導語：Eye on A.I.是由紐約時報資深記者Craig S. Smith主持的一檔雙週部落格節目。每一期節目，Craig都將與這一領域有影響力的人物進行交流，推進廣義環境中的機器智慧新發展，思考技術發展新蘊意。
機器之心為此係列對話的中文合作方。以下為此係列內容的第二篇，Craig Smith與Jack Clark就全球AI發展進行了探討。
Hi, this is Craig Smith with a new podcast about artificial intelligence. I’ll be talking to Jack Clark, author of the popular Import AI newsletter, about what he has learned during the previous week and why it is significant. Jack is a veteran of the British technology journal, The Register, and of Bloomberg News. He now works on policy and communications for OpenAI, the nonprofit artificial intelligence research company founded by Elon Musk. So, think of this podcast as a review of what’s happening in the world of AI, curated by one of its keenest observers.
大家好，我是 Craig Smith，這是我建立的一個關於人工智慧的播客。今天與我對話的是 JackClark，他是很受歡迎的Import AI 新聞訂閱源的作者。今天我們要談談他過去一週瞭解到了什麼以及為什麼這很重要。Jack是英國科技雜誌 TheRegister和 Bloomberg News的資深行業老兵。他現在也為 ElonMusk資助的非營利性人工智慧研究公司 OpenAI工作，致力於政策和溝通問題。所以你可以把今天的播客看作是對當前人工智慧世界的概覽，而且是來自一位最敏銳的觀察者。
The first thing I thought we’d talk about is this guy who wants to use reinforcement learning to trade cryptocurrencies. One question I had is why there hasn’t been more research published in this area. I know academics sort of look down on applying AI to trading, but all the big hedge funds are chasing AI engineers these days. With all the data available on the financial markets, you’d think it would be fertile ground for researchers.
Craig：今天我們要談的第一件事是這個想使用強化學習來交易加密貨幣的傢伙。我的一個問題是，為什麼這方面沒有很多研究論文發表出來？我知道學術界多少有些瞧不起將 AI用來交易的做法，但現在所有大型的對沖基金都在爭奪 AI工程師。鑑於金融市場有那麼多可用的資料，我不禁思考這會成為研究者的一片沃土，你覺得呢？
Jack: I mean hedge funds for many years have hired physicists, they've hired A.I. engineers, they have hired basically quantitative people, statisticians and others.
Jack：多年以來，對沖基金已經在僱傭物理學家了，他們也一直在僱傭 AI工程師，他們基本上僱傭的是做量化分析的人、統計人員等。
[01:34]They have been modeling this stuff. It's just by nature, any information you provide back to the public realm puts you at a disadvantage. I mean let's think about you know hedge funds like Renaissance are basically the epitome of these quant aka quantitative trading strategy shops. The difference with this and what interested me about it is that Bitcoin, Ripple, you know these other large crypto currencies are early enough in their cycle that we can start to model these markets. We have lots of data about them. But they aren't so large as markets that they have these giant-like crypto algorithmic sharks in them that are trying to sort of destroy everyone that trades in them. So, it actually seems like a reasonable research platform whereas today if you if you don't know much about the financial markets and you try to build an algorithmic trading bot that works in them, you'll most likely be killed almost instantly and so you can't learn much. Whereas crypto presents a kind of new burgeoning market where we can see maybe more of a DIY culture about the sort of financial bot research, which excites me and I think could be quite a big thing for broadening the knowledge about it.
