ll_lose_to_machines_and_the_ones_we_won_t?spm=.blogcont.379d55b8adOavt
演讲者：安东尼·戈德布卢姆（Anthony Goldbloom）
机器学习并不仅仅是为了解决像信用风险评估和邮件分类等简单的任务，如今，它可以运用到更为复杂的应用中，比如：文章评级和疾病诊断等。随着这些进步出现了一个让人感到不安的问题：机器人在不久的未来会做你的工作吗？
4.我们在建立一个反乌托邦，仅仅是为了让人们点击广告
观看地址：
https://www.ted.com/talks/zeynep_tufekci_we_re_building_a_dystopia_just_to_make_people_click_on_ads?spm=.blogcont.379d55b8adOavt
演讲者：泽奈普·图费克奇（ ZeynepTufekci）
在这个令人大开眼界的演讲中，她详细介绍了像Facebook，Google和Amazon这样的公司是如何用同样的算法让你点击广告，这些算法同样被用来组织你对政治和社会信息内容的访问。并且智能机器甚至不是真正的威胁。我们需要了解的是，这个强大的力量可能如何利用AI来控制我们？我们又能怎样应对。
5.未来没有工作我们如何赚钱？
观看地址：
https://www.ted.com/talks/martin_ford_how_we_ll_earn_money_in_a_future_without_jobs?spm=.blogcont.379d55b8adOavt
演讲者：马丁·福特（Martin Ford）
6.电脑如何学习创新？
观看地址：
https://www.ted.com/talks/blaise_aguera_y_arcas_how_computers_are_learning_to_be_creative?spm=.blogcont.379d55b8adOavt
演讲者：Blaise AgüerayArcas
Google首席科学家Blaise使用深度神经网络进行机器感知和分布式学习。他在演讲中展示了被训练识别图像的神经网络是如何反向运行并生成网络。
7.AI如何提高我们的记忆力、工作和社交生活？
观看地址：
https://www.ted.com/talks/tom_gruber_how_ai_can_enhance_our_memory_work_and_social_lives?spm=.blogcont.379d55b8adOavt
演讲者：汤姆·格鲁伯（Tom Gruber）
他分享了自己对AI未来愿景，AI帮助我们在感知、创造力和认知能力方面实现超人表现：从增强设计技能，到帮助我们记住读过的所有内容，以及我们所见过的每个人的名字。
8.AI如何带来第二次工业革命？
观看地址：
https://www.ted.com/talks/kevin_kelly_how_ai_can_bring_on_a_second_industrial_revolution?spm=.blogcont.379d55b8adOavt
演讲者：凯文·凯利（Kevin Kelly）
凯文·凯利提到，未来20年里，机器人将会越来越聪明，这将会对我们做的几乎每一件事产生深远的影响，我们需要了解的AI三个发展趋势，以接受及引导其发展。
9.不要害怕超级智能AI
观看地址：
https://www.ted.com/talks/grady_booch_don_t_fear_superintelligence?spm=.blogcont.379d55b8adOavt
演讲者：Grady Booch
科学家兼哲学家Grady Booch称，新科技催生了新的焦虑，但是我们没有必要害怕一个全能但没感情的AI。通过解释如何教而不是编程，分享人类的价值观，来消除我们对超级计算机最坏的恐惧感。他并不担心那些不太可能存在的威胁，他呼吁我们思考AI如何提高人类的生活质量。
10.创建一个更安全的AI的准则。
观看地址：
https://www.ted.com/talks/stuart_russell_how_ai_might_make_us_better_people?spm=.blogcont.379d55b8adOavt
演讲者：斯图尔·特罗素（Stuart Russell）
