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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(shù)的發(fā)展趨勢(shì)蚣常。 當(dāng)（新的）技術(shù)到來(lái)時(shí)， 常常會(huì)令我們感到驚訝痊银。 但事實(shí)上史隆，技術(shù)在很大程度上 是能夠被預(yù)見(jiàn)的。 這是因?yàn)樗械募夹g(shù) 都有某種傾向性曼验， 有某種沖動(dòng)泌射， 有某種趨勢(shì)。 這些趨勢(shì)是由電線鬓照、開(kāi)關(guān)熔酷、以及電子的 物理和化學(xué)本質(zhì)所決定的， 并且呈現(xiàn)出不斷重復(fù)的模式豺裆。 或者說(shuō)拒秘，這些模式形成了 某種趨勢(shì)、某種傾向臭猜。

00:54

你可以把它看成類(lèi)似于重力的東西躺酒。 想象雨點(diǎn)匯入山谷： 一滴雨點(diǎn)流入山谷的實(shí)際路徑 是無(wú)法預(yù)測(cè)的。 我們并不知道它的具體走向蔑歌， 但大方向是很顯然的： 它往下流羹应。 因此，這些內(nèi)在趨勢(shì)和沖動(dòng)次屠， 深深扎根于技術(shù)系統(tǒng)中园匹， 使我們能夠感知它們的大體方向。 具體點(diǎn)說(shuō)劫灶， 電話是必然的裸违， 但 iPhone 不是； 因特網(wǎng)是必然的本昏， 但推特不是供汛。

01:33

同樣道理， 當(dāng)下有許多正在發(fā)生的趨勢(shì)涌穆， 而我認(rèn)為其中最重要的一個(gè) 是讓物體變得越來(lái)越聰明怔昨。 我稱之為“知化”， 也就是人們常說(shuō)的 人工智能蒲犬，或者 AI朱监。 我認(rèn)為在未來(lái)二十年中岸啡， 這將是社會(huì)中最具影響力的 發(fā)展趨勢(shì)和驅(qū)動(dòng)力原叮。

02:00

當(dāng)然，它已經(jīng)發(fā)生了。 我們已經(jīng)有了 AI奋隶， 它們通常都隱身在后臺(tái)工作擂送， 在醫(yī)院里， AI 分析 X 光片的水準(zhǔn) 比人類(lèi)醫(yī)生還要棒唯欣。 在律所里嘹吨， AI 核查證物的本事 比人類(lèi)助理律師還要強(qiáng)。 我們乘坐的飛機(jī)是由 AI 在駕駛境氢。 人類(lèi)駕駛員只飛個(gè)七蟀拷、八分鐘而已； 其他時(shí)間都是 AI 在操控萍聊。 當(dāng)然问芬，在 Netflix 和亞馬遜網(wǎng)站， 是AI在后臺(tái)進(jìn)行推薦寿桨。 這些都是我們已經(jīng)實(shí)現(xiàn)的此衅。

02:34

我們也有一些更前沿的例子， 比如“阿爾法狗”戰(zhàn)勝了 人類(lèi)最強(qiáng)的圍棋世界冠軍亭螟。 但還不止于此挡鞍。 我們打電玩時(shí)，對(duì)手往往是 AI预烙。 不過(guò)最近墨微，谷歌教會(huì)了他們的 AI 自己學(xué)習(xí)如何打電子游戲。 教（AI）打游戲 已經(jīng)不是什么新鮮事了扁掸， 但（AI）自己學(xué)習(xí) 打游戲則是另一個(gè)境界欢嘿。 這就是人工智慧。 我們正在以此為起點(diǎn)也糊， 讓它變得越來(lái)越聰明炼蹦。

03:18

在這個(gè)大趨勢(shì)中， 我認(rèn)為有三點(diǎn)尚未被充分認(rèn)識(shí)狸剃； 如果我們能理解這三點(diǎn)掐隐， 就能更好的理解 AI， 并更加全身心的擁抱 AI钞馁。 只有擁抱 AI虑省，才能控制AI。 我們可以通過(guò)擁抱 大趨勢(shì)來(lái)控制細(xì)節(jié)僧凰。