他們也一直在做這方面的建模。從本質上講，你提供給公共領域的任何資訊都會將你處於不利的地位。我的意思是，假設你知道 Renaissance 這樣的對沖基金就是這些量化交易策略商店的縮影。我感興趣的地方又有所不同，而是與比特幣、瑞波幣有關，你知道這些大型的加密貨幣仍處於其生命週期中相當早期的階段，我們才剛開始建模這些市場。我們有大量與之相關的資料。但它們的市場又並不非常大，裡面還存在著一些巨大的演算法巨鯨，想要吞噬在其中交易的每個人。所以，如果你不瞭解金融市場，而你又試圖創造一個演算法交易機器人，這些加密貨幣市場實際上就是相當合理的研究平臺。因為在其它已有充分發展的市場，你很可能立馬就會遭遇滑鐵盧，學不到任何東西。加密貨幣是一個相當新的發展迅速的市場，我們可以看到在金融機器人研究方面可能更多的還是 DIY文化，我覺得這激動人心，我認為這能幫助人們擴充套件對金融的理解。
Craig: Yeah, I thought that people should band together, a bunch of AI engineers and create an AI hedge fund and use the proceeds of that to fund further research. I mean that's kind of what Renaissance does right. But the research their funding is just fed back into their own hedge fund. In any case. Yeah, I thought it was interesting that this guy Denny Britz is talking about it. I hadn't seen many papers about it, I hadn't seen anything referencing crypto currencies. Maybe I'm just not reading the right things but…
Craig：我認為人們應該團結起來，大量 AI工程師團結在一起可以創造一個 AI對沖基金，比使用其收益來支援進一步的研究。Renaissance 現在做的就是這種事情。但他們贊助的研究只是回饋給了他們自己的對沖基金。Denny Britz談論過它。但我看到有關這方面的論文不多，我沒看到任何參考了加密貨幣的研究。也許只是因為我讀得還不夠多……
Jack: It was the first thing I saw, also. I think you're reading the right things and I think it's just not much is happening yet because we're early in that market. But I'm excited to see what happens when you have a bunch of hobbyist AI researchers turn their attention towards something with a huge amount of information and a huge amount of dynamicism like a financial market and get to work. I think that could lead to some really fun things.
Jack：這也是我看到的第一個。我認為你讀得夠多了，我認為只是現在相關研究還不夠多，因為這個市場還處於相當早期的階段。但是我很期待看到當一些有相關愛好的 AI研究者將注意力轉向這個有大量資訊且非常動態的領域時會發生什麼。我認為這會帶來一些真正有趣的成果。
Craig: Fun and presumably lucrative.
Craig：有趣而且也可能有利可圖。
[04:02] You also talk in your newsletter about neural architecture search, which allows researchers to quickly find the best architecture for their needs. There seems to be a lot of focus right now on using AI to automate a lot of tasks in designing AI systems - and that speeds up the process. As that continues, it just seems that it would feed on itself and more and more of the writing of AI systems would be done by AI systems and researchers will focus more and more on fresh implementations or theory.
你也在你的新聞訂閱中談到了神經架構搜尋，這讓研究者可以快速找到滿足自身需求的最佳架構。現在似乎有很多研究者都在關注使用 AI來自動化設計 AI系統中的很多工——這能加速設計過程。隨著這方面研究的繼續，似乎會實現自我增益，使得越來越多的 AI系統開發工作可以交給 AI系統完成，研究者則會越來越多地關注全新的實現或理論。
Jack: Yeah, I think that something that most people don't really realize yet is that the last five years of AI has been dominated by data. You know you always hear about how important data is and that's true. If I'm doing an application today I need data. If I'm doing a specific proprietary sort of vertical AI business, I probably need some kind of proprietary data. But what techniques like neural architecture search tell us is that the person with the largest computer can probably figure out the most efficient AI algorithm to do something with data. And regardless of how strategic data is, in the future, techniques like neural architecture search mean that the person that can wield that most effectively will essentially be the most flexible and most agile at taking whatever data they are presented with and creating some best-in-class algorithm on top. So, I think that, not only is the technique interesting in the sense it uses things like reinforcement learning to get AI to learn how to build better AI, but from an economic standpoint it's quite indicative of the sorts of economies of scale benefits you're going to get as an AI developer in the future and what it means for the larger competitive market around it.
Jack：我認為大多數人還沒真正意識到一件事，即過去五年的 AI領域已經被資料所主導。你常常聽人說資料很重要，這是事實。如果現在我要做個應用，我就需要資料。如果我要做一種專有的垂直領域的 AI 業務，我可能就需要某種形式的專有資料。但神經架構搜尋這樣的技術告訴我們，擁有最強大計算機的人可能能夠找到最有效的使用資料的 AI演算法。而且不管資料的戰略性如何，在未來，如果神經架構搜尋這樣的技術得到最有效的應用，那麼基本上就能得到最靈活的模型——不管處理什麼資料，都能創造出那一類別中最優的演算法。在我看來，使用強化學習等方法來讓 AI學習如何創造更好的 AI，這方面重要的不僅有技術問題，而且從經濟角度看也大有裨益——能夠形成規模經濟，未來你也許只需要一位 AI開發者即可，這也意味著會有更大的競爭性市場。
Craig: They can design state-of-the art systems using less than a day’s computation on a single GPU, which represents 1,000-time reduction in computational cost. So, when you are talking about scale, you mean if you can do this on a single GPU and there are people with access to hundreds of GPUs, that's where the scale comes in. Is that what you mean?