我们怎样利用超级智能AI的力量，同时也要防止机器人接管人类所造成的灾难？随着我们与创造无所不知的机器越来越近，AI先驱斯图尔·特罗素正在研究一些与众不同的机器人：具有不确定性的机器人。听听他对未来的愿景：与人类兼容的AI可以使用常识、利他主义和其它人类价值观解决问题。
以上为译文。
本文由阿里云云栖社区组织翻译。
文章原标题《top-10-ted-talks-data-scientists-machine-learning》，译者：Mags，审校：袁虎。
ID：yunqiinsight
责任编辑：
声明：该文观点仅代表作者本人，搜狐号系信息发布平台，搜狐仅提供信息存储空间服务。
今日搜狐热点视频-TED：未来没有工作我们如何赚钱？
视频介绍：
可以思考、学习和适应的机器时代即将到来，这可能意味着我们人类将最终失业。我们该怎么办？未来如何生活？如何挣钱？......如果未来没有工作，我们将如何挣钱？
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如果未来没有工作，我们将如何挣钱？
How we'll earn money in a future without jobs
能够思考、学习并自适应的机器即将登场——这可能意味着我们人类将面临大规模失业。我们将何去何从？针对这一极具争议的观点，未来学家马丁·福德直率建言，提出将收入与传统工作分离，并制定全民基本收入标准。
Machines that can think, learn and adapt are coming -- and that could mean that we humans will end up with significant unemployment. What should we do about it? In a straightforward talk about a controversial idea, futurist Martin Ford makes the case for separating income from traditional work and instituting a universal basic income.
Martin Ford was one of the first analysts to write compellingly about the future of work and economies in the face of the growing automation of everything. He sketches a future that's radically reshaped not just by robots but by the loss of the income-distributing power of human jobs. How will our economic systems need to adapt?He's the author of two books:
(winner of the 2015 Financial Times/McKinsey Business Book of the Year Award ) and , and he's the founder of a Silicon Valley-based software development firm. He has written about future technology and its implications for the New York Times, Fortune, Forbes, The Atlantic, The Washington Post, Harvard Business Review and The Financial Times.&
可以点击正文的句子跳转到播放该句
首先，我想提出一个可怕的问题：
我们是否正在迈向一个没有工作的未来？
如今目睹了自动驾驶汽车
等技术的显著进步，
我们对此问题的关注日益激增，
但这一问题在过去
人们已多次提及，
也许我们真正应当关心的
是这次情况是否会有所不同。
人类一直担忧，自动化会取代工人
进而导致大量失业，
从二百多年前的英国卢德运动开始，
这种担忧便一再出现。
我猜在座各位
多数没听说过“三次革命报告”，
这是一份杰出的报告，
出自一群优秀人才之手，
其中包括两名诺奖得主。
