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.
我準備來談談未來科技的走勢。 每當新的科技發明, 我們總是驚嘆它所帶給我們的驚喜。 但是實際上科技有一大方面 是很容易預測的, 因為所有的科技系統 都有一定的脈絡可循, 它們有迫切性, 有一定的趨勢, 而這些趨勢都是來自於 電線、開關、電子的 物理本質與化學原理, 而這些模式會周而復始地發生。 所以是這些模式造就了 科技的趨勢及走向。
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.
你幾乎可以把它 看做是一種「萬有引力」。 想像一下,就像雨滴落到山谷中, 雨滴流到山谷中的實際路徑 是無法預測的。 我們看不到雨滴會怎麼流, 但大致上的方向是一定的: 這個方向是向下的。 而這些深根在科技系統裡的 趨勢及迫切性, 告訴了我們科技的大方向。 具體說, 我認為電話的發明是必然的, 但 iPhone 就不是了。 網際網路的發明是必然的, 但推特就不是了。
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.
所以我們現在有很多趨勢正在進行, 而我認為它們其中一個 主要的趨勢就是, 東西越來越聰明了。 我稱這個過程為 「認知化 」──認知── 也就是大家知道的 人工智慧,或者「AI」 我認為未來 20 年, AI 將成為我們社會其中一個 最有影響力的發展、趨勢及驅動力。
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.
當然,AI 已經出現了, 我們已經有 AI 了, 而且它經常在幕後幫助我們, 它出現在醫院後面的辦公室, 用 AI 來診斷 X 光片的能力 比人類醫生還精準。 它會出現在律師事務所, 用 AI 審閱法律文件, 速度比人類的律師還要快。 各位今天坐的飛機也有人工智慧, 人工駕駛只有 7~8 分鐘, 剩下的都是 AI 在駕駛。 當然, Netflix 和 Amazon 也有, 它在幕後給出做出推薦和建議。 這是我們目前已經實現的。
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.
當然,還有一個更先進的案例, 就是打敗世界圍棋冠軍的 AlphaGo。 但人工智慧不僅於此。 如果你在玩電動,你對抗的是 AI, 但最近,Google 開始教他們的 AI 實際意義上的學習如何打電動。 重申一下,教 AI 「打電動」 是一種層次, 但教 AI 「學習如何打電動」 又是另一種層次。 這是人造的智能產品。 而我們正在做的就是將 這種人造的智能產品 變得越來越聰明。
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.
這個趨勢大致上有三個面向, 我認為尚未被充分認知; 我想如果搞懂這三個面向, 我們對 AI 的了解,會更深入一些。 我認為了解這些事, 也可以幫助我們擁抱 AI, 唯有擁抱 AI 才能駕馭 AI。 藉由懷抱更大趨勢來駕馭細節。
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.
所以容我來談談 AI 的三個不同面向。 第一:以人類目前對智慧的了解, 我們對智慧的認知仍相當貧乏。 我們似乎把智能看的太單一面向了, 它有點像是個音符,會越來越大聲。 剛開始像個 IQ 測量儀。 一開始的智商也許跟老鼠一樣低, 有的像猩猩,稍微多一點, 之後開始像個低智商的人類, 然後進化到像我一樣的普通人, 然後變成一個天才。 IQ 智能分數越來越高。 這種看法完全是錯誤的。 這不是智慧該有的樣子── 人類的智慧不僅於此。 它像是一首交響樂, 由不同的音符組成, 而每一個音符, 由不同的認知樂器所伴奏。
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.
人類腦中有很多不同種類的智慧, 我們有演繹推理的能力, 我們有情感的智慧, 我們有空間概念的智慧, 我們可能有 100 種 不同的智能聚合在一起, 而且每個人各有各的強項。 當然,以動物而言, 牠們可能是另一套體系── 另一種不同的智能交響樂, 有時候跟我們人類的一樣。 牠們可能思考方式相同, 但著重點不同, 也許在某些方面超過人類, 像是松鼠的長期記憶力,相當出色, 能清楚記得堅果的埋藏之處。 但其它方面,牠們也許就比較弱了。
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.
當我們要製造機器時, 我們會用同樣的方式來設計機器, 有些智慧型裝置 做得比人類聰明得多, 但其它方面則遠遠不如我們, 因為根本不需要。 我們會將這些產品 這些人工產品, 在不同的 AI 上, 裝置不同的人工認知功能, 我們可以把它們的特定功能 做得相當、相當出色。
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.
所以你的計算機在計算方面 比你聰明許多; 你的 GPS 在空間導航上 比你聰明得多; Google, Bing 的長期記憶比你強。 然後我們再把這些不同種類的智能, 放在,像是,車子裡。 我們之所以這麼做的原因, 是因為它們不會像人類那樣開車, 它們不會像人類那樣思考。 這是它唯一的特色。 它不會分心, 它不用擔心瓦斯爐沒關, 它不用考慮要不要主修財經。 它只會開車。
(Laughter)
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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.
只會開車,好嗎? 而我們最終可能會拿它來廣告 「無意識」。 它們沒有意識, 它們不會關心這些瑣事, 它們不會分心。
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,我們應該盡我們所能 去嘗試做出一些不同的想法。 我們將會天馬行空, 去嘗試所有可能的思考方式。 也許還有一些 相當不好解決的商業及科學問題, 單憑人類自身的想法可能無法解決。 我們可能需要分兩步走, 先發明出新的思考方式, 再來解決這些真正的難題, 比如說,像是暗能量或量子引力。
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.