Craig：他們僅用單個 GPU，用少於一天的計算時間就設計出了當前最佳的系統，這表示計算成本下降了 1000倍。所以，當涉及到規模時，既然在單個 GPU上就能做成這種事，那麼如果有數百個 GPU，應該就能做出更大的成果。這就是規模化的好處。你是這個意思嗎？
Jack: Yes. What it means is that previously if I was Google or Facebook or Microsoft, I would have a few hundred GPUs lying around and I could ask them to try and figure out AI systems for me.
Jack：是的。這意味著，假設之前我在谷歌或 Facebook或微軟，我能使用數以百計的 GPU，我可以用它們來為我試探和尋找 AI系統。
And now if I have one GPU lying around then it will allow me to do the same thing except if I’m Google I can now use my hundreds of GPUs to be hundreds of times more efficient than I was previously and sort of widen the gap further. So, though it's made it much more efficient for the individual developer, it has some pretty significant advantages built in for the large scale operators.
現在也許我用一個 GPU也能做成同樣的事情，但如果我在谷歌，有數百個 GPU，那麼我的效率也會比之前提升數百倍，從而能進一步拉開差距。所以，儘管對個人開發者來說效率更高了，但這也會大規模運營者帶來十分顯著的內在優勢。
Craig: And along the same line, [07:13] this Facebook Tensor Comprehensions that allows people to write in mathematical notation and it will in effect translate that into implementations. There seems to be this trend of automating a lot of these steps or at least streamlining a lot of these steps to allow a lot of it to be taken care of by AI systems and leave the researchers more time to research. Am I reading that right?
Craig：這一思路的還有，Facebook的 Tensor Comprehensions能把用數學符號寫的演算法有效地轉譯成具體實現。似乎正是這一趨勢，也就是自動化很多步驟，至少簡化了其中很多步驟，將其交給 AI系統處理，讓研究者能將更多時間投入研究。我的理解正確嗎？
Jack: You are, and it's a lot like the shoes that you and I wear on our feet, right, where if you ever go to a bespoke shoemaker they will make you probably the best shoe of your life. And that's still the case in AI. If you go to a person they'll be able to make the best possible system today. But if you go to a factory, the same way that if you go to a factory for making shoes, and you give them your parameters, which in your and my case will be our foot size and some other details, and in an AI case will be the type of problem, the type of system you're running it on, and, you know, via Facebook’s Tensor Comprehensions some more specific details about the characteristics of this problem you're trying to solve, they will automate the solution to it.
Jack：你是對的，這就像是你和我腳上穿的鞋一樣，如果你去找定製鞋匠，他們可能能為你做出最合腳的鞋。在 AI領域也是如此。現在如果你專門找人做，他們可能能做出可能最佳的系統。但如果你去工廠，就像你買鞋廠做的鞋一樣，你提供的引數，也就是我們的腳掌大小和某些細節，在 AI的案例中就是問題的型別、你所執行的系統的型別，它們就能自動化提供解決方案。當然，對於 Facebook 的 Tensor Comprehensions，還需要有關問題特徵的某些更具體的細節。
And the really significant thing about that is that we have a relatively small number of people who are capable of doing smart stuff in AI, but we have a really, really, really large amount of computers available to us. [08:50] And this again suggests to me that we're going to enter this era of the compute monopoly, where though these organizations are making new techniques available - you know Facebook tells us about this; Google tells us about neural architecture search - if you don't have the underlying infrastructure you're going to struggle to implement this and struggle to sort of develop it.
而真正意義重大的問題是能在 AI領域做聰明的事情的人相對來說非常少，但我們有非常非常多可用的計算機。這讓我又覺得我們即將進入計算壟斷時代——儘管這些組織機構會公開提供新技術（你知道 Facebook提供了這個技術，谷歌提供了神經架構搜尋），但如果你沒有那些底層基礎設定，你很難實現這些技術，更別說開發這些技術。
One point I need to make so sure I'm being clear about Facebook is that they are releasing this general automated algorithm tool and I think that's great. If you're an individual developer this means you don't need to design the really, really, fine grained stuff that maximizes your performance in an industrialized way. But I think it is indicative of how, just as in the Industrial Revolution, the large compute companies of this era are automating themselves and are experimenting with new ways of arranging their workloads, except that rather than with people and people being automated, it’s now automating discrete software programs into larger automated end to end systems. And that to me is very new and very significant.