该报告最终呈送美国总统审阅。
报告指出，美国正处于经济和社会动荡的边缘，
因为工业自动化将导致数百万工人
下岗失业。
时任总统林登·约翰逊收到报告时
是1964年3月。
如今五十多年过去了，
美国并没有发生动荡。
这样的故事从此不断上演：
警钟一再响起，
但最终总是虚惊一场。
随着虚惊不断发生，
人们对其产生了思维惯性：
动荡本质上无可避免，
新技术势必席卷整个工业界
并取代所有的工作岗位。
但与此同时，
科技进步也将带来全新的变化。
在未来，新型工业将会兴起，
势必产生新的用人需求。
未来将会出现全新的工作，
有的甚至今天我们根本无法想像。
这便是迄今为止的故事，
一直是令人乐观的。
事实证明，新出现的工作
通常远胜过旧的工作。
新工作更有吸引力，
工作环境更为安全舒适，
挣得自然也更多。
所以这个故事是乐观的，
而事情迄今的发展亦是如此。
但有一类特殊的劳动群体，
其境遇却全然不同。
对这个群体来说，
科技彻底取代了他们的工作，
却并未为其带来任何新的就业机会。
当然，我所指的“劳动群体”，
由此我便能提出一个尖锐的问题：
在未来，是否有可能
会出现相当数量的劳动力冗余
正如马的遭遇一般？
出于本能，人们可能会下意识地
反驳道：“荒唐！
牲畜岂能和人相比？”
马的能力固然有限，
当汽车、卡车和拖拉机出现后，
马便没了用武之地。
但人却是具有智慧的，
能够学习，并且会适应环境。
因此理论上讲，
人应当总能找到新的工作，
始终保持与未来经济的联系。
但我们必须意识到的关键是，
未来将威胁取代劳动力的机器
和取代了马匹的汽车、卡车或拖拉机
绝不可同日而语。
未来的机器将具有思维，学习和适应能力。
这便真正意味着
技术终将开始入侵
最根本的人类能力——
那种使我们有别于动物的能力。
正是由于这种能力，
人们才能引领时代发展，
并始终作用于经济
成为其不可或缺的重要一环。
所以今天的信息技术
相对于过去的技术革新
其不同之处究竟在哪里呢？
我将指出三个基本的方面。
第一，我们已经目睹了这一进程
指数爆炸式的增长速度。
想必大家都了解摩尔定律，
但其适用面其实要宽广得多
涵盖了很多不同领域的发展规律，
比如软件，通讯，带宽增长等。
但我们要意识到的关键是
这种增长已经持续了很久，
实际上已经有几十年了。
若从五十年代末
第一批集成电路问世算起，
我们现今的计算能力已经翻了30番。
对于任何事物，这样的增长量都是惊人的，
我们正处在一个时间点，
能够目睹科技巨大的进步，
并且从今往后，科技仍将
继续加速增长。
因此，当我们期待若干年后的未来时，
我们将面临始料未及的变迁；
我们将见证令人惊奇的成就。
狭义上讲，机器开始具有思考能力。
我此话所指，并非是智力堪比人类，
或是科幻小说中出现的人工智能。
我只想指出，机器和算法将能够进行决策，
解决问题，以及最重要的，能够自我学习。
其实如果说有一项核心技术
是科技进步的关键驱动力，
那便是机器学习，
一项正在显示其惊人威力，
颠覆性，以及扩展性的技术。
近来我见到的最佳事例
就有谷歌DeepMind团队研发了
AlphaGo系统，
在历史悠久的围棋游戏上
击败了世界最强高手。
而至少在我看来，
围棋有两个突出的特点。
其一是在下棋时，
棋盘上可能的变化
基本上无法穷尽。
棋局的数目甚至比宇宙中的原子还要多。
这便意味着，
若要造出一台下围棋能赢的电脑，
采用设计国际象棋软件的思路，
通过增加计算资源暴力破解，是不可行的，
必然要采用一种更为复杂，更贴近思考的途径。
第二个突出的特点便是，
即便去请教一位围棋冠军，
他也不一定能讲清自己下棋时
究竟是如何思考的。
棋手常依赖某种直观的判断，
几乎是凭感觉，来决定该走哪手棋。
鉴于以上两点，
按说要把围棋下到世界冠军水平，
机器是无法胜任的，
而事实并非如此，我们应当引起警惕了。
究其原因，我们习惯划一条明确的界线
在界线的一侧，是我们认为
较为基础，常规，重复
且容易预测的工作。
这类工作可能来自不同行业，
不同岗位，所需技能也有高低之分，
但本质上都是可以预测的，
因此终有一日会受到
机器学习的冲击，
并走向自动化。
这类工作数目可是不少
经济体中差不多一半的工作
都属于此类。
而界线另一侧的工作，
我们认为需要特殊的能力，
只有人能胜任，
所以这些工作是安全的。
以我对围棋的了解，
我猜它应该在分界线“安全”的一侧。
但谷歌攻克了这一难题，否定了我的猜测，
也说明这条分界线将会剧烈变动，
移向安全的一侧，
