I'd like to tell you about two games of chess.
我想给你们讲一下关于两场国际象棋竞赛的故事。
The first happened in 1997, in which Garry Kasparov, a human, lost to Deep Blue, a machine.
第一个故事是在1997年，加里·卡斯帕罗夫，一个人类，输给了深蓝，一台机器。
To many, this was the dawn of a new era, one where man would be dominated by machine.
对大多数人来说，这是一个新时代、一个人们将被机器支配的时代的曙光。
But here we are, 20 years on, and the greatest change in how we relate to computers is the iPad, not HAL.
但是20年后的今天，我们和计算机之间关系的最大改变是iPad，不是HAL。
The second game was a freestyle chess tournament in 2005,
第二场是2005年的自由式国际象棋锦标赛，
in which man and machine could enter together as partners, rather than adversaries, if they so chose.
人类与机器可以一起参加比赛，以合作伙伴的身份，而不是对手，如果他们愿意的话。
At first, the results were predictable. Even a supercomputer was beaten by a grandmaster with a relatively weak laptop.
起初，结果是可以预见的。即使是一台超级计算机也会输给特级大师和一台相对较弱的笔记本电脑。
The surprise came at the end. Who won?
可结局令人惊讶。谁赢了？
Not a grandmaster with a supercomputer, but actually two American amateurs using three relatively weak laptops.
不是使用超级计算机的大师，而是两个美国业余选手和他们使用的三台相对较弱的笔记本电脑。
Their ability to coach and manipulate their computers to deeply explore specific positions
他们能训练并控制他们的计算机，去深度的探究精确的棋位，
effectively counteracted the superior chess knowledge of the grandmasters and the superior computational power of other adversaries.
有效地抵消了特级大师更高深的国际象棋知识以及其他对手更高级的运算能力。
This is an astonishing result: average men, average machines beating the best man, the best machine.
这是一个令人吃惊的结果：普通人和普通的机器，击败了最好的人和最好的机器。
And anyways, isn't it supposed to be man versus machine?
不管怎么说，不应该是机器与人对战吗？
Instead, it's about cooperation, and the right type of cooperation.
相反，这是关于合作，以及正确的合作方式的。
We've been paying a lot of attention to Marvin Minsky's vision for artificial intelligence over the last 50 years.
近50年来，我们一直把大量精力放在了马文·明斯基的人工智能的愿景上。
It's a sexy vision, for sure. Many have embraced it. It's become the dominant school of thought in computer science.
这肯定是一个迷人的愿景。很多人已经接受了它。它已成为计算机科学的主流学派。
But as we enter the era of big data, of network systems, of open platforms, and embedded technology,
但是，当我们进入了大数据、网络系统、开放平台和嵌入式技术的时代，
I'd like to suggest it's time to reevaluate an alternative vision that was actually developed around the same time.
我想建议的是，现在是时候重新评估另一种愿景了，这种愿景实际上也是在大约同一时间发展起来的。
I'm talking about J.C.R. Licklider's human-computer symbiosis, perhaps better termed "intelligence augmentation," I.A.
我要谈论的是J.C.R.立克里德的人机共生，或许称为“智能增强”更好，I.A.
Licklider was a computer science titan who had a profound effect on the development of technology and the Internet.
立克里德是一位计算机科学巨人，他对技术和互联网发展有非常深远的影响。
His vision was to enable man and machine to cooperate in making decisions,
他的设想是，使人与机器进行合作，从而作出决定，
controlling complex situations without the inflexible dependence on predetermined programs. Note that word "cooperate."
控制复杂的情况，而不是死板的依赖于预先设定的程序。请注意“合作”这个词。
Licklider encourages us not to take a toaster and make it Data from "Star Trek," but to take a human and make her more capable.
立克里德鼓励我们不是用一个烤面包机，并使其变成《星际迷航》中的科技，而要用一个人，并使她更有能力。
Humans are so amazing -- how we think, our non-linear approaches, our creativity, iterative hypotheses,
人类是如此奇妙--我们的思维方式，我们的非线形方法，我们的创造力，迭代的假设，
all very difficult if possible at all for computers to do.
都很难让计算机做到类似的事。
Licklider intuitively realized this, contemplating humans setting the goals,
立克里德本能地意识到了这一点，考虑到了人们会设定目标，
formulating the hypotheses, determining the criteria, and performing the evaluation.
提出假说，确定标准并执行评估。
Of course, in other ways, humans are so limited. We're terrible at scale, computation and volume.