我們所做的實際上 就是在創造「異形智能」。 在某種程度上, 這概念有點像是,人造異形。 它們將幫助我們從不同的角度思考, 因為不同的想法是創新、 財富和新經濟的引擎。
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.
第二方面:我們將用 AI 進行第二次的工業革命。 在第一次工業革命中, 是以我稱之為「人工力量」 為基礎的革命。 在此之前, 在農業革命時期, 每樣東西都需要用人力 或畜力完成。 除此之外別無它法。 在工業革命期間最偉大的發明就是 我們利用水蒸氣、石化燃料 產生人工力量, 來做任何我們想做的事情。 今日,當你開車行駛在高速公路上, 只要輕輕撥弄開關, 就相當於在駕馭 250 匹馬, 或者說,250 馬力。 它可以讓我們蓋大樓、 建造城市、修建道路, 開辦能夠源源不斷 生產椅子或冰箱的工廠, 這都遠遠超出人力所為。 而且這樣的人工電力 可以透過電線、電網 輸送到每一個家庭、工廠、農場, 讓每個人都可以買到 這樣的人工電力, 只要插上插頭就可以使用。
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.
所以,這也是創新的來源之一, 因為農民可以為手工幫浦通上電, 有了這種人工力量, 就變成了電動幫浦。 你將這種力量擴大成千上萬倍, 而這個公式為我們帶來了工業革命。 而我們所看到的一切、 那些我們現今享受的過程, 幾乎都來源於此。
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.
現在我們也要在 AI 上做同樣的事。 我們將用網路傳送 AI, 現在好比你有一個「電泵」, 你把「電泵」加上人工智能, 你就會得到聰明的「電泵」, 類似的改造做上幾百萬次, 就會引爆第二次的工業革命。 將來汽車行駛在高速公路上, 它不僅有 250 匹馬力, 還有 250 種腦力。 這就是自動駕駛車。 它是一種新的商品; 它是一種新的基礎設施。 AI 將會在網路、雲端上傳輸 就跟電一樣。
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.
所以之前我們把每樣東西都電力化, 現在,我們要把它們認知化。 所以,我會推測, 接下來的一萬家初創公司的公式, 相當, 相當簡單, 就是拿某樣東西 X,加上 AI。 這個公式就是我們將來要做的。 我們將以這種方式 創造第二次的工業革命。 順帶一提,目前,此時此刻, 你可以登入Google 用六美分購買 AI 來提交一百個圖像識別請求。 目前已經有這項服務了。
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.
第三個形勢: 如果我們將 AI 編組起來, 我們會得到機械人。 而機械人就是一些小型的任務執行器, 它們將會取代我們現在已經在做的事。 工作只是一堆任務, 所以人類的工作會被重新定義, 因為它們會幫我們執行這些任務。 但它們也會創造出全新的分類 很多全新種類的任務, 一些我們從未聽過的工作。 它們實際上會催生出新的職業, 一些我們願意從事的新工作, 就像自動化所引發的許多新事物, 我們之前不知道會需要它們, 但時至今日, 我們已經離不開它們了。 機器人產生的新工作 比我們被取代的工作還要多, 更重要的是, 我們交給它們的那些任務 都需要效率或生產率。 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的話, 那麽就應該交給機器人來完成。 機器人擅長的就是生產率。 我們真正擅長的是浪費時間。
(Laughter)
(笑聲)
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。 它的運作方式實際上是 一次又一次的失敗, 很多試驗和嘗試都徒勞無功, 不這樣做,你學不到東西。 事實就是, 科學研究沒有效率可言。 創新從定義上來說就是低效的。 因為我們需要製作原型, 需要做各種嘗試,經歷各種失敗。 探索本質上是低效的。 藝術是低效的。 人際關係也是低效的。 這些都是我們喜歡做的事情, 因為它們是低效的。 要效率找機器人才對。 我們要知道, 我們將和 AI 一起工作, 因為它們的思維與我們不同。
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.
當深藍打敗西洋棋的世界冠軍後, 人們認為西洋棋玩完了。 但事實上,如今全世界 最厲害的西洋棋冠軍 並不是 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 不是醫生,也不是 AI, 而是他們組成的團隊。 我們將和 AI 一起工作, 你將來的薪資, 很可能取決於 你跟機器人合作得如何。 這就是我想說的第三點: AI 是不同於我們的, 它們是基礎設施, 我們將與它們一起工作, 而非競爭。
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.
所以,未來: AI 將帶我們到哪裡? 我想,二十五年後, 人們回頭看今日 我們對 AI 的理解,會說: 「你們那都不叫 AI,實際上, 你們甚至都還沒有真正的 網際網路呢!」 和 25 年後相比較的話, 我們還沒有真正的 AI 專家。 目前有大量的資本投資在這個領域, 已經花了數十億美金; 這是一個巨大的產業。 和 20 年後相比較, 我們尚未有真正的 AI 專家。 我們還處在剛開始的開始, 所有這一切才剛開始。 我們處在網際網路的 第一個小時裡。 我們正處在未來發展的 第一個小時裡。 二十年後最受人們喜愛的 AI 產品, 人人都會用的 AI 產品, 還沒有被發明出來。 也就是說,你還為時未晚。
Thank you.
謝謝!
(Laughter)
(笑聲)
(Applause)
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