I'd like to tell you about two games of chess. The first happened in 1997, in which Garry Kasparov, a human, lost to Deep Blue, a machine. To many, this was the dawn of a new era, one where man would be dominated by machine. But here we are, 20 years on, and the greatest change in how we relate to computers is the iPad, not HAL.
我想告诉你们两场象棋比赛。 一场发生在1997年,卡斯帕罗夫, 一个人类, 输给了‘深蓝’,一部机器。 对许多人来说,这是一个新时代的黎明, 一个人被机器统治的时代。 但现在的我们,20年已经过去了,而最能改变 我们与电脑之间关系的是IPAD, 不是 HAL。
The second game was a freestyle chess tournament in 2005, in which man and machine could enter together as partners, rather than adversaries, if they so chose. At first, the results were predictable. Even a supercomputer was beaten by a grandmaster with a relatively weak laptop. The surprise came at the end. Who won? Not a grandmaster with a supercomputer, but actually two American amateurs using three relatively weak laptops. Their ability to coach and manipulate their computers to deeply explore specific positions effectively counteracted the superior chess knowledge of the grandmasters and the superior computational power of other adversaries. This is an astonishing result: average men, average machines beating the best man, the best machine. And anyways, isn't it supposed to be man versus machine? Instead, it's about cooperation, and the right type of cooperation.
第二场是自由式国际象棋锦标赛 在2005年,人类与机器可以一起进入比赛 以合作伙伴的身份,而不是敌人,如果他们这样选择。 起初,结果是可以预测的。 即使是一台超级计算机也会输给特级大师 和一台相对较弱的便携式计算机。 可结局令人惊讶。谁赢了? 不是使用超级计算机的大师, 而实际上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。 他们有能力知道和操纵他们的计算机 从而深入探索具体的位置 以有效的方法抵消 大师和卓越计算的优越的国际象棋知识 和其他对手。 这是一个令人吃惊的结果: 普通男性, 一般的计算机击败最好的人和最好的机器。 不管怎么说,不应该是机器于人对战吗? 相反,它是关于合作和正确的合作方式。
We've been paying a lot of attention to Marvin Minsky's vision for artificial intelligence over the last 50 years. It's a sexy vision, for sure. Many have embraced it. It's become the dominant school of thought in computer science. But as we enter the era of big data, of network systems, of open platforms, and embedded technology, I'd like to suggest it's time to reevaluate an alternative vision that was actually developed around the same time. I'm talking about J.C.R. Licklider's human-computer symbiosis, perhaps better termed "intelligence augmentation," I.A.
近 50 年,我们一直集中大量的精力到 Marvin Minsky 的人工智能的愿景。 它是一个性感的远景,这是肯定的。很多人已经接受它了。 它已成为计算机科学的主流学派。 但是,当我们进入了大数据的时代、 网络系统、 开放平台和嵌入式技术, 我想建议是重新评估另一个的远景的时候了 这实际上是大约在同一时间进行开发的。 我讨论的是 J.C.R.Licklider 的人机共生, 或许更好地被称为"智能强化"一I.A.
Licklider was a computer science titan who had a profound effect on the development of technology and the Internet. His vision was to enable man and machine to cooperate in making decisions, controlling complex situations without the inflexible dependence on predetermined programs. Note that word "cooperate." 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. Humans are so amazing -- how we think, our non-linear approaches, our creativity, iterative hypotheses, all very difficult if possible at all for computers to do. Licklider intuitively realized this, contemplating humans setting the goals, formulating the hypotheses, determining the criteria, and performing the evaluation. Of course, in other ways, humans are so limited. We're terrible at scale, computation and volume. We require high-end talent management to keep the rock band together and playing. Licklider foresaw computers doing all the routinizable work that was required to prepare the way for insights and decision making.