我需要明確一點，Facebook公開發布這種通用的自動化演算法工具是很讚的。如果你是一位個人開發者，這意味著你無需做這些真的非常細粒度的事情，就能以工業化的方式最大化你的效能。但我認為，正如這一次工業革命一樣，這說明這個時代的大公司正在自動化它們自身，並且正在實驗安排自身工作的新方式。這不再是使用人或讓人被自動化，而是將分散的軟體程式自動化到更大型的自動化端到端系統中。在我看來，這是非常新穎且具有重大意義的。
Craig: Yeah. What struck me about this in particular was moving from a two-step process where you have a researcher who writes something out in mathematical notation and then gives it to an engineer who codes for GPUs or CPUs, you have the ability for a researcher to write in mathematical notation and have that automatically coded and implemented for a GPU or CPU.
Craig：我覺得這尤其值得關注的一點是脫離了原來兩步式的過程，即先由研究者用數學符號寫出某些東西，然後再交給工程師寫成在 GPU或 CPU上執行的程式碼。現在只需要讓研究者寫出數學符號，然後就能自動編寫用於 GPU或 CPU的實現程式碼。
[10:36] Are we moving to the day where you will not even need to write in mathematical notation, that you will be able to write in natural language and that that will be translated into code?
未來我們甚至可以不用寫數學符號嗎，能不能直接將用自然語言寫成的思路翻譯成程式碼？
Jack: I wish we were in that day now, but we are some distance from it. Like where we are now is more like I can write a somewhat less alien programming language and I can automate the sort of underlying system to a high degree and that's what you're seeing with Tensor Comprehensions and to some extent with these neural architecture search techniques. I think that language is going to be one of the last things that we get really, really, really good at before we have fundamental breakthroughs in the fields that people call artificial general intelligence. So as hopeful as you are, Craig, I really wouldn't hold your breath here. I actually think it's going to sadly take a while before you or me can specify natural language stuff that it's able to do. And the reason why that is that it needs to map your language to a very specific subset of technical commands which it then needs to map to a broad set of sort of technical implementation terms. And language is so unrestricted and so broad, even relative to programming languages that we use for computers, that I think it's going to be one of the last things we do.
Jack：我希望我們現在就能達到那種程度，但我們還有些距離。現在我們所做到的是將一些用不那麼難懂的程式語言寫成的東西自動化處理成可以執行的程式碼，這是 Tensor Comprehensions 以及某種程度上那些神經架構搜尋技術所做到的。我認為，在所謂的通用人工智慧領域實現重大突破之前，語言將會是人類最後一個真的非常擅長的領域。所以，儘管你滿含希望，Craig，但我認為你不應抱有期待。很不幸，我認為還需要很長時間我們才能用自然語言指定計算機要做的事情。因為這需要將你的語言對映到一個非常特定的技術指令子集，然後它還需要對映到一個更廣泛的技術實現術語集合。而相對於我們為計算機使用的程式語言，自然語言本身又是如此的非限定且具有廣泛的含義，所以我認為這將會成為人類最後一項獨有能力。
Craig: [12:16] We’ve spoken before about the rise of Evolution Strategies as an alternative to reinforcement learning. You highlight Google’s research that shows the results of the two techniques converging when they are extended out far enough. Can you talk a little bit about that?
Craig：我們之前談到過進化策略（Evolution Strategies）的興起，成為強化學習的一種替代方法。你重點談到了谷歌的一項研究，該研究表明這兩種技術的結果在擴充套件得足夠遠時會聚合到一起。你能談談這方面的研究嗎？
Jack: Evolution is all around us. Evolution definitely works in the sense that you and I are talking to each other via technology that we invented and we both evolved from slugs. So, we had some pretty compelling evidence that over a long enough time scale evolution produces significant things. Reinforcement learning has weirdly more of a theoretical basis, more of a reason to believe in it and more of a reason to be able to easily develop it. And what this Google research shows is that, if you have a very, very, very large number of computers, a large enough amount to leverage the distributed intelligent nature of evolution, then evolution will converge to a higher accuracy amount faster than reinforcement learning. So, if you run the computers for a long enough amount of time reinforcement learning and evolution strategies attain something corresponding to parity. And that's pretty interesting.