将我们当前认为无法自动化的工作任务
逐渐纳入不安全的范围。
还有一点需要了解，
会遭受冲击的不只是低薪的蓝领工作，
或是教育程度较低的人
所从事的工作。
诸多证据表明
科技的威力正在飞速攀升。
我们已经看到科技对专业工作的冲击，
受到冲击的包括会计师
财政分析师
律师，以及放射科医师等。
而我们目前有很多假设
在探讨哪些职业、任务和工作
未来将遭到自动化的冲击。
这些假设将来都很可能遭到挑战。
当我们汇总这些趋势后，
便能看出，未来我们很可能面临
严重的失业。
就算退一万步讲，
我们也会面临就业不足，或者工资水平停滞
乃至下降。
社会不平等自然也会激增。
以上问题势必会给社会结构
带来巨大压力。
除此之外，还有一个根本的经济问题：
就业是我们当前主要的收入分配机制，
有了收入，消费者就有了购买力
可以购买人们生产出的产品和服务。
为使市场经济繁荣，
需要大量有购买力的消费者
来消化社会生产出的产品和服务。
否则我们就会面临经济停滞
甚至螺旋式下降的风险，
因为没有足够的消费者
购买生产出的产品和服务。
重要的是要认识到，
作为个体，我们每个人都要靠参与市场经济
来获得成功。
我们不妨来想象一位杰出的人物，
比如说史蒂夫·乔布斯，
把他一个人丢在荒岛上，
让他四处奔走
去收集椰子，和其他人一样。
他肯定成不了什么“乔帮主”，
原因很显然，岛上没有手机市场
能让他施展才智，大显身手。
所以市场对我们个人来说至关重要，
同时也是整个社会可持续发展的重中之重。
那么问题就变成了，我们究竟该如何应对挑战？
我们可以在非常理想的框架中看待这一问题。
设想在未来，我们都会减少工作，
拥有更多闲暇时光陪伴家人，
或者做些真正怡情养性的事情，等等。
这是非常美妙的愿景，
值得我们为之全力奋斗。
但同时我们也要务实，
我们极有可能面临严重的收入分配问题。
许多人可能会落在后面。
要解决收入分配问题，
我们最终要找到一条途径
将收入与传统的工作分离。
而据我所知，最直接有效的方法
便是设立某种无条件基本收入。
基本收入已经成为重要的概念，
广受关注及推崇，
世界各国也相继开展了
很多重大试点项目。
我认为基本收入不是灵丹妙药，
不是“一用就见效”的解决方案。
这仅是一个起点，一个设想
还有待我们在其基础上加以完善。
例如，我围绕这样一个设想写过不少文章，
那便是，将显性激励整合到基本收入中。
我具体解释一下，
想象你是个苦苦挣扎的高中生，
正面临被开除的风险。
但假设你知道未来有这么一天，
不管什么情况下，
你都能得到和其他人一样的基本收入。
在我看来，这将造成一种不当动机
使你甘心直接退学。
所以说，办事不能这样一刀切。
反之，与辍学的人相比，应该给高中毕业的人
更多一些的收入。
这种将激励整合到基本收入中的做法，
我们也可以用在其他领域中。
比如，我们可以创造激励来鼓励社区义工，
鼓励互相帮助，
鼓励保护环境的行为，等等。
通过将激励纳入基本收入制度中，
我们便能改善这一制度，
或者把步子迈得再大些，
去解决另一个问题
——未来我们很可能要面对的问题。
那就是：未来我们可能不再需要大量的传统工作了，
那我们又该如何利用时间，
寻找生命的意义，实现人生的圆满呢？
因此，通过改良并推广基本收入制度，
我们可以令其更为合意，
并且更容易为政界采纳，
也更容易在社会中实施，
最终有更大的几率
使这项制度真正落地。
对于推行基本收入制度，
或者任何重大的保险金制度，
我想很多人都会持反对意见。
其中有一条意见尤为根本，
几乎是出于本能，我们会担心
最终有太多人安于坐享其成，
而没有足够的人真正去推动经济发展。
而我在此要表达的观点是
机器将会替我们更好地推动经济发展。
这给我们构建社会，组织经济
提供了更多选择方案。
而我相信，最终这将不仅只是一种选择，
更将成为大势所趋。
原因很显然，当今发生的一切
将使社会面临巨大的压力，
而就业机制又是
赋予消费者购买力，
驱动经济发展的重要抓手。
如果未来这一机制遭到侵蚀，
我们便需要采用其他措施。
否则我们便将面临
社会无法持续运转的风险。
但有一点我坚信不疑，
那便是：如何解决这些问题，
尤其是寻找一条构建未来经济的途径
使得社会中每个阶层
均能从中受益，
将是我们未来亟需共同面对的
至关重要的挑战。
谢谢大家！
I'm going to begin with a scary question:
Are we headed toward
a future without jobs?