Licklider 是一位计算机科学巨人 他对技术和互联网发展有非常深刻的影响。 他的设想是,使人与机器进行合作 从而作出决定,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,这个词语"合作"。 Licklider 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, 而要采取一个人,并使她更有能力。 人类如此惊人 — 我们的思维 我们的非线形方法,我们的创造力, 迭代的假设,都很难 让计算机做到类似的事。 Licklider 直观地认识了这一点,考虑人类 设定目标,提出假说, 确定的标准,并进行评价。 当然,在其他方面,人类是如此有限。 我们在大规模、 计算和容量方面做得很遭。 我们需要高端的人才管理 以保持摇滚乐队一起演奏。 Licklider 预见到所有的程序化的工作可以由计算机完成 这需要预先准备目标和决策的方法。
Silently, without much fanfare, this approach has been compiling victories beyond chess. Protein folding, a topic that shares the incredible expansiveness of chess — there are more ways of folding a protein than there are atoms in the universe. This is a world-changing problem with huge implications for our ability to understand and treat disease. And for this task, supercomputer field brute force simply isn't enough. Foldit, a game created by computer scientists, illustrates the value of the approach. Non-technical, non-biologist amateurs play a video game in which they visually rearrange the structure of the protein, allowing the computer to manage the atomic forces and interactions and identify structural issues. This approach beat supercomputers 50 percent of the time and tied 30 percent of the time. Foldit recently made a notable and major scientific discovery by deciphering the structure of the Mason-Pfizer monkey virus. A protease that had eluded determination for over 10 years was solved was by three players in a matter of days, perhaps the first major scientific advance to come from playing a video game.
安静得,没有大张旗鼓, 这种做法已经超越了象棋的胜利。 蛋白质排列,一个同样令人难以置信的广阔的国际象棋的话题 — — 蛋白质排列方式要比在宇宙中的原子更多。 这对改变世界问题启示了 我们有能力了解和治疗疾病。 而对于这个任务,只有超级计算机的蛮力还不够。 Foldit,计算机科学家创建的一个游戏, 说明了这个方法的价值。 非技术性、 非生物学家业余玩的视频游戏 在其中他们直观地重新排列蛋白质的结构, 允许此计算机管理原子的力量 和互动,并识别结构问题。 这种方法以50%的几率击败了超级计算机 以30%的几率战平。 Foldit最近取得一个显著并重大的科学发现 它破译梅森辉瑞猴病毒的结构。 一种躲避测定十多年的蛋白酶 被三名球员在仅仅几天时间就解决了, 也许这是第一次重大科学进展 源于玩视频游戏。
Last year, on the site of the Twin Towers, the 9/11 memorial opened. It displays the names of the thousands of victims using a beautiful concept called "meaningful adjacency." It places the names next to each other based on their relationships to one another: friends, families, coworkers. When you put it all together, it's quite a computational challenge: 3,500 victims, 1,800 adjacency requests, the importance of the overall physical specifications and the final aesthetics. 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. The truth is a bit more nuanced. While an algorithm was used to develop the underlying framework, humans used that framework to design the final result. So in this case, a computer had evaluated millions of possible layouts, managed a complex relational system, and kept track of a very large set of measurements and variables, allowing the humans to focus on design and compositional choices. So the more you look around you, the more you see Licklider's vision everywhere. Whether it's augmented reality in your iPhone or GPS in your car, human-computer symbiosis is making us more capable.
去年,该站点的双子塔, 9/11 纪念馆开幕。 它显示了数千名受害者的名称 通过一个美丽的概念称为"有意义的邻接"。 它把他们的名字彼此相邻的安排在一起,根据 从一个到另一个人的关系: 朋友、 家人、 同事。 当你把它放在一起时,这是相当大的计算 挑战:3,500名 受害者、 1,800名邻接请求, 整体物理属性的重要性 和最后的审美。 当第一次被媒体报道时,这件壮举完全归功 给了纽约 本地的设计公司设计的运算法则。事实真相更为微妙。 虽然一种运算法则被用来开发基本框架, 人类利用这一框架来设计最终的结果。 所以在这种情况下,计算机已评估了数百万种 可能的布局, 管理一个复杂的关系系统, 和跟踪大量测量数据 和变量,使人类能够专注于 设计和组合的选择。 所以你越常环顾你的周围, 在各个地方,您越常看到 Licklider 的愿景。 无论是已经现实在你的iPhone的技术或在你车上的GPS的技术, 人机共生使我们更有能力。
So if you want to improve human-computer symbiosis, what can you do? You can start by designing the human into the process. Instead of thinking about what a computer will do to solve the problem, design the solution around what the human will do as well. When you do this, you'll quickly realize that you spent all of your time on the interface between man and machine, specifically on designing away the friction in the interaction. In fact, this friction is more important than the power of the man or the power of the machine in determining overall capability. That's why two amateurs with a few laptops handily beat a supercomputer and a grandmaster. What Kasparov calls process is a byproduct of friction. The better the process, the less the friction. And minimizing friction turns out to be the decisive variable.