Jack：進化就在我們身邊。進化肯定是有效的，畢竟你和我就在透過我們發明的技術交談，而我們都是從黏黏的蟲子進化而來的。所以，我們有非常有說服力的證據表明足夠長時間的進化能得到意義重大的結果。強化學習更多的是一個理論基礎，一個可以相信的原因以及一個可以實現輕鬆開發的原因。谷歌的這個研究表明，如果你有非常非常非常多的計算機，多到足以體現進化的分散式智慧的本質，那麼進化將會以比強化學習更快的速度收斂到更高的準確度。所以，如果你執行計算機足夠長的時間，強化學習和進化策略會得到相近的結果。這是非常有意思的研究成果。
And that again comes back to a point that you and I were talking about earlier, which is about compute. And evolution strategies, you can view as a big sort of dumb tool that gets its power from compute and reinforcement learning as a slightly more specific tool that gets its power from certain priors baked into the algorithm that help the algorithm efficiently learn over the information that is provided. And what you find is that, if I'm in a regime where I have an infinite number of computers, then evolution strategies does just as well if not marginally better than reinforcement learning. Now that doesn't mean that reinforcement learning researchers are about to become unemployed but it does mean that they're going to need to think quite carefully about how they can surpass this and in what regimes you need to have to the sort of priors that an RL algorithm has, whereas in other regimes they may be so economically valuable or so critical that you're just happy to throw a shitload of computers at them and run evolution over them instead.
而這又回到了我們之前談論過的問題，也就是關於計算的問題。對於進化策略，你可以將其看作是某種巨大而笨重的工具，其力量源自計算；而強化學習是更特定一點的工具，其力量源自某些融入演算法中的先驗知識，這些知識可幫助演算法有效地學習所提供的資訊。你可以發現，如果我在一個能使用無限數量的計算機的領域，那麼進化策略的表現能達到與強化學習同等乃至更好的程度。這並不意味著強化學習研究者就要失業了，但卻意味著他們應該思考如何實現超越——在哪些領域你需要強化學習所需的那種先驗知識，而在其它可能非常具有經濟價值或至關重要的領域，你可能則更願意直接找一大推計算機來執行進化程式。
Craig: And what did you mean when you said that larger computer operators will be able to explore potentially dangerous use cases earlier, giving them an advantage?
Craig：你還說更大型的計算機運營者有可能更早地探索危險的用例，為自己提供優勢，這是什麼意思？
Jack: What I mean is that we're entering the era where we can design AI agents that can take actions in the world, as well as just sort of passively observe them. The whole endpoint of reinforcement learning is to design AI agents that can be installed on, you know, robots or cars or drones or what have you, that can do things. You know they know how to do things, they know how to generalize their actions, and it tells us that people with the biggest computers can probably train the most advanced AI agents to take actions in the world. And so they are going to become aware quicker than other people about whether these things are dangerous or not. They're going to be able to learn how capable a drone is that can, say, follow a person running around a forest and target them. You know, that something that you're going to learn if you have a bigger computer. And that doesn't necessarily mean you have to use evolution strategies or you have to use reinforcement learning. But one of the messages of this research is that if you have larger computers you can explore this strange frontier more.
Jack：我的意思是，我們正在進入這樣一個時代——我們可以設計出能夠在世界中採取行動的智慧體，而且某種程度上只能被動地觀察它們。強化學習的整體最終目標是設計出能安裝在機器人、汽車乃至任何事物上的 AI 智慧體，使它們能夠完成任務。你知道它們知道如何行事，它們知道如何泛化它們的動作。有最強大的計算機的人可能能夠訓練出最先進的AI智慧體在世界中執行任務。因此他們能比其他人更快地瞭解這些事物是否危險。他們能知道一臺無人機的能做到什麼，比如跟蹤一個跑進森林的人並且以他為攻擊目標。你知道的，如果你有更大的計算機，你就能知道某些東西。這並不意味著你必須使用進化策略或使用強化學習。但這一研究傳遞出了一個資訊：如果你有更大的計算機，你就能更深入未知的領域。
Craig: But the point that experiments like this suggests that large compute operators will be able to explore potentially dangerous use cases earlier doesn't necessarily have anything to do with evolution strategies versus reinforcement learning. It's just that these guys showed that with 450 GPUs they can see down the road that these two things converge. And so, with that kind of compute they will be able to see other things down the road faster than someone that doesn't have that much computing power.
