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I'm going to talk a little bit about where technology's going. And often technology comes to us, we're surprised by what it brings. But there's actually a large aspect of technology that's much more predictable, and that's because technological systems of all sorts have leanings, they have urgencies, they have tendencies. And those tendencies are derived from the very nature of the physics, chemistry of wires and switches and electrons, and they will make reoccurring patterns again and again. And so those patterns produce these tendencies, these leanings.
我打算谈一谈技术的发展趋势。 当（新的）技术到来时， 常常会令我们感到惊讶。 但事实上，技术在很大程度上 是能够被预见的。 这是因为所有的技术 都有某种倾向性， 有某种冲动， 有某种趋势。 这些趋势是由电线、开关、以及电子的 物理和化学本质所决定的， 并且呈现出不断重复的模式。 或者说，这些模式形成了 某种趋势、某种倾向。
You can almost think of it as sort of like gravity. Imagine raindrops falling into a valley. The actual path of a raindrop as it goes down the valley is unpredictable. We cannot see where it's going, but the general direction is very inevitable: it's downward. And so these baked-in tendencies and urgencies in technological systems give us a sense of where things are going at the large form. So in a large sense, I would say that telephones were inevitable, but the iPhone was not. The Internet was inevitable, but Twitter was not.
你可以把它看成类似于重力的东西。 想象雨点汇入山谷： 一滴雨点流入山谷的实际路径 是无法预测的。 我们并不知道它的具体走向， 但大方向是很显然的： 它往下流。 因此，这些内在趋势和冲动， 深深扎根于技术系统中， 使我们能够感知它们的大体方向。 具体点说， 电话是必然的， 但 iPhone 不是； 因特网是必然的， 但推特不是。
So we have many ongoing tendencies right now, and I think one of the chief among them is this tendency to make things smarter and smarter. I call it cognifying -- cognification -- also known as artificial intelligence, or AI. And I think that's going to be one of the most influential developments and trends and directions and drives in our society in the next 20 years.
同样道理， 当下有许多正在发生的趋势， 而我认为其中最重要的一个 是让物体变得越来越聪明。 我称之为“知化”， 也就是人们常说的 人工智能，或者 AI。 我认为在未来二十年中， 这将是社会中最具影响力的 发展趋势和驱动力。
So, of course, it's already here. We already have AI, and often it works in the background, in the back offices of hospitals, where it's used to diagnose X-rays better than a human doctor. It's in legal offices, where it's used to go through legal evidence better than a human paralawyer. It's used to fly the plane that you came here with. Human pilots only flew it seven to eight minutes, the rest of the time the AI was driving. And of course, in Netflix and Amazon, it's in the background, making those recommendations. That's what we have today.
当然，它已经发生了。 我们已经有了 AI， 它们通常都隐身在后台工作， 在医院里， AI 分析 X 光片的水准 比人类医生还要棒。 在律所里， AI 核查证物的本事 比人类助理律师还要强。 我们乘坐的飞机是由 AI 在驾驶。 人类驾驶员只飞个七、八分钟而已； 其他时间都是 AI 在操控。 当然，在 Netflix 和亚马逊网站， 是AI在后台进行推荐。 这些都是我们已经实现的。
And we have an example, of course, in a more front-facing aspect of it, with the win of the AlphaGo, who beat the world's greatest Go champion. But it's more than that. If you play a video game, you're playing against an AI. But recently, Google taught their AI to actually learn how to play video games. Again, teaching video games was already done, but learning how to play a video game is another step. That's artificial smartness. What we're doing is taking this artificial smartness and we're making it smarter and smarter.
我们也有一些更前沿的例子， 比如“阿尔法狗”战胜了 人类最强的围棋世界冠军。 但还不止于此。 我们打电玩时，对手往往是 AI。 不过最近，谷歌教会了他们的 AI 自己学习如何打电子游戏。 教（AI）打游戏 已经不是什么新鲜事了， 但（AI）自己学习 打游戏则是另一个境界。 这就是人工智慧。 我们正在以此为起点， 让它变得越来越聪明。
There are three aspects to this general trend that I think are underappreciated; I think we would understand AI a lot better if we understood these three things. I think these things also would help us embrace AI, because it's only by embracing it that we actually can steer it. We can actually steer the specifics by embracing the larger trend.
在这个大趋势中， 我认为有三点尚未被充分认识； 如果我们能理解这三点， 就能更好的理解 AI， 并更加全身心的拥抱 AI。 只有拥抱 AI，才能控制AI。 我们可以通过拥抱 大趋势来控制细节。
So let me talk about those three different aspects. The first one is: our own intelligence has a very poor understanding of what intelligence is. We tend to think of intelligence as a single dimension, that it's kind of like a note that gets louder and louder. It starts like with IQ measurement. It starts with maybe a simple low IQ in a rat or mouse, and maybe there's more in a chimpanzee, and then maybe there's more in a stupid person, and then maybe an average person like myself, and then maybe a genius. And this single IQ intelligence is getting greater and greater. That's completely wrong. That's not what intelligence is -- not what human intelligence is, anyway. It's much more like a symphony of different notes, and each of these notes is played on a different instrument of cognition.