当然，在其他方面，人类能力有限。我们在比例、计算和容量方面做得很糟。
We require high-end talent management to keep the rock band together and playing.
我们需要高端的人才管理，来让摇滚乐队团结在一起，并进行演奏。
Licklider foresaw computers doing all the routinizable work that was required to prepare the way for insights and decision making.
立克里德预见到所有的程序化的工作都可以由计算机完成，这需要人们为其预先洞察和决策一些东西。
Silently, without much fanfare, this approach has been compiling victories beyond chess.
悄悄地，没有大张旗鼓，这种做法已经超越了象棋的胜利。
Protein folding, a topic that shares the incredible expansiveness of chess
蛋白质折叠，一个和国际象棋一样具有令人难以置信的广阔性的话题，
there are more ways of folding a protein than there are atoms in the universe.
一个蛋白质的折叠方式要比宇宙中的原子还要多。
This is a world-changing problem with huge implications for our ability to understand and treat disease.
这是一个改变世界的问题，对我们有能力了解和治疗疾病具有巨大影响。
And for this task, supercomputer field brute force simply isn't enough.
对于这个任务，只有超级计算机的蛮力还不够。
Foldit, a game created by computer scientists, illustrates the value of the approach.
Foldit，计算机科学家开发的一个游戏，说明了这个方法的价值。
Non-technical, non-biologist amateurs play a video game in which they visually rearrange the structure of the protein,
不具科技背景，不具生物学背景的业余者，在一个电玩中，重新排列了蛋白质的结构
allowing the computer to manage the atomic forces and interactions and identify structural issues.
并允许计算机去管理各种原子力、交互作用，并找出结构性问题。
This approach beat supercomputers 50 percent of the time and tied 30 percent of the time.
这种方法在50%的时间里击败了超级计算机，在30%的时间里与之战平。
Foldit recently made a notable and major scientific discovery by deciphering the structure of the Mason-Pfizer monkey virus.
Foldit最近取得一个显著并重大的科学发现，它破译了梅森辉瑞猴病毒的结构。
A protease that had eluded determination for over 10 years was solved was by three players in a matter of days,
十多年来，人类未曾破解这种蛋白酶，而三名玩家却在短短几天内就解决了，
perhaps the first major scientific advance to come from playing a video game.
也许这是第一次由打游戏带来的重大科学进展。
Last year, on the site of the Twin Towers, the 9/11 memorial opened.
去年，在世贸大楼遗址，911纪念馆开幕。
It displays the names of the thousands of victims using a beautiful concept called "meaningful adjacency."
它通过使用一个很美丽的、叫做“有意义的连结”的概念，展示了上千位遇难者的名字。
It places the names next to each other based on their relationships to one another: friends, families, coworkers.
根据遇难者彼此之间的关系：朋友、家人、同事，它将这些名字放在了一起。
When you put it all together, it's quite a computational challenge:
当你把这些全部都放在一起时，这绝对是一个计算上的挑战：
3,500 victims, 1,800 adjacency requests, the importance of the overall physical specifications and the final aesthetics.
3500名遇难者，1800个邻接的需求，整体形式特征的重要性以及最终的美观。
When first reported by the media, full credit for such a feat was given to an algorithm from the New York City design firm Local Projects.
当第一次被媒体报道时，这件壮举完全归功给了纽约设计公司Local Projects的算法。
The truth is a bit more nuanced.
事实真相却不尽然。
While an algorithm was used to develop the underlying framework, humans used that framework to design the final result.
当采用一种算法来开发底层框架时，人类会利用这一框架来设计最终结果。
So in this case, a computer had evaluated millions of possible layouts, managed a complex relational system,
所以在这种情况下，一台计算机已对数百万种可能的布局进行了评估，对一个复杂的关系体系进行了管理，
and kept track of a very large set of measurements and variables, allowing the humans to focus on design and compositional choices.
并对大量测量数据和变量进行了跟踪，这使得人类能够专注于设计及组合的选择。
So the more you look around you, the more you see Licklider's vision everywhere.
所以你越观察你的四周，就越能发现立克里德的理念无所不在。
Whether it's augmented reality in your iPhone or GPS in your car, human-computer symbiosis is making us more capable.
不论是你的iPhone上面的增强现实，还是你汽车上的GPS，人机共生都使我们更具能力。
So if you want to improve human-computer symbiosis, what can you do?