Craig：這個實驗的目的是說明更大的計算機運營者有可能更早地探索危險的用例，這並不一定與進化策略或強化學習有關。只是這些人表明，使用 450個 GPU，他們能看到這兩種方法的匯聚。因此，有那樣的計算能力，他們將能比其他沒有如此多計算能力的人更早看到發展的路徑。
Jack: It’s sort of a chicken and egg. Like if I had a very large number of computers then it's very reasonable to me to test evolutionary strategies versus reinforcement learning. If I have a small number of computers, I'm going to use the area it has the greatest theoretical justification which is, to some extent RL versus evolution, which I know requires sort of larger quantities of computers to get the initial performance but may converge faster than RL. And it tells you that you're in this kind of rich-get-richer world where the people with the largest computer can probably do the more impactful experiments about the underlying theoretical constraints of the scientific discipline they're working in.
Jack：這多少像是雞和蛋的問題。比如如果我有非常多的計算機，那麼對我來說，對比測試進化策略與強化學習是非常合理的。如果我僅有少量計算機，我會將其用於已有最好的理論證明的領域，在某種程度上即是強化學習領域；至於進化方法，我知道這需要更多計算機才能達到初級的表現，但也許收斂得比強化學習快。而且這也會告訴你現在是一個富者更富的世界，有更大型計算機的人更可能去做更意義重大的實驗，從而更加了解他們所研究的科學學科的基礎理論約束。
Craig: Yeah. [17:41] And I thought it was interesting what you said about AI research diverging into low compute and high compute domains; people who can run these massive experiments and then there are others that are stuck with less computing power who can still do things with reinforcement learning, for example, but can't project as far forward because they don't have the computing power.
Craig：你說 AI研究將分成低計算力領域和高計算力領域，我覺得這很有意思。有大量計算能力的人可以執行那些大規模實驗，而受更少計算力所困的其他人只能堅持用強化學習等方法來進行開發，從而不能將研究推進得更遠。
Jack: It's that and it's also a way to explore fundamental rules that are completely not obvious until you break them. And a really good example is, until you break the sound barrier, it’s not obvious what the sound barrier is. But when you break it, it's very obvious because there's a giant sound and there’s a visual appearance. And we know that in most things in a world there are scaling laws and there are what’s called phase changes, right, where you go through a transition in the system. And these transitions are usually a consequence of some kind of energy that you're inputting into the system. And so all this tells us is that the larger my computer, the higher my chance of finding these phase change boundaries, which will usually tell me about, or at least provide helpful pointers to, understanding the underlying sort of theoretical constructs from which I'm deriving information. Because if I know the kinks in it, or the transition points, it’s easier for me to work out the underlying theory that justifies it all.
Jack：是那樣的，而且有更多算力才能探索基本規則，這些規則在被打破之前是完全不明顯的。音障就是個很好的例子，在你突破音障之前，音障都不明視訊記憶體在。但當你突破時，就非常明顯了，會有巨大的音爆和視覺現象。而且你知道，世界上很多事情都有規模定律，會有所謂的相變，也就是在系統中經歷某種轉變。這些轉變通常都是你將能量注入該系統所造成的結果。這些都告訴我們，計算機越強大，找到這些相變邊界的可能性就越高，這通常能讓我們理解或至少能提供一些有幫助的資訊，從而幫助我們瞭解推導資訊所基於的基礎理論結構。因為如果我知道癥結或轉變點在哪裡，對我來說得出其底層理論來證明這一切就會更加容易。
Craig: Let’s move on to some interesting applications, particularly in the world of healthcare. You talk about one study that frankly amazed me: [19:24] This study that took data from wearable devices like Fitbit and Apple Watch and showed a high accuracy at detecting things like diabetes and high cholesterol, high blood pressure, sleep apnea.
Craig：讓我們來談談某些有趣的應用，尤其是醫療領域的應用。你談到了一個讓我很驚奇的研究。這個研究從 Fitbit 和 Apple Watch 等可穿戴裝置獲取資料，在檢測糖尿病、高膽固醇、高血壓、睡眠呼吸暫停等疾病時能達到很高的準確度。
That really surprised me because it doesn't seem to me that something as simple as an Apple Watch or Fitbit, simple in terms of what kind of data it's collecting, would be enough to make those kind of diagnoses. But the probabilities were very high, 80 percent, 75 percent. Was this groundbreaking? Have there been other studies that have shown that such skimpy data can provide that kind of diagnoses? And if so, how long before we see commercialization of this kind of thing because it sounds fantastic.