所以，请允许我谈谈这三点。 第一点，我们自己尚未很好的理解 什么是智能。 我们通常认为智能是单维度的， 就像一个越来越响的音符。 我们用智商来衡量它。 老鼠的智商较低， 猩猩的智商较高， 接下来是比较笨的人， 然后是像我一样的普通人， 再往上是天才。 智商越高，智能就越高。 这种看法是完全错误的。 这根本就不是智能， 人类智能也并非如此。 智能更像由不同音符 组成的交响乐， 每个音符由不同的认知乐器来奏响。
There are many types of intelligences in our own minds. We have deductive reasoning, we have emotional intelligence, we have spatial intelligence; we have maybe 100 different types that are all grouped together, and they vary in different strengths with different people. And of course, if we go to animals, they also have another basket -- another symphony of different kinds of intelligences, and sometimes those same instruments are the same that we have. They can think in the same way, but they may have a different arrangement, and maybe they're higher in some cases than humans, like long-term memory in a squirrel is actually phenomenal, so it can remember where it buried its nuts. But in other cases they may be lower.
人类的心智包含了多种智能。 我们可以进行演绎推理， 我们具备情绪智力， 我们有空间智能。 我们可能有一百种 不同的智能集合在一起， 它们在不同人的身上也 体现得强弱不一。 而动物们则可能是另一套体系—— 由其他智能组成的另一首交响乐， 当然，有些乐器与人类是相同的。 可能思考的方式相同但侧重点不同， 某些方面可能还强于人类， 像松鼠的长期记忆就很了不得， 能清楚记得坚果的埋藏之所。 但在另外一些方面可能不如人类。
When we go to make machines, we're going to engineer them in the same way, where we'll make some of those types of smartness much greater than ours, and many of them won't be anywhere near ours, because they're not needed. So we're going to take these things, these artificial clusters, and we'll be adding more varieties of artificial cognition to our AIs. We're going to make them very, very specific.
当我们制造机器时， 也会用同样的方式来设计它们， 它们在某些方面会比我们聪明得多， 而在其他方面则远远不如我们， 因为根本没必要。 我们会用这些东西， 这些人造的功能组合， 为我们的 AI 添加 各种各样的人工认知。 我们会让它们（的功能）非常具体。
So your calculator is smarter than you are in arithmetic already; your GPS is smarter than you are in spatial navigation; Google, Bing, are smarter than you are in long-term memory. And we're going to take, again, these kinds of different types of thinking and we'll put them into, like, a car. The reason why we want to put them in a car so the car drives, is because it's not driving like a human. It's not thinking like us. That's the whole feature of it. It's not being distracted, it's not worrying about whether it left the stove on, or whether it should have majored in finance. It's just driving.
比方说，计算器在数学运算上 要比我们聪明得多； GPS 的空间导航能力远胜过我们； 谷歌、必应在长期记忆上完胜我们。 然后我们再把这些不同类型的智能 塞到……比如说汽车里， 实现自动行驶。 我们之所以这么做， 正是因为它的驾驶方式 跟我们不一样。 它不像我们那样思考。 这恰恰是它的特点。 它不会分心， 不会担心是否忘记了关炉子， 不会纠结要不要选金融专业。 它只知道开车。
Just driving, OK? And we actually might even come to advertise these as "consciousness-free." They're without consciousness, they're not concerned about those things, they're not distracted.
它会专心开车，对吧？ 我们甚至可以把这个做为卖点， 叫做“无意识”。 它们没有意识， 不会东想西想， 不会分心。
So in general, what we're trying to do is make as many different types of thinking as we can. We're going to populate the space of all the different possible types, or species, of thinking. And there actually may be some problems that are so difficult in business and science that our own type of human thinking may not be able to solve them alone. We may need a two-step program, which is to invent new kinds of thinking that we can work alongside of to solve these really large problems, say, like dark energy or quantum gravity.
所以，我们应该尽我们所能制造各种各样的思考（机器）。 我们应该去尝试 所有可能的思考方式。 在商业和科学上， 我们会遇到一些难题， 单凭人类自身的思考无法解决。 我们可能需要分两步走， 先发明出新的思考方式， 再与它们一起解决这些真正的难题， 比如暗能量和量子引力。
What we're doing is making alien intelligences. You might even think of this as, sort of, artificial aliens in some senses. And they're going to help us think different, because thinking different is the engine of creation and wealth and new economy.
