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"The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable," says digital visionary Kevin Kelly -- and technology is much the same, driven by patterns that are surprising but inevitable. Over the next 20 years, he says, our penchant for making things smarter and smarter will have a profound impact on nearly everything we do. Kelly explores three trends in AI we need to understand in order to embrace it and steer its development. "The most popular AI product 20 years from now that everyone uses has not been invented yet," Kelly says. "That means that you're not late."

00:15

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.

00:54

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 systemsgive 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.

01:33

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.

02:00

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.

02:34

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.

03:18

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.

03:39

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.

04:27

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.

05:10

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.

05:38

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.

06:17

(Laughter)

06:18

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.

06:30

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.

07:08

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.

07:25

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.

08:40

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.

09:02

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.

09:34

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.

10:06

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.

11:16

(Laughter)

11:17

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.

12:02

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.

12:43

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.

13:35

Thank you.

13:36

(Laughter)

13:37

(Applause)

00:15

我打算談一談技術的發展趨勢。 當（新的）技術到來時， 常常會令我們感到驚訝。 但事實上，技術在很大程度上 是能夠被預見的。 這是因為所有的技術 都有某種傾向性， 有某種沖動， 有某種趨勢。 這些趨勢是由電線、開關、以及電子的 物理和化學本質所決定的， 并且呈現出不斷重復的模式。 或者說，這些模式形成了 某種趨勢、某種傾向。

00:54

你可以把它看成類似于重力的東西。 想象雨點匯入山谷： 一滴雨點流入山谷的實際路徑 是無法預測的。 我們并不知道它的具體走向， 但大方向是很顯然的： 它往下流。 因此，這些內在趨勢和沖動， 深深扎根于技術系統中， 使我們能夠感知它們的大體方向。 具體點說， 電話是必然的， 但 iPhone 不是； 因特網是必然的， 但推特不是。

01:33

同樣道理， 當下有許多正在發生的趨勢， 而我認為其中最重要的一個 是讓物體變得越來越聰明。 我稱之為“知化”， 也就是人們常說的 人工智能，或者 AI。 我認為在未來二十年中， 這將是社會中最具影響力的 發展趨勢和驅動力。

02:00

當然，它已經發生了。 我們已經有了 AI， 它們通常都隱身在后臺工作， 在醫院里， AI 分析 X 光片的水準 比人類醫生還要棒。 在律所里， AI 核查證物的本事 比人類助理律師還要強。 我們乘坐的飛機是由 AI 在駕駛。 人類駕駛員只飛個七、八分鐘而已； 其他時間都是 AI 在操控。 當然，在 Netflix 和亞馬遜網站， 是AI在后臺進行推薦。 這些都是我們已經實現的。

02:34

我們也有一些更前沿的例子， 比如“阿爾法狗”戰勝了 人類最強的圍棋世界冠軍。 但還不止于此。 我們打電玩時，對手往往是 AI。 不過最近，谷歌教會了他們的 AI 自己學習如何打電子游戲。 教（AI）打游戲 已經不是什么新鮮事了， 但（AI）自己學習 打游戲則是另一個境界。 這就是人工智慧。 我們正在以此為起點， 讓它變得越來越聰明。

03:18

在這個大趨勢中， 我認為有三點尚未被充分認識； 如果我們能理解這三點， 就能更好的理解 AI， 并更加全身心的擁抱 AI。 只有擁抱 AI，才能控制AI。 我們可以通過擁抱 大趨勢來控制細節。

03:39

所以，請允許我談談這三點。 第一點，我們自己尚未很好的理解 什么是智能。 我們通常認為智能是單維度的，就像一個越來越響的音符。 我們用智商來衡量它。 老鼠的智商較低， 猩猩的智商較高， 接下來是比較笨的人，然后是像我一樣的普通人， 再往上是天才。 智商越高，智能就越高。 這種看法是完全錯誤的。 這根本就不是智能， 人類智能也并非如此。 智能更像由不同音符 組成的交響樂， 每個音符由不同的認知樂器來奏響。

04:27

人類的心智包含了多種智能。 我們可以進行演繹推理， 我們具備情緒智力， 我們有空間智能。 我們可能有一百種 不同的智能集合在一起， 它們在不同人的身上也 體現得強弱不一。 而動物們則可能是另一套體系—— 由其他智能組成的另一首交響樂， 當然，有些樂器與人類是相同的。 可能思考的方式相同但側重點不同， 某些方面可能還強于人類， 像松鼠的長期記憶就很了不得， 能清楚記得堅果的埋藏之所。 但在另外一些方面可能不如人類。

05:10

當我們制造機器時， 也會用同樣的方式來設計它們， 它們在某些方面會比我們聰明得多， 而在其他方面則遠遠不如我們， 因為根本沒必要。 我們會用這些東西， 這些人造的功能組合， 為我們的 AI 添加 各種各樣的人工認知。 我們會讓它們（的功能）非常具體。

05:38

比方說，計算器在數學運算上 要比我們聰明得多； GPS 的空間導航能力遠勝過我們； 谷歌、必應在長期記憶上完勝我們。 然后我們再把這些不同類型的智能 塞到……比如說汽車里， 實現自動行駛。 我們之所以這么做， 正是因為它的駕駛方式 跟我們不一樣。 它不像我們那樣思考。 這恰恰是它的特點。 它不會分心， 不會擔心是否忘記了關爐子， 不會糾結要不要選金融專業。 它只知道開車。