這確實讓我驚訝，因為像Apple Watch 或 Fitbit 這樣簡單的事物（簡單是說收集的資料方面）也足以進行這樣的診斷。而且其機率也非常高，80%，75%。這算是突破嗎？是否有其他研究也表明這樣簡單的資料也能提供這樣的診斷？如果有，這種聽起來非常棒的方法何時才能實現商業化？
Jack: So, a rule that we keep on finding with deep learning systems is that they are able to discover things that were not obvious to professionals before. I can give you a very tangible example. Just this week, Google published a new paper that showed that by looking at scans of people's eyeballs, it was better able to predict certain cardiological problems than doctors.
Jack：我們發現深度學習系統有個特性，它們能發現之前對專家而言也不甚明顯的東西。我可以提供一個非常具體的案例。就在這周，谷歌發表了一篇新論文，表明可以透過掃描眼球來預測特定的心臟問題，而且表現還優於醫生。
And that was because its model had learned an interesting correspondence present in sort of the dataset of people's eyeballs that it was showed that somehow correlated to their underlying health of their hearts. And that’s obviously a very strange thing to understand. Right. You know it's not obvious how they connect.
這是因為這個模型已經學到了人們的眼球資料集中呈現出的某種非常相關的對應，其透過某種方式與人們的心臟健康狀況聯絡在一起。這顯然是非常奇怪的事情。對吧。我們自己覺得它們的聯絡並不明顯。
However, I think that was pretty compelling evidence that they do connect and that system found a connection. And to me this study was very similar, where you're using somewhat crude data, you know, to learn about people’s, you know, sleep apnea. You're reading enough information about their wrists somewhat similar to learning about people's hearts by looking at their eyeballs and given the aggregate data, this sort of unprecedentedly large group in the sense that getting very large medical study groups together is a massive pain currently, and they were able to sort of trivially get thousands of people to donate data, shows these interesting correspondences. So, I think it is significant, that tells us about a new sort of scientific medicine practice that's going to emerge as a consequence of this convergence of AI and big data and deep learning.
但是，我認為確實存在非常具有說服力的證據表明它們確實存在聯絡，而且該系統找到了這種聯絡。在我看來，這項研究非常類似於使用某種粗略的資料來了解人們的睡眠呼吸暫停問題。當讀取到了足夠多的來自手腕的資訊之後，就能找到其中的關聯，就像是根據聚合資料，透過檢查眼球來了解人們的心臟問題。這種前所未有的大型群體在某種程式上需要將非常大的醫療研究團隊聚合到一起，這是很難做到的。而他們能夠輕而易舉地讓數以千計的人捐贈資料，展現這些有趣的關聯。所以，我認為這意義重大，能透過 AI、大資料和深度學習的融合，為我們帶來一種新的科學醫療實踐方法。
Now how soon it gets productized? I am an Englishman living in America and I don't understand your healthcare system. I don't understand any aspect of how it works and I don't see great incentives for it to save my life cheaply. So, I don't know how quickly it gets productized. I wish it was quick but I it's not obvious to me how that happens quickly because the meta system that it's happening within doesn't seem to necessarily have the right incentives to, to make me live longer.
那麼，這種技術離產品化還有多遠？因為我是一個住在美國的英國人，所以我不瞭解你們的醫療體系。我不瞭解這到底是如何運作的，我也沒看到用廉價的方式來拯救我的生命的強大動機。所以，我不知道產品化的速度有多快。我希望能很快，但我不清楚現在進展有多快，因為醫療體系中的元系統似乎並沒有適當的動機來讓人們活得更加長久。
Craig: That's interesting. This this sounds so promising, there have been so many of these health-related AI papers and a lot of them have been implemented in computer vision scanning for malignant lesions and things like that. But this one had the possibility because of the simplicity or crudeness of the data that it's collecting and how widespread these wearable devices are it seems that it really has the possibility of revolutionizing the diagnosis of certain kinds of illnesses. I mean things like high cholesterol and diabetes.
Craig：很有意思。聽起來很有希望，現在已有很多與醫療相關的 AI論文，其中很多都涉及到計算機視覺掃描，比如掃描惡性病變等東西。但這一個很有可能成功應用，因為它所用的資料很簡單和普通，而且可穿戴裝置也有廣泛的應用，看起來這確實可能為某些疾病的診斷帶來變革，比如高膽固醇和糖尿病。
The crudeness of the data that that's working off of or as you said looking at the eyeballs and detecting heart trouble, you know, the Chinese, traditional medicine, there are two principal ways that a doctor diagnoses a patient: one is measuring its pulse which is analogous to a Fitbit, and the other is looking at the patient's tongue. And I love this other paper that you highlighted about [23:45] scientists mapping herbal prescriptions to tongue images. That they had 10,000 pictures of tongues and they mapped it to the herbal prescriptions that had been given to those patients.