因此,如果您想要改善人机共生 你可以做什么? 您可以从将人设计到过程中开始。 而不是思考如何让计算机解决问题, 围绕着人去设计结局方案。 当您执行此操作时,您很快就会发现你花了 你所有的时间上在人和机器之间的接口上, 特别是关于设计互动中的摩擦。 事实上,这种冲突比 人或机器的力量更重要, 从整体能力上讲。 这就是为什么几个笔记本电脑和两个业余选手 轻松击败了一台超级计算机和特级大师。 卡斯帕罗夫称这个过程是摩擦的副产品。 越好的过程,摩擦越少。 尽量减少摩擦原来是决定性变量。
Or take another example: big data. Every interaction we have in the world is recorded by an ever growing array of sensors: your phone, your credit card, your computer. The result is big data, and it actually presents us with an opportunity to more deeply understand the human condition. The major emphasis of most approaches to big data focus on, "How do I store this data? How do I search this data? How do I process this data?" These are necessary but insufficient questions. The imperative is not to figure out how to compute, but what to compute. How do you impose human intuition on data at this scale?
或再举一个例子: 海量数据。 我们在世界上每个互动都被记录着 由与日俱增的传感器:您的电话, 您的信用卡,您的计算机。其结果是大量的数据, 同时它实际上提供了我们一个机会 去更深入地理解人类的特点。 处理海量数据的主要方法 是集中在,"如何存储这些数据?如何搜索 这些数据?如何处理这些数据?" 这些都是必要但不完全的问题。 当务之急是不弄清楚如何计算, 但用什么来计算。如何加入的人类直觉 在这种规模的数据上?
Again, we start by designing the human into the process. When PayPal was first starting as a business, their biggest challenge was not, "How do I send money back and forth online?" It was, "How do I do that without being defrauded by organized crime?" Why so challenging? Because while computers can learn to detect and identify fraud based on patterns, 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: brilliant people, relentlessly resourceful, entrepreneurial spirit — (Laughter) — and one huge and important difference: purpose. And so while computers alone can catch all but the cleverest fraudsters, catching the cleverest is the difference between success and failure.
又一次,我们开始设计把人类引入这一进程。 PayPal 作为一家企业,当他们第一次启动时,他们最大的 挑战不是,"如何在线转账?" 而是,"如何避免有组织犯罪的诈骗?" 为什么如此具有挑战性?因为虽然计算机能学到 探测和识别基于模式的欺诈, 他们学不会做基于模式 之外的判断,这同有组织犯罪 有很多共同点: 聪明, 足智多谋、 有创业精神 —(笑声)— 还有一个重大的区别:目的。 所以在单独的计算机可以捕获所有得同时,最聪明 的诈骗犯捕捉最聪明的,区别就是 成功与失败。
There's a whole class of problems like this, ones with adaptive adversaries. They rarely if ever present with a repeatable pattern that's discernable to computers. Instead, there's some inherent component of innovation or disruption, and increasingly these problems are buried in big data.
像这类的问题有很多,都是 相互适应。他们很少显示出 可以辨认到计算机的可重复执行的模式。 相反,有一些继承下来的创新或中断的部分 同时这些问题越来越多地被藏在了大量的数据中。
For example, terrorism. Terrorists are always adapting in minor and major ways to new circumstances, and despite what you might see on TV, these adaptations, and the detection of them, are fundamentally human. Computers don't detect novel patterns and new behaviors, but humans do. Humans, using technology, testing hypotheses, searching for insight by asking machines to do things for them. Osama bin Laden was not caught by artificial intelligence. He was caught by dedicated, resourceful, brilliant people in partnerships with various technologies.