我们实际上是在创造异形智能。 某种意义上，甚至可以将它们看作 人造异形。 它们将帮助我们用不同的方式思考， 而换一种思考方式是创造的源泉， 是财富和新经济的引擎。
The second aspect of this is that we are going to use AI to basically make a second Industrial Revolution. The first Industrial Revolution was based on the fact that we invented something I would call artificial power. Previous to that, during the Agricultural Revolution, everything that was made had to be made with human muscle or animal power. That was the only way to get anything done. The great innovation during the Industrial Revolution was, we harnessed steam power, fossil fuels, to make this artificial power that we could use to do anything we wanted to do. So today when you drive down the highway, you are, with a flick of the switch, commanding 250 horses -- 250 horsepower -- which we can use to build skyscrapers, to build cities, to build roads, to make factories that would churn out lines of chairs or refrigerators way beyond our own power. And that artificial power can also be distributed on wires on a grid to every home, factory, farmstead, and anybody could buy that artificial power, just by plugging something in.
第二点是，我们将用 AI 推动第二次工业革命。 在第一次工业革命中， 人类发明了我称之为 “人造能源”的东西。 在此之前， 在农业革命时期， 制造业靠人力驱动， 或者靠畜力。 除此之外别无他法。 工业革命时期的伟大发明就是 人们利用化石燃料和蒸汽 所产生的“人造能源”来做 我们想做的任何事情。 今天，当我们开车行驶在高速上， 只需轻轻拨弄开关， 就能驾驭 250 匹马—— 或者说，250 匹马的马力—— 我们可以建造高楼大厦， 修建道路，建设城市， 开办工厂，源源不断地 生产桌椅或冰箱， 这些都远远超出了人力所为。 这种“人造能源” 还可以通过电网和电线 输送到家庭、工厂和农庄， 任何人都可以 购买这种“人造能源”， 只需插上插头就可以使用。
So this was a source of innovation as well, because a farmer could take a manual hand pump, and they could add this artificial power, this electricity, and he'd have an electric pump. And you multiply that by thousands or tens of thousands of times, and that formula was what brought us the Industrial Revolution. All the things that we see, all this progress that we now enjoy, has come from the fact that we've done that.
它也带来了很多创新， 农民可以为手动泵通上电， 加上这种“人造能源”， 就变成了电泵。 类似的改造成千上万， 这个（人力器械+人造能源的） 公式造就了工业革命。 今天我们看到的所有事物， 享受的所有服务， 几乎都来源于此。
We're going to do the same thing now with AI. We're going to distribute that on a grid, and now you can take that electric pump. You can add some artificial intelligence, and now you have a smart pump. And that, multiplied by a million times, is going to be this second Industrial Revolution. So now the car is going down the highway, it's 250 horsepower, but in addition, it's 250 minds. That's the auto-driven car. It's like a new commodity; it's a new utility. The AI is going to flow across the grid -- the cloud -- in the same way electricity did.
现在我们要用 AI 做同样的事情。 我们用网路传输 AI， 把 AI 加载到 诸如电泵之类的东西上， 就得到了聪明的电泵。 类似的改造做上几百万次， 就会掀起第二次工业革命。 那么将来汽车行驶在高速上， 它不仅有 250倍马力， 还有 250倍的脑力。 这就是自动驾驶汽车。 它是一种新的商品， 是一种新的基础设施。 AI 将会在网络、在云端传输， 就像电一样。
So everything that we had electrified, we're now going to cognify. And I would suggest, then, that the formula for the next 10,000 start-ups is very, very simple, which is to take x and add AI. That is the formula, that's what we're going to be doing. And that is the way in which we're going to make this second Industrial Revolution. And by the way -- right now, this minute, you can log on to Google and you can purchase AI for six cents, 100 hits. That's available right now.
所以凡是可以用电的地方， 都可以用 AI。 而我可以建议说， 未来一万家创业公司的秘诀 其实非常非常简单： 拿来某样东西，加上 AI。 这个公式就是我们将要不断践行的。 我们将以这种方式 来掀起第二次工业革命。 顺便说一句，就在此时， 你可以登录谷歌， 购买 AI：用6美分 购买100次服务。 这个服务现在就能用。
So the third aspect of this is that when we take this AI and embody it, we get robots. And robots are going to be bots, they're going to be doing many of the tasks that we have already done. A job is just a bunch of tasks, so they're going to redefine our jobs because they're going to do some of those tasks. But they're also going to create whole new categories, a whole new slew of tasks that we didn't know we wanted to do before. They're going to actually engender new kinds of jobs, new kinds of tasks that we want done, just as automation made up a whole bunch of new things that we didn't know we needed before, and now we can't live without them. So they're going to produce even more jobs than they take away, but it's important that a lot of the tasks that we're going to give them are tasks that can be defined in terms of efficiency or productivity. If you can specify a task, either manual or conceptual, that can be specified in terms of efficiency or productivity, that goes to the bots. Productivity is for robots. What we're really good at is basically wasting time.