The remarkable progress that we're seeing
in technologies like self-driving cars
has led to an explosion
of interest in this question,
but because it's something
that's been asked
so many times in the past,
maybe what we should really be asking
is whether this time is really different.
The fear that automation
might displace workers
and potentially lead
to lots of unemployment
goes back at a minimum 200 years
to the Luddite revolts in England.
And since then, this concern
has come up again and again.
I'm going to guess
that most of you have probably never
heard of the Triple Revolution report,
but this was a very prominent report.
It was put together
by a brilliant group of people —
it actually included
two Nobel laureates —
and this report was presented
to the President of the United States,
and it argued that the US was on the brink
of economic and social upheaval
because industrial automation
was going to put millions of people
out of work.
Now, that report was delivered
to President Lyndon Johnson
in March of 1964.
So that's now over 50 years,
and, of course, that
hasn't really happened.
And that's been the story again and again.
This alarm has been raised repeatedly,
but it's always been a false alarm.
And because it's been a false alarm,
it's led to a very conventional way
of thinking about this.
And that says essentially that yes,
technology may devastate
entire industries.
It may wipe out whole occupations
and types of work.
But at the same time, of course,
progress is going to lead
to entirely new things.
So there will be new industries
that will arise in the future,
and those industries, of course,
will have to hire people.
There'll be new kinds of work
that will appear,
and those might be things that today
we can't really even imagine.
And that has been the story so far,
and it's been a positive story.
It turns out that the new jobs
that have been created
have generally been
a lot better than the old ones.
They have, for example,
been more engaging.
They've been in safer,
more comfortable work environments,
and, of course, they've paid more.
So it has been a positive story.
That's the way things
have played out so far.
But there is one particular
class of worker
for whom the story
has been quite different.
For these workers,
technology has completely
decimated their work,
and it really hasn't created
any new opportunities at all.
And these workers, of course,
are horses.
(Laughter)
So I can ask a very provocative question:
Is it possible that at some
point in the future,
a significant fraction of the human
workforce is going to be made redundant
in the way that horses were?
Now, you might have a very visceral,
reflexive reaction to that.
You might say, "That's absurd.
How can you possibly compare
human beings to horses?"
Horses, of course, are very limited,
and when cars and trucks
and tractors came along,
horses really had nowhere else to turn.
People, on the other hand,
we can learn, we can adapt.
And in theory,
that ought to mean that we can
always find something new to do,
and that we can always remain
relevant to the future economy.
But here's the really
critical thing to understand.
The machines that will threaten
workers in the future
are really nothing like those cars
and trucks and tractors
that displaced horses.
The future is going to be full
of thinking, learning, adapting machines.
And what that really means
is that technology is finally
beginning to encroach
on that fundamental human capability —
the thing that makes us
so different from horses,
and the very thing that, so far,
has allowed us to stay ahead
of the march of progress
and remain relevant,
and, in fact, indispensable
to the economy.
So what is it that is really so different
about today's information technology
relative to what we've seen in the past?
I would point to three fundamental things.
The first thing is that we have seen
this ongoing process
of exponential acceleration.
I know you all know about Moore's law,
but in fact, it's more
it extends in many cases,
for example, to software,
it extends to communications,
bandwidth and so forth.
But the really key thing to understand
is that this acceleration has now
been going on for a really long time.
In fact, it's been going on for decades.
If you measure from the late 1950s,
when the first integrated
circuits were fabricated,
we've seen something on the order
of 30 doublings in computational power
since then.