例如,恐怖主义。恐怖分子总可以适应这种 次要和主要方式的新环境,而且即使 在电视上,你可能会看到这些适应能力, 和对他们的检测,基本上都是人类。 计算机不会检测新的模式和新的行为, 但人类可以。人类,利用技术、 测试假设, 通过机器为他们寻找目标。 本 · 拉登不是被人工智能抓住的。 他被抓住是因为专注、足智多谋和聪明的 人与各种技术的合作。
As appealing as it might sound, you cannot algorithmically data mine your way to the answer. There is no "Find Terrorist" button, and the more data we integrate from a vast variety of sources across a wide variety of data formats from very disparate systems, the less effective data mining can be. Instead, people will have to look at data and search for insight, and as Licklider foresaw long ago, the key to great results here is the right type of cooperation, and as Kasparov realized, that means minimizing friction at the interface.
这听起来颇具吸引力,你不能通过计算 数据的方式来挖掘你的答案。 没有"找到恐怖分子"的按钮,同时越多的数据 ,整合的来源越多, 格式的种类越广,形成了一个非常 迥异的系统,数据挖掘也更加低效。 相反,人们还要参考数据 和搜索目标,Licklider 很久以前预见到的, 成功的关键是正确的合作, 正如卡斯帕罗夫意识到的, 这意味着,尽量减少操作界面的摩擦。
Now this approach makes possible things like combing through all available data from very different sources, identifying key relationships and putting them in one place, something that's been nearly impossible to do before. To some, this has terrifying privacy and civil liberties implications. To others it foretells of an era of greater privacy and civil liberties protections, but privacy and civil liberties are of fundamental importance. That must be acknowledged, and they can't be swept aside, even with the best of intents.
现在这种方法使得梳理 所有可用且来源非常不同的数据成为了可能, 确定关键的关系,并将它们放在一个地方, 之前看似几乎不可能完成的东西。 对某些人来说,这会影响隐私和公民自由 的实行。对其他人来说,这预示了的一个更加伟大的时代 对于保护隐私和公民自由, 但隐私和公民自由是根本也是最重要的。 必须承认,他们不能被抛在一边, 即使是出于好意。
So let's explore, through a couple of examples, the impact that technologies built to drive human-computer symbiosis have had in recent time.
让我们探讨,通过几个例子, 科技构建驱动人机共生 最近产生的影响。
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. They found a treasure trove of documents: 700 biographical sketches of foreign fighters. These foreign fighters had left their families in the Gulf, the Levant and North Africa to join al Qaeda in Iraq. These records were human resource forms. The foreign fighters filled them out as they joined the organization. It turns out that al Qaeda, too, is not without its bureaucracy. (Laughter) They answered questions like, "Who recruited you?" "What's your hometown?" "What occupation do you seek?"
2007 年 10 月,美国和盟军部队突袭了 一个在辛贾尔市的基地组织的安全屋 位于叙利亚的伊拉克边界。 他们发现一份宝贵的文档: 700个外国战士的小传草稿。 这些外国战士在海湾地区,他们离开他们 南欧和北非的家,加入在伊拉克境内的基地组织。 这些记录是人力资源管理的形式。 外国战士填写这些之后加入该组织。 事实证明,基地组织,也, 并不是没有其官僚作风。(笑声) 他们回答类似的问题,"谁聘请的你?" "你的家乡是哪儿?""你要求从事什么职业呢?"
In that last question, a surprising insight was revealed. The vast majority of foreign fighters were seeking to become suicide bombers for martyrdom -- hugely important, since between 2003 and 2007, Iraq had 1,382 suicide bombings, a major source of instability. Analyzing this data was hard. The originals were sheets of paper in Arabic that had to be scanned and translated. The friction in the process did not allow for meaningful results in an operational time frame using humans, PDFs and tenacity alone. The researchers had to lever up their human minds with technology to dive deeper, to explore non-obvious hypotheses, and in fact, insights emerged. Twenty percent of the foreign fighters were from Libya, 50 percent of those from a single town in Libya, hugely important since prior statistics put that figure at three percent. It also helped to hone in on a figure of rising importance in al Qaeda, Abu Yahya al-Libi, a senior cleric in the Libyan Islamic fighting group. In March of 2007, he gave a speech, after which there was a surge in participation amongst Libyan foreign fighters.