第三点是， 我们将AI实体化， 就得到了机器人。 机器人可以帮助我们， 完成许多曾经需要 我们亲力亲为的任务。 而工作就是一系列的任务， 我们的工作将会被重新定义， 一部分任务将交给机器人来完成。 与此同时，也将产生一大批 不同种类的新任务， 一批以往我们没有意识到 要去做的任务。 它们甚至有可能催生出新的职业， 我们感兴趣的新工作， 就像自动化带来的许多新事物， 我们之前并不知道会需要它们， 但今天我们已经离不开它们了。 所以机器人带来的 工作机会比它们抢走的要多。 更重要的是，我们交给它们的 都是需要效率或生产率的任务。 如果一个任务， 不管是体力的还是脑力的， 可以用效率或生产率来衡量， 那么就应该交给机器人来完成。 需要效率的事情交给机器人好了。 我们真正擅长的是浪费时间。
We're really good at things that are inefficient. Science is inherently inefficient. It runs on that fact that you have one failure after another. It runs on the fact that you make tests and experiments that don't work, otherwise you're not learning. It runs on the fact that there is not a lot of efficiency in it. Innovation by definition is inefficient, because you make prototypes, because you try stuff that fails, that doesn't work. Exploration is inherently inefficiency. Art is not efficient. Human relationships are not efficient. These are all the kinds of things we're going to gravitate to, because they're not efficient. Efficiency is for robots. We're also going to learn that we're going to work with these AIs because they think differently than us.
我们最擅长做那些没有效率的事情。 科学从本质上来说是低效的。 我们一次又一次的失败， 很多试验和尝试都徒劳无功， 否则我们也学不到什么东西。 事实就是， 科学研究没有什么效率。 创新从定义上来说就是低效的。 毕竟我们需要制作原型， 需要做各种尝试，经历各种失败。 探索是低效的。 艺术是低效的。 人际关系也是低效的。 这些都是我们喜欢做的事情， 因为它们都是低效的。 高效是机器人的使命。 还要认识到，我们将和 AI 一起工作， 因为它们的思维方式与我们不同。
When Deep Blue beat the world's best chess champion, people thought it was the end of chess. But actually, it turns out that today, the best chess champion in the world is not an AI. And it's not a human. It's the team of a human and an AI. The best medical diagnostician is not a doctor, it's not an AI, it's the team. We're going to be working with these AIs, and I think you'll be paid in the future by how well you work with these bots. So that's the third thing, is that they're different, they're utility and they are going to be something we work with rather than against. We're working with these rather than against them.
在“深蓝”战胜国际象棋的世界冠军后， 人们以为国际象棋没什么玩头了。 但事实上，目前世界上 最厉害的国际象棋冠军 并不是 AI， 也不是人类， 而是由人类和 AI 组成的团队。 最棒的医学诊疗师 既不是医生，也不是 AI， 而是他们组成的团队。 也就是说我们将和 AI 一起工作， 你将来的薪酬， 很可能取决于 你跟机器人合作得如何。 这就是我想说的第三点： AI 是不同于我们的， 它们是技术设备， 我们将与它们合作， 而非竞争。
So, the future: Where does that take us? I think that 25 years from now, they'll look back and look at our understanding of AI and say, "You didn't have AI. In fact, you didn't even have the Internet yet, compared to what we're going to have 25 years from now." There are no AI experts right now. There's a lot of money going to it, there are billions of dollars being spent on it; it's a huge business, but there are no experts, compared to what we'll know 20 years from now. So we are just at the beginning of the beginning, we're in the first hour of all this. We're in the first hour of the Internet. We're in the first hour of what's coming. The most popular AI product in 20 years from now, that everybody uses, has not been invented yet. That means that you're not late.
那么， 未来会如何？ 我想，25年后我们回头再看 今天对 AI 的理解，我们会说： “你们那都不叫 AI。 你们甚至都还没有真正的因特网， 25年后的因特网才能叫因特网呢。“ 我们也还没有真正的 AI 专家。 而大量的资本正涌向这个领域， 动辄数十亿美金， 这是一个巨大的产业。 但我们尚未拥有真正的 AI 专家—— 如果跟20年后相比的话。 我们还处在最初的起步阶段， 所有一切才刚刚开始。 因特网的历史才刚刚开始。 美好的未来才刚刚开始。 未来20年最受欢迎的 AI 产品， 最普及的 AI 产品， 还没有被发明呢。 也就是说，你们还有机会。
Thank you.
谢谢。
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