That's just an extraordinary number
of times to double any quantity,
and what it really means
is that we're now at a point
where we're going to see
just an extraordinary amount
of absolute progress,
and, of course, things are going
to continue to also accelerate
from this point.
So as we look forward
to the coming years and decades,
I think that means
that we're going to see things
that we're really not prepared for.
We're going to see things
that astonish us.
The second key thing
is that the machines are,
in a limited sense, beginning to think.
And by this, I don't mean human-level AI,
or science fiction
I simply mean that machines and algorithms
are making decisions.
They're solving problems,
and most importantly, they're learning.
In fact, if there's one technology
that is truly central to this
and has really become
the driving force behind this,
it's machine learning,
which is just becoming
this incredibly powerful,
disruptive, scalable technology.
One of the best examples
I've seen of that recently
was what Google's DeepMind
division was able to do
with its AlphaGo system.
Now, this is the system that was able
to beat the best player in the world
at the ancient game of Go.
Now, at least to me,
there are two things that really
stand out about the game of Go.
One is that as you're playing the game,
the number of configurations
that the board can be in
is essentially infinite.
There are actually more possibilities
than there are atoms in the universe.
So what that means is,
you're never going to be able to build
a computer to win at the game of Go
the way chess was approached, for example,
which is basically to throw
brute-force computational power at it.
So clearly, a much more sophisticated,
thinking-like approach is needed.
The second thing
that really stands out is that,
if you talk to one
of the championship Go players,
this person cannot necessarily
even really articulate what exactly it is
they're thinking about
as they play the game.
It's often something
that's very intuitive,
it's almost just like a feeling
about which move they should make.
So given those two qualities,
I would say that playing Go
at a world champion level
really ought to be something
that's safe from automation,
and the fact that it isn't should really
raise a cautionary flag for us.
And the reason is that we tend
to draw a very distinct line,
and on one side of that line
are all the jobs and tasks
that we perceive as being on some level
fundamentally routine and repetitive
and predictable.
And we know that these jobs
might be in different industries,
they might be in different occupations
and at different skill levels,
but because they are innately predictable,
we know they're probably at some point
going to be susceptible
to machine learning,
and therefore, to automation.
And make no mistake —
that's a lot of jobs.
That's probably something
on the order of roughly half
the jobs in the economy.
But then on the other side of that line,
we have all the jobs
that require some capability
that we perceive as being uniquely human,
and these are the jobs
that we think are safe.
Now, based on what I know
about the game of Go,
I would've guessed that it really ought
to be on the safe side of that line.
But the fact that it isn't,
and that Google solved this problem,
suggests that that line is going
to be very dynamic.
It's going to shift,
and it's going to shift in a way
that consumes more and more jobs and tasks
that we currently perceive
as being safe from automation.
The other key thing to understand
is that this is by no means just about
low-wage jobs or blue-collar jobs,
or jobs and tasks done by people
that have relatively
low levels of education.
There's lots of evidence to show
that these technologies are rapidly
climbing the skills ladder.
So we already see an impact
on professional jobs —
tasks done by people like accountants,
financial analysts,
journalists,
lawyers, radiologists and so forth.
So a lot of the assumptions that we make
about the kind of occupations
and tasks and jobs
that are going to be threatened
by automation in the future
are very likely to be
challenged going forward.
So as we put these trends together,
I think what it shows is that we could
very well end up in a future
with significant unemployment.
Or at a minimum,
we could face lots of underemployment
or stagnant wages,
maybe even declining wages.
And, of course, soaring levels
of inequality.
All of that, of course, is going to put
a terrific amount of stress
on the fabric of society.
But beyond that, there's also
a fundamental economic problem,
and that arises because jobs
are currently the primary mechanism
that distributes income,
and therefore purchasing power,
to all the consumers that buy the products
and services we're producing.
In order to have a vibrant market economy,
you've got to have
lots and lots of consumers
that are really capable of buying
the products and services
that are being produced.