因此，如果你想要改善人机共生，你能做什么呢？
You can start by designing the human into the process.
你可以从把人类设计到程序中开始。
Instead of thinking about what a computer will do to solve the problem, design the solution around what the human will do as well.
与其思考一台计算机会如何解决问题，不如去设计一个人类遇到问题的解决方案。
When you do this, you'll quickly realize that you spent all of your time on the interface between man and machine,
当你这么做的时候，你会很快意识到你把所有的时间都花在了人机之间的接口上，
specifically on designing away the friction in the interaction.
特别是避免交互矛盾上的设计。
In fact, this friction is more important than the power of the man or the power of the machine in determining overall capability.
事实上，在决定整体能力上，这种矛盾比起人类个别的力量、计算机个别的力量要更重要。
That's why two amateurs with a few laptops handily beat a supercomputer and a grandmaster.
这就是为什么两个持有几台笔记本电脑的业余选手，轻松击败了一台超级计算机和一位特级大师。
What Kasparov calls process is a byproduct of friction.
卡斯帕罗夫称这种过程是矛盾的一种副产品。
The better the process, the less the friction. And minimizing friction turns out to be the decisive variable.
过程越顺利，矛盾就越少。因此，减少矛盾成为了决定性的变量。
Or take another example: big data.
再举另外一个例子：大数据。
Every interaction we have in the world is recorded by an ever growing array of sensors: your phone, your credit card, your computer.
我们在世界上的每个互动，都被一系列不断增长的传感器记录了下来：你的电话、你的信用卡、你的计算机。
The result is big data, and it actually presents us with an opportunity to more deeply understand the human condition.
结果就是大数据，并给我们呈现了一个更深入了解人类状态的机会。

The major emphasis of most approaches to big data focus on,
对于大数据，大多数方法重点关注的是：
"How do I store this data? How do I search this data? How do I process this data?"
“我要怎么保存这个数据？我要怎么搜索这个数据？我要怎么处理这个数据？”
These are necessary but insufficient questions. The imperative is not to figure out how to compute, but what to compute.
这些问题是必要的，但并不全面。当务之急不是去搞清如何计算，而是搞清要计算什么。
How do you impose human intuition on data at this scale?
你要如何把人的直觉加到这种规模的数据上呢？
Again, we start by designing the human into the process.
同样，我们也从把人类设计到程序中开始。
When PayPal was first starting as a business, their biggest challenge was not, "How do I send money back and forth online?"
在PayPal创业初期，他们最大的挑战不是“我要如何在线流动资金”
It was, "How do I do that without being defrauded by organized crime?"
而是“我该如何做，才能不成为组织化犯罪行为下的诈欺工具？”
Why so challenging? Because while computers can learn to detect and identify fraud based on patterns,
为什么这么有挑战性？因为虽然计算机可以建模，学习如何侦测并辨识诈骗行为，
they can't learn to do that based on patterns they've never seen before, and organized crime has a lot in common with this audience:
但是他们不能根据他们从未见过的模式来这么做，这和组织化犯罪有很多共同点：
brilliant people, relentlessly resourceful, entrepreneurial spirit, and one huge and important difference: purpose.
杰出的人才，足智多谋、有创业精神，还有一个重大的区别：目的。
And so while computers alone can catch all but the cleverest fraudsters,
所以虽然计算机本身可以抓到除了最聪明的诈骗者之外的所有人，
catching the cleverest is the difference between success and failure.
但成功与失败的区别就在于能否抓到最聪明的人。
There's a whole class of problems like this, ones with adaptive adversaries.
像这类的问题有很多，其中一类就是适应性强的对手。
They rarely if ever present with a repeatable pattern that's discernable to computers.
他们很少重复出招，计算机无可辨识。
Instead, there's some inherent component of innovation or disruption, and increasingly these problems are buried in big data.
相反，创新和破坏有一些固有的成分，这些问题越来越多地隐藏在了大数据中。
For example, terrorism. Terrorists are always adapting in minor and major ways to new circumstances,
比如说恐怖主义。恐怖分子总在适应新环境，有时明显有时不很明显，
and despite what you might see on TV, these adaptations, and the detection of them, are fundamentally human.
不管你在电视上看到了什么，发现这些变化的基本都是人类。
Computers don't detect novel patterns and new behaviors, but humans do.
计算机无法发现新模式及新行为，但人类却可以。
Humans, using technology, testing hypotheses, searching for insight by asking machines to do things for them.