03:39

所以探颈，請(qǐng)?jiān)试S我談?wù)勥@三點(diǎn)。 第一點(diǎn)训措，我們自己尚未很好的理解 什么是智能伪节。 我們通常認(rèn)為智能是單維度的光羞，就像一個(gè)越來(lái)越響的音符。 我們用智商來(lái)衡量它怀大。 老鼠的智商較低纱兑， 猩猩的智商較高， 接下來(lái)是比較笨的人化借，然后是像我一樣的普通人潜慎， 再往上是天才。 智商越高蓖康，智能就越高铐炫。 這種看法是完全錯(cuò)誤的。 這根本就不是智能蒜焊， 人類(lèi)智能也并非如此驳遵。 智能更像由不同音符 組成的交響樂(lè)， 每個(gè)音符由不同的認(rèn)知樂(lè)器來(lái)奏響山涡。

04:27

人類(lèi)的心智包含了多種智能堤结。 我們可以進(jìn)行演繹推理， 我們具備情緒智力鸭丛， 我們有空間智能竞穷。 我們可能有一百種 不同的智能集合在一起， 它們?cè)诓煌说纳砩弦?體現(xiàn)得強(qiáng)弱不一鳞溉。 而動(dòng)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(lè)瘾带， 當(dāng)然，有些樂(lè)器與人類(lèi)是相同的熟菲。 可能思考的方式相同但側(cè)重點(diǎn)不同看政， 某些方面可能還強(qiáng)于人類(lèi)， 像松鼠的長(zhǎng)期記憶就很了不得抄罕， 能清楚記得堅(jiān)果的埋藏之所允蚣。 但在另外一些方面可能不如人類(lèi)。

05:10

當(dāng)我們制造機(jī)器時(shí)呆贿， 也會(huì)用同樣的方式來(lái)設(shè)計(jì)它們嚷兔， 它們?cè)谀承┓矫鏁?huì)比我們聰明得多， 而在其他方面則遠(yuǎn)遠(yuǎn)不如我們做入， 因?yàn)楦緵](méi)必要冒晰。 我們會(huì)用這些東西， 這些人造的功能組合竟块， 為我們的 AI 添加 各種各樣的人工認(rèn)知壶运。 我們會(huì)讓它們（的功能）非常具體。

05:38

比方說(shuō)浪秘，計(jì)算器在數(shù)學(xué)運(yùn)算上 要比我們聰明得多蒋情； GPS 的空間導(dǎo)航能力遠(yuǎn)勝過(guò)我們埠况； 谷歌、必應(yīng)在長(zhǎng)期記憶上完勝我們恕出。 然后我們?cè)侔堰@些不同類(lèi)型的智能 塞到……比如說(shuō)汽車(chē)?yán)铮?實(shí)現(xiàn)自動(dòng)行駛。 我們之所以這么做违帆， 正是因?yàn)樗鸟{駛方式 跟我們不一樣浙巫。 它不像我們那樣思考。 這恰恰是它的特點(diǎn)刷后。 它不會(huì)分心的畴， 不會(huì)擔(dān)心是否忘記了關(guān)爐子， 不會(huì)糾結(jié)要不要選金融專業(yè)尝胆。 它只知道開(kāi)車(chē)丧裁。

06:17

（笑聲）

06:18

它會(huì)專心開(kāi)車(chē)，對(duì)吧含衔？ 我們甚至可以把這個(gè)做為賣(mài)點(diǎn)煎娇， 叫做“無(wú)意識(shí)”。 它們沒(méi)有意識(shí)贪染， 不會(huì)東想西想缓呛， 不會(huì)分心。

06:29

所以杭隙，我們應(yīng)該盡我們所能 制造各種各樣的思考（機(jī)器）哟绊。 我們應(yīng)該去嘗試 所有可能的思考方式。 在商業(yè)和科學(xué)上痰憎， 我們會(huì)遇到一些難題票髓， 單憑人類(lèi)自身的思考無(wú)法解決。 我們可能需要分兩步走铣耘， 先發(fā)明出新的思考方式洽沟， 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蜗细。

07:08

我們實(shí)際上是在創(chuàng)造異形智能玲躯。 某種意義上，甚至可以將它們看作 人造異形鳄乏。 它們將幫助我們用不同的方式思考跷车， 而換一種思考方式是創(chuàng)造的源泉， 是財(cái)富和新經(jīng)濟(jì)的引擎橱野。