这个最后的问题,据透露出十分惊人的洞察力。 绝大多数的外国战士 在谋求成为自爆烈士 — — 极其重要的是,自 2003 年至 2007 年,伊拉克 有 1,382 自杀性爆炸,不稳定的主要根源。 分析这些数据是困难的。原始的表格 是阿拉伯语的,必须要扫描和翻译。 这过程中的摩擦不允许有意义的 运用时间构架人类的结果,PDFs 和独立的坚韧。 研究者们不得不撬动了他们人类的思维 并运用科技去潜得更深,去探索非显而易见 的假说,事实上,目标实现了。 20 % 的外国战士来自利比亚, 50%的人来自利比亚的同一个镇, 非常重要,因为以前的统计数字显示, 是3%。它还帮助找出 了基地组织新的重要人物,阿布叶海亚 · 阿尔-利比亚, 利比亚伊斯兰战斗组中的高级官员。 2007 年 3 月,他给过一个演讲, 之后 来自利比亚的外国战士数量激增。
Perhaps most clever of all, though, and least obvious, by flipping the data on its head, the researchers were able to deeply explore the coordination networks in Syria that were ultimately responsible for receiving and transporting the foreign fighters to the border. These were networks of mercenaries, not ideologues, who were in the coordination business for profit. For example, they charged Saudi foreign fighters substantially more than Libyans, money that would have otherwise gone to al Qaeda. Perhaps the adversary would disrupt their own network if they knew they cheating would-be jihadists.
也许最聪明的是,而且,最不明显的是, 通过整理其有关数据,研究人员 能够深入的探索了叙利亚的协调网络, 它负责接收和 运送外国战士到边境。 这些都是网络的雇佣军,不是空想, 同过协调生意赚取利润。 例如,他们收取沙特外国战士的钱 大大超过利比亚的,钱 最终会去向基地组织。 敌人可能会破坏他们的网络 如果他们知道他们作弊是为了圣战。
In January, 2010, a devastating 7.0 earthquake struck Haiti, third deadliest earthquake of all time, left one million people, 10 percent of the population, homeless. One seemingly small aspect of the overall relief effort became increasingly important as the delivery of food and water started rolling. January and February are the dry months in Haiti, yet many of the camps had developed standing water. The only institution with detailed knowledge of Haiti's floodplains had been leveled in the earthquake, leadership inside. So the question is, which camps are at risk, how many people are in these camps, what's the timeline for flooding, and given very limited resources and infrastructure, how do we prioritize the relocation? The data was incredibly disparate. The U.S. Army had detailed knowledge for only a small section of the country. There was data online from a 2006 environmental risk conference, other geospatial data, none of it integrated. The human goal here was to identify camps for relocation based on priority need. The computer had to integrate a vast amount of geospacial information, social media data and relief organization information to answer this question. By implementing a superior process, what was otherwise a task for 40 people over three months became a simple job for three people in 40 hours,
2010 年 1 月,破坏性的 7.0级地震袭击了海地, 有史以来第三次惨重的地震灾害,100 万人, 10%的人口,无家可归。 一个看似小规模的整体救灾工作 变得越来越重要,从提供粮食 和水开始。 在海地,一月和二月是干燥的月份 然而许多难民的帐篷都被水淹了。 唯一拥有海地详细信息的机构 已经被洪水淹没了 在地震中,包括领导。 所以,问题是,哪些营地处于危险之中, 在这些营地有多少人, 洪水的时间是什么时候,给予有限的资源和时间 和基础设施,我们如何排定搬迁呢? 数据得出迥然不同的结论。美国陆军曾 有一小部分这个国家详细资料。 在线数据源于2006年环境风险 会议,其他的地理空间数据,没有其它的集成。 在这里,人的目标是要确定可以搬迁的营地 基于优先级别的需要。 计算机不得不把大量的空间地理 信息、 社交媒体数据和救援组织 信息集成后来回答这个问题。 通过实施一个优秀的程序,要不然 这是一个需要40 人超过三个月才能完成的任务 现在简化到三个人用40小时就能完成的工作,
all victories for human-computer symbiosis.
这是人机共生所取得的胜利。
We're more than 50 years into Licklider's vision for the future, 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. (Applause) (Applause)
Licklider 的梦想, 50 年之后 的未来,数据表明我们应该 很兴奋的解决这个世纪最困难的问题, 人与机器在一起合作。 谢谢。(掌声) (掌声)