06:17

（笑聲）

06:18

它會專心開車，對吧？ 我們甚至可以把這個做為賣點， 叫做“無意識”。 它們沒有意識， 不會東想西想， 不會分心。

06:29

所以，我們應該盡我們所能 制造各種各樣的思考（機器）。 我們應該去嘗試 所有可能的思考方式。 在商業和科學上， 我們會遇到一些難題， 單憑人類自身的思考無法解決。 我們可能需要分兩步走， 先發明出新的思考方式， 再與它們一起解決這些真正的難題， 比如暗能量和量子引力。

07:08

我們實際上是在創造異形智能。 某種意義上，甚至可以將它們看作 人造異形。 它們將幫助我們用不同的方式思考， 而換一種思考方式是創造的源泉， 是財富和新經濟的引擎。

07:25

第二點是，我們將用 AI 推動第二次工業革命。 在第一次工業革命中， 人類發明了我稱之為 “人造能源”的東西。在此之前， 在農業革命時期， 制造業靠人力驅動， 或者靠畜力。 除此之外別無他法。 工業革命時期的偉大發明就是 人們利用化石燃料和蒸汽 所產生的“人造能源”來做 我們想做的任何事情。 今天，當我們開車行駛在高速上， 只需輕輕撥弄開關， 就能駕馭 250 匹馬—— 或者說，250 匹馬的馬力—— 我們可以建造高樓大廈， 修建道路，建設城市， 開辦工廠，源源不斷地 生產桌椅或冰箱， 這些都遠遠超出了人力所為。 這種“人造能源” 還可以通過電網和電線 輸送到家庭、工廠和農莊， 任何人都可以 購買這種“人造能源”， 只需插上插頭就可以使用。

08:40

它也帶來了很多創新， 農民可以為手動泵通上電， 加上這種“人造能源”， 就變成了電泵。 類似的改造成千上萬， 這個（人力器械+人造能源的） 公式造就了工業革命。 今天我們看到的所有事物， 享受的所有服務， 幾乎都來源于此。

09:02

現在我們要用 AI 做同樣的事情。 我們用網路傳輸 AI， 把 AI 加載到 諸如電泵之類的東西上， 就得到了聰明的電泵。 類似的改造做上幾百萬次， 就會掀起第二次工業革命。 那么將來汽車行駛在高速上， 它不僅有 250倍馬力， 還有 250倍的腦力。 這就是自動駕駛汽車。 它是一種新的商品， 是一種新的基礎設施。 AI 將會在網絡、在云端傳輸， 就像電一樣。

09:34

所以凡是可以用電的地方， 都可以用 AI。 而我可以建議說， 未來一萬家創業公司的秘訣 其實非常非常簡單：拿來某樣東西，加上 AI。 這個公式就是我們將要不斷踐行的。 我們將以這種方式 來掀起第二次工業革命。 順便說一句，就在此時， 你可以登錄谷歌， 購買 AI：用6美分 購買100次服務。 這個服務現在就能用。

10:06

第三點是， 我們將AI實體化， 就得到了機器人。 機器人可以幫助我們， 完成許多曾經需要 我們親力親為的任務。 而工作就是一系列的任務， 我們的工作將會被重新定義， 一部分任務將交給機器人來完成。 與此同時，也將產生一大批 不同種類的新任務， 一批以往我們沒有意識到 要去做的任務。 它們甚至有可能催生出新的職業，我們感興趣的新工作， 就像自動化帶來的許多新事物， 我們之前并不知道會需要它們， 但今天我們已經離不開它們了。 所以機器人帶來的 工作機會比它們搶走的要多。 更重要的是，我們交給它們的 都是需要效率或生產率的任務。 如果一個任務， 不管是體力的還是腦力的， 可以用效率或生產率來衡量， 那么就應該交給機器人來完成。 需要效率的事情交給機器人好了。 我們真正擅長的是浪費時間。

11:16

（笑聲）

11:16

我們最擅長做那些沒有效率的事情。 科學從本質上來說是低效的。 我們一次又一次的失敗， 很多試驗和嘗試都徒勞無功， 否則我們也學不到什么東西。 事實就是， 科學研究沒有什么效率。 創新從定義上來說就是低效的。畢竟我們需要制作原型， 需要做各種嘗試，經歷各種失敗。 探索是低效的。 藝術是低效的。 人際關系也是低效的。 這些都是我們喜歡做的事情， 因為它們都是低效的。 高效是機器人的使命。 還要認識到，我們將和 AI 一起工作， 因為它們的思維方式與我們不同。

12:02

在“深藍”戰勝國際象棋的世界冠軍后， 人們以為國際象棋沒什么玩頭了。 但事實上，目前世界上 最厲害的國際象棋冠軍 并不是 AI， 也不是人類， 而是由人類和 AI 組成的團隊。 最棒的醫學診療師 既不是醫生，也不是 AI，而是他們組成的團隊。 也就是說我們將和 AI 一起工作， 你將來的薪酬， 很可能取決于 你跟機器人合作得如何。 這就是我想說的第三點： AI 是不同于我們的， 它們是技術設備， 我們將與它們合作， 而非競爭。

12:43

那么， 未來會如何？ 我想，25年后我們回頭再看 今天對 AI 的理解，我們會說： “你們那都不叫 AI。 你們甚至都還沒有真正的因特網， 25年后的因特網才能叫因特網呢。“ 我們也還沒有真正的 AI 專家。 而大量的資本正涌向這個領域， 動輒數十億美金， 這是一個巨大的產業。 但我們尚未擁有真正的 AI 專家—— 如果跟20年后相比的話。 我們還處在最初的起步階段， 所有一切才剛剛開始。 因特網的歷史才剛剛開始。 美好的未來才剛剛開始。 未來20年最受歡迎的 AI 產品， 最普及的 AI 產品， 還沒有被發明呢。 也就是說，你們還有機會。

13:35

謝謝！

13:36

（笑聲）

13:37

（掌聲）