07:25

第二點(diǎn)是朽缴，我們將用 AI 推動(dòng)第二次工業(yè)革命。 在第一次工業(yè)革命中水援， 人類(lèi)發(fā)明了我稱之為 “人造能源”的東西密强。在此之前茅郎， 在農(nóng)業(yè)革命時(shí)期， 制造業(yè)靠人力驅(qū)動(dòng)或渤， 或者靠畜力系冗。 除此之外別無(wú)他法。 工業(yè)革命時(shí)期的偉大發(fā)明就是 人們利用化石燃料和蒸汽 所產(chǎn)生的“人造能源”來(lái)做 我們想做的任何事情薪鹦。 今天掌敬，當(dāng)我們開(kāi)車(chē)行駛在高速上， 只需輕輕撥弄開(kāi)關(guān)池磁， 就能駕馭 250 匹馬—— 或者說(shuō)奔害，250 匹馬的馬力—— 我們可以建造高樓大廈， 修建道路地熄，建設(shè)城市华临， 開(kāi)辦工廠，源源不斷地 生產(chǎn)桌椅或冰箱端考， 這些都遠(yuǎn)遠(yuǎn)超出了人力所為雅潭。 這種“人造能源” 還可以通過(guò)電網(wǎng)和電線 輸送到家庭、工廠和農(nóng)莊却特， 任何人都可以 購(gòu)買(mǎi)這種“人造能源”寻馏， 只需插上插頭就可以使用。

08:40

它也帶來(lái)了很多創(chuàng)新核偿， 農(nóng)民可以為手動(dòng)泵通上電诚欠， 加上這種“人造能源”， 就變成了電泵漾岳。 類(lèi)似的改造成千上萬(wàn)轰绵， 這個(gè)（人力器械+人造能源的） 公式造就了工業(yè)革命。 今天我們看到的所有事物尼荆， 享受的所有服務(wù)左腔， 幾乎都來(lái)源于此。

09:02

現(xiàn)在我們要用 AI 做同樣的事情捅儒。 我們用網(wǎng)路傳輸 AI液样， 把 AI 加載到 諸如電泵之類(lèi)的東西上， 就得到了聰明的電泵巧还。 類(lèi)似的改造做上幾百萬(wàn)次鞭莽， 就會(huì)掀起第二次工業(yè)革命。 那么將來(lái)汽車(chē)行駛在高速上麸祷， 它不僅有 250倍馬力澎怒， 還有 250倍的腦力。 這就是自動(dòng)駕駛汽車(chē)阶牍。 它是一種新的商品喷面， 是一種新的基礎(chǔ)設(shè)施星瘾。 AI 將會(huì)在網(wǎng)絡(luò)、在云端傳輸惧辈， 就像電一樣琳状。

09:34

所以凡是可以用電的地方， 都可以用 AI盒齿。 而我可以建議說(shuō)念逞， 未來(lái)一萬(wàn)家創(chuàng)業(yè)公司的秘訣 其實(shí)非常非常簡(jiǎn)單：拿來(lái)某樣?xùn)|西，加上 AI县昂。 這個(gè)公式就是我們將要不斷踐行的肮柜。 我們將以這種方式 來(lái)掀起第二次工業(yè)革命陷舅。 順便說(shuō)一句倒彰，就在此時(shí)， 你可以登錄谷歌莱睁， 購(gòu)買(mǎi) AI：用6美分 購(gòu)買(mǎi)100次服務(wù)待讳。 這個(gè)服務(wù)現(xiàn)在就能用。