人类运用技术，测试假设，通过让机器为他们做事来增强其自身的洞察力。
Osama bin Laden was not caught by artificial intelligence.
奥萨马·本·拉登的可不是被人工智能抓住的。
He was caught by dedicated, resourceful, brilliant people in partnerships with various technologies.
抓住他的是那些专注奉献、足智多谋、才华横溢的人类，而他们应用了各种技术。
As appealing as it might sound, you cannot algorithmically data mine your way to the answer.
听起来很吸引人，你却不能通过算法进行数据挖掘，找到答案。
There is no "Find Terrorist" button,
根本不存在什么“寻找恐怖分子”的按钮，
and the more data we integrate from a vast variety of sources across a wide variety of data formats from very disparate systems,
我们从各种来源、各种数据格式，各种系统中整合的数据越多，
the less effective data mining can be.
数据挖掘的效率就越低。
Instead, people will have to look at data and search for insight, and as Licklider foresaw long ago,
相反，人们会通过观察数据，寻找洞察力，正如立克里德在很久以前预测的，
the key to great results here is the right type of cooperation, and as Kasparov realized, that means minimizing friction at the interface.
这里取得重大成果的关键在于正确的合作方式，而正如卡斯帕罗夫所认识到的，这意味着在人机接口上减少矛盾。
Now this approach makes possible things like combing through all available data from very different sources,
现在，这种方法可以让像梳理来自各种来源的数据、
identifying key relationships and putting them in one place, something that's been nearly impossible to do before.
确定关键联系、并把它们放在一起变得可能，而这些在之前都几乎是不可能做到的。
To some, this has terrifying privacy and civil liberties implications.
对一些人来说，这对隐私及公民自由是种威胁。
To others it foretells of an era of greater privacy and civil liberties protections,
而对另一些人来说，它预示着一个有更好的隐私及公民自由保护的时代，
but privacy and civil liberties are of fundamental importance.
但隐私和公民自由是至关重要的。
That must be acknowledged, and they can't be swept aside, even with the best of intents.
我们必须认可这点，即使是出于最好的意图，也不能将它们置之不理。
So let's explore, through a couple of examples, the impact that technologies built to drive human-computer symbiosis have had in recent time.
所以让我们现在就来通过一些例子来探讨，近年来促进人机共生实现的技术对我们产生的影响。
In October, 2007, U.S. and coalition forces raided an al Qaeda safe house in the city of Sinjar on the Syrian border of Iraq.
在2017年10月，美国和联军袭击了基地组织位于叙利亚和伊拉克边境辛贾尔市的一个安全驻所。
They found a treasure trove of documents: 700 biographical sketches of foreign fighters.
他们发现了一批珍贵的文件：700个外国战士的人物小传。
These foreign fighters had left their families in the Gulf, the Levant and North Africa to join al Qaeda in Iraq.
这些外国战士离开了在波斯湾、地中海东部地区和北非的家人，加入了伊拉克的基地组织。
These records were human resource forms. The foreign fighters filled them out as they joined the organization.
这些记录是人力资源表格。当外国战士加入组织时，就会填上这些。
It turns out that al Qaeda, too, is not without its bureaucracy.
事实证明，基地组织也有官僚体制。
They answered questions like, "Who recruited you?" "What's your hometown?" "What occupation do you seek?"
他们回答过这样的问题：“谁招募的你？你的家乡在哪？你想从事什么职业？”
In that last question, a surprising insight was revealed.
在最后一个问题中，洞察力惊人地显现了出来。
The vast majority of foreign fighters were seeking to become suicide bombers for martyrdom -- hugely important,
绝大多数外国战士都想成为殉难者式的自杀炸弹手--这点非常重要，
since between 2003 and 2007, Iraq had 1,382 suicide bombings, a major source of instability.
因为2003年到2007年间，伊拉克有1382起的自杀性爆炸事件，这是一个不稳定因素来源。
Analyzing this data was hard. The originals were sheets of paper in Arabic that had to be scanned and translated.
分析这些数据很难。因为原件都是纸质版的阿拉伯文，必须进行扫描和翻译。
The friction in the process did not allow for meaningful results in an operational time frame using humans, PDFs and tenacity alone.
这个过程中会有一些阻力，若仅靠人力、PDF和坚持不懈，则无法在操作时间内得到有效结果。
The researchers had to lever up their human minds with technology to dive deeper,
由于技术不断深化，研究人员不得不提高自己的思维深度，
to explore non-obvious hypotheses, and in fact, insights emerged.