If you don't have that,
then you run the risk
of economic stagnation,
or maybe even a declining economic spiral,
as there simply aren't enough
customers out there
to buy the products
and services being produced.
It's really important to realize
that all of us as individuals rely
on access to that market economy
in order to be successful.
You can visualize that by thinking
in terms of one really exceptional person.
Imagine for a moment you take,
say, Steve Jobs,
and you drop him
on an island all by himself.
On that island, he's going
to be running around,
gathering coconuts just like anyone else.
He's really not going to be
anything special,
and the reason, of course,
is that there is no market
for him to scale
his incredible talents across.
So access to this market
is really critical to us as individuals,
and also to the entire system
in terms of it being sustainable.
So the question then becomes:
What exactly could we do about this?
And I think you can view this
through a very utopian framework.
You can imagine a future
where we all have to work less,
we have more time for leisure,
more time to spend with our families,
more time to do things that we find
genuinely rewarding
and so forth.
And I think that's a terrific vision.
That's something that we should
absolutely strive to move toward.
But at the same time, I think
we have to be realistic,
and we have to realize
that we're very likely to face
a significant income distribution problem.
A lot of people are likely
to be left behind.
And I think that in order
to solve that problem,
we're ultimately going
to have to find a way
to decouple incomes from traditional work.
And the best, more straightforward
way I know to do that
is some kind of a guaranteed income
or universal basic income.
Now, basic income is becoming
a very important idea.
It's getting a lot
of traction and attention,
there are a lot of important
pilot projects
and experiments going on
throughout the world.
My own view is that a basic income
it's not necessarily
a plug-and-play solution,
but rather, it's a place to start.
It's an idea that we can
build on and refine.
For example, one thing that I have
written quite a lot about
is the possibility of incorporating
explicit incentives into a basic income.
To illustrate that,
imagine that you are a struggling
high school student.
Imagine that you are at risk
of dropping out of school.
And yet, suppose you know
that at some point in the future,
no matter what,
you're going to get the same
basic income as everyone else.
Now, to my mind, that creates
a very perverse incentive
for you to simply give up
and drop out of school.
So I would say, let's not
structure things that way.
Instead, let's pay people who graduate
from high school somewhat more
than those who simply drop out.
And we can take that idea of building
incentives into a basic income,
and maybe extend it to other areas.
For example, we might create
an incentive to work in the community
to help others,
or perhaps to do positive
things for the environment,
and so forth.
So by incorporating incentives
into a basic income,
we might actually improve it,
and also, perhaps, take at least
a couple of steps
towards solving another problem
that I think we're quite possibly
going to face in the future,
and that is, how do we all find
meaning and fulfillment,
and how do we occupy our time
in a world where perhaps
there's less demand for traditional work?
So by extending and refining
a basic income,
I think we can make it look better,
and we can also, perhaps, make it
more politically and socially acceptable
and feasible —
and, of course, by doing that,
we increase the odds
that it will actually come to be.
I think one of the most fundamental,
almost instinctive objections
that many of us have
to the idea of a basic income,
or really to any significant
expansion of the safety net,
is this fear that we're going to end up
with too many people
riding in the economic cart,
and not enough people pulling that cart.
And yet, really, the whole point
I'm making here, of course,
is that in the future,
machines are increasingly going
to be capable of pulling that cart for us.
That should give us more options
for the way we structure
our society and our economy,
And I think eventually, it's going to go
beyond simply being an option,
and it's going to become an imperative.
The reason, of course,
is that all of this is going to put
such a degree of stress on our society,
and also because jobs are that mechanism
that gets purchasing power to consumers
so they can then drive the economy.
If, in fact, that mechanism
begins to erode in the future,
then we're going to need to replace
it with something else
or we're going to face the risk
that our whole system simply
may not be sustainable.
But the bottom line here
is that I really think
that solving these problems,
and especially finding a way
to build a future economy
that works for everyone,
at every level of our society,
is going to be one of the most important
challenges that we all face
in the coming years and decades.
Thank you very much.
(Applause)
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