10:06

第三點(diǎn)是仰剿， 我們將AI實(shí)體化创淡， 就得到了機(jī)器人。 機(jī)器人可以幫助我們南吮， 完成許多曾經(jīng)需要 我們親力親為的任務(wù)琳彩。 而工作就是一系列的任務(wù)， 我們的工作將會(huì)被重新定義部凑， 一部分任務(wù)將交給機(jī)器人來(lái)完成露乏。 與此同時(shí)，也將產(chǎn)生一大批 不同種類(lèi)的新任務(wù)涂邀， 一批以往我們沒(méi)有意識(shí)到 要去做的任務(wù)瘟仿。 它們甚至有可能催生出新的職業(yè)，我們感興趣的新工作比勉， 就像自動(dòng)化帶來(lái)的許多新事物劳较， 我們之前并不知道會(huì)需要它們， 但今天我們已經(jīng)離不開(kāi)它們了浩聋。 所以機(jī)器人帶來(lái)的 工作機(jī)會(huì)比它們搶走的要多观蜗。 更重要的是，我們交給它們的 都是需要效率或生產(chǎn)率的任務(wù)衣洁。 如果一個(gè)任務(wù)嫂便， 不管是體力的還是腦力的， 可以用效率或生產(chǎn)率來(lái)衡量闸与， 那么就應(yīng)該交給機(jī)器人來(lái)完成毙替。 需要效率的事情交給機(jī)器人好了岸售。 我們真正擅長(zhǎng)的是浪費(fèi)時(shí)間。

11:16

（笑聲）

11:16

我們最擅長(zhǎng)做那些沒(méi)有效率的事情厂画。 科學(xué)從本質(zhì)上來(lái)說(shuō)是低效的凸丸。 我們一次又一次的失敗， 很多試驗(yàn)和嘗試都徒勞無(wú)功袱院， 否則我們也學(xué)不到什么東西屎慢。 事實(shí)就是， 科學(xué)研究沒(méi)有什么效率忽洛。 創(chuàng)新從定義上來(lái)說(shuō)就是低效的腻惠。畢竟我們需要制作原型， 需要做各種嘗試欲虚，經(jīng)歷各種失敗集灌。 探索是低效的。 藝術(shù)是低效的复哆。 人際關(guān)系也是低效的欣喧。 這些都是我們喜歡做的事情， 因?yàn)樗鼈兌际堑托У摹?高效是機(jī)器人的使命梯找。 還要認(rèn)識(shí)到唆阿，我們將和 AI 一起工作， 因?yàn)樗鼈兊乃季S方式與我們不同锈锤。

12:02

在“深藍(lán)”戰(zhàn)勝國(guó)際象棋的世界冠軍后驯鳖， 人們以為國(guó)際象棋沒(méi)什么玩頭了。 但事實(shí)上久免，目前世界上 最厲害的國(guó)際象棋冠軍 并不是 AI浅辙， 也不是人類(lèi)， 而是由人類(lèi)和 AI 組成的團(tuán)隊(duì)妄壶。 最棒的醫(yī)學(xué)診療師 既不是醫(yī)生摔握，也不是 AI，而是他們組成的團(tuán)隊(duì)丁寄。 也就是說(shuō)我們將和 AI 一起工作氨淌， 你將來(lái)的薪酬， 很可能取決于 你跟機(jī)器人合作得如何伊磺。 這就是我想說(shuō)的第三點(diǎn)： AI 是不同于我們的盛正， 它們是技術(shù)設(shè)備， 我們將與它們合作屑埋， 而非競(jìng)爭(zhēng)豪筝。

12:43

那么， 未來(lái)會(huì)如何？ 我想续崖，25年后我們回頭再看 今天對(duì) AI 的理解敲街，我們會(huì)說(shuō)： “你們那都不叫 AI。 你們甚至都還沒(méi)有真正的因特網(wǎng)严望， 25年后的因特網(wǎng)才能叫因特網(wǎng)呢多艇。“ 我們也還沒(méi)有真正的 AI 專家像吻。 而大量的資本正涌向這個(gè)領(lǐng)域峻黍， 動(dòng)輒數(shù)十億美金， 這是一個(gè)巨大的產(chǎn)業(yè)拨匆。 但我們尚未擁有真正的 AI 專家—— 如果跟20年后相比的話姆涩。 我們還處在最初的起步階段， 所有一切才剛剛開(kāi)始惭每。 因特網(wǎng)的歷史才剛剛開(kāi)始骨饿。 美好的未來(lái)才剛剛開(kāi)始。 未來(lái)20年最受歡迎的 AI 產(chǎn)品洪鸭， 最普及的 AI 產(chǎn)品样刷， 還沒(méi)有被發(fā)明呢仑扑。 也就是說(shuō)览爵，你們還有機(jī)會(huì)。

13:35

謝謝镇饮！

13:36

（笑聲）

13:37

（掌聲）