这样才能去探索到那些不太明显的假设，事实上，洞见力就产生了。
Twenty percent of the foreign fighters were from Libya, 50 percent of those from a single town in Libya,
国外战士中，有20%来自利比亚，其中又有50%来自利比亚的一个城镇，
hugely important since prior statistics put that figure at three percent.
这非常重要，因为此前的统计数字为百分之三。
It also helped to hone in on a figure of rising importance in al Qaeda, Abu Yahya al-Libi,
这也有助于研究基地组织中一个数字不断上升的重要性，阿布·亚哈·利比，
a senior cleric in the Libyan Islamic fighting group.
是一个利比亚伊斯兰战斗小组的资深神职人员。
In March of 2007, he gave a speech, after which there was a surge in participation amongst Libyan foreign fighters.
在2007年3月，他发表了一次演说，在那之后，利比亚外国战士的参与激增。
Perhaps most clever of all, though, and least obvious, by flipping the data on its head,
或许这当中最聪明也最不明显的，从头分析数据，
the researchers were able to deeply explore the coordination networks in Syria
研究者们能够深入探讨叙利亚的协调网络
that were ultimately responsible for receiving and transporting the foreign fighters to the border.
最终是如何负责接收和运送外国战士到边境的。
These were networks of mercenaries, not ideologues, who were in the coordination business for profit.
这些都是网络的雇佣兵，不是理论家，他们在协调业务以盈利。
For example, they charged Saudi foreign fighters substantially more than Libyans, money that would have otherwise gone to al Qaeda.
例如，他们向沙特的外国武装人员收取的费用，远远超过了利比亚人，而这些钱本来是属于基地组织的。
Perhaps the adversary would disrupt their own network if they knew they cheating would-be jihadists.
也许对手会扰乱他们的网络，如果他们知道他们在欺骗潜在的圣战主义者。
In January, 2010, a devastating 7.0 earthquake struck Haiti, third deadliest earthquake of all time,
2010年1月，一场毁灭性的7级地震袭击了海地，这是有史以来第三大致命的地震，
left one million people, 10 percent of the population, homeless.
使无家可归者达一百万人，占总人口的10%。
One seemingly small aspect of the overall relief effort became increasingly important as the delivery of food and water started rolling.
随着食物和水的运输工作的展开，原本在救济工作中看起来很渺小的一方面，却变得越来越重要。
January and February are the dry months in Haiti, yet many of the camps had developed standing water.
一月和二月是海地的干旱月份，但许多营地已经形成了积水。
The only institution with detailed knowledge of Haiti's floodplains had been leveled in the earthquake, leadership inside.
唯一拥有海地洪泛区详细情况的组织，已经在地震中被夷为平地。
So the question is, which camps are at risk, how many people are in these camps,
所以问题是，哪些营地面临危险，这些营地有多少人，
what's the timeline for flooding, and given very limited resources and infrastructure, how do we prioritize the relocation?
洪水的时间表是什么，以及由于资源和基础设施非常有限，我们该如何决定搬迁的优先级？
The data was incredibly disparate. The U.S. Army had detailed knowledge for only a small section of the country.
这些数据惊人地不同。美国军队只有一小部分地区的细节资料。
There was data online from a 2006 environmental risk conference, other geospatial data, none of it integrated.
当时还有从2006年环境风险座谈会的网络数据，其他的地理空间数据，没有一项可以用来整合使用。
The human goal here was to identify camps for relocation based on priority need.
人类要辨识营地的目地，是为了能根据他们的需要的优先级进行搬迁。
The computer had to integrate a vast amount of geospacial information,
计算机需要整合大量的地理信息、
social media data and relief organization information to answer this question.
社群媒体的数据和救灾团体的资料，来回答这个问题。
By implementing a superior process, what was otherwise a task for 40 people over three months
通过执行一个更高级的处理过程，原本需要40个人花费超过三个月的时间的任务，
became a simple job for three people in 40 hours, all victories for human-computer symbiosis.
变成了一个只要三个人花40个小时的简单的工作，这都是人类跟计算机合作的胜利。
We're more than 50 years into Licklider's vision for the future,
我们比立克里德对未来的愿景还超前了50年，
and the data suggests that we should be quite excited about tackling this century's hardest problems, man and machine in cooperation together. Thank you.
而数据表明，我们应该为可以应对本世纪最困难的问题--人机合作而感到兴奋。谢谢。