這種簡單的資料也是有效的，就像你說的，透過檢查眼球來診斷心臟問題。你知道嗎？中國的中醫在醫生診斷病人時有兩個做法：一是測脈搏，這類似於 Fitbit；二是看舌頭。我還很喜歡你提到的這篇論文，科學家將中藥處方與舌頭影像進行了對映。他們有 10000張舌頭照片，並將它們與提供給這些病人的中藥處方進行了對映。
I mean it sounds funny but just as with the eyeball thing, the presumption is that in Chinese traditional medicine there is something to this tongue diagnosis. With AI and with enough data you can start seeing whether there are correlations. In this case what bothered me was that they were correlating the tongues to different herbals prescriptions, not to different illnesses.
這聽起來很有趣，但就像眼球這個研究一樣，人們假設中醫中存在與舌頭診斷相關的東西。使用 AI和足夠多的資料，就能找到它們之間存在的關聯。這個案例讓我疑惑的是他們是將舌頭關聯到不同的中藥處方，而不是不同的疾病。
So just because certain colorations or textures of the tongue tend to get the same herbal prescription doesn't necessarily mean that there is an underlying pathology there that that is being accurately diagnosed.
只是因為特定的舌頭顏色或紋理往往要用同一種中藥處方並不一定意味著其底層的病理得到了正確的診斷。
Jack: Correct. I wouldn't take a prescription that the system gave me at all, I'd run in the opposite direction. However, the fact that you have scientists who are mostly specialists in any sort of medicine looking at using these systems for better diagnosis - the main point to me is that these things have reached such an obvious point of, not only utility, but usability, that you're bringing in really, really fringe stuff at an increasing rate. That to me is the interesting thing. Like their methodology may be somewhat bogus. I suspect that it is. But their ability to access large amounts of data is quite good and their intuition which is, we should sort of gather this data and see if we can learn sort of mappings that we can then fortify, is also good.
Jack：說得對。我根本不會使用這個系統給我的任何處方，我會馬上跑開。但是，你看到各種型別的醫學都有專家在試圖使用這樣的系統來進行更好的診斷。在我看來，主要的一點是這些東西已經達到了一個相當明顯的程度，不僅是效用方面，而且也有可用性方面，並正以越來越快的速度將那些真正邊緣的東西納入其中。對我來說真的很有意思。他們的方法可能有些假冒偽劣。我懷疑就是這樣。但他們獲取大量資料的能力還是相當好的，他們的直覺是應該收集這些資料，然後看能否學習到某種對映，之後再對其進行加強，這樣的直覺也很好。
And it suggests to me that a lot of medicine that has less of a scientific basis and at least in Western scientific medical traditions, we may see weird deep learning systems emerge which show correlations that run counter to the beliefs of the scientific establishment. And that to me is why papers like this are interesting because I see them as the first signs of people starting to experiment with this. And the reason why they're able to experiment with these sorts of technologies is that they have become available enough and simple enough to use that you can use them in these domains that are sort of somewhat non-standard.
我認為很多醫學的科學基礎較少，而在西方則有科學醫療的傳統。我們也許會看到某些奇怪的深度學習系統展現出了某些與已有的科學論斷相違背的關聯。對我而言，這正是這些論文讓我覺得非常有趣的原因，因為我認為這是一個標誌，說明人們在開始實驗了。而他們能夠實驗這種型別的技術的原因是這些技術已經足夠可用並且足夠簡單，你可以在這些多少有些非標準化的領域使用這些技術。
Craig: Yeah. This stuff is moving so quickly that it feels literally months, certainly not many years away, before you take a picture your tongue or you send your Fitbit feed or you take a picture of the boil on your leg and get back a diagnosis very quickly.
Craig：是啊，這方面發展得非常快，讓人感覺用不了幾個月，肯定不需要很多年，你只需要拍一張舌頭照片，或傳送 Fitbit資料流或腳癬的照片，你就能很快得到診斷結果。
Jack: I mean bring it on. You know, I am so ready for that moment, aren’t you?
Jack：那就來吧，你懂的，我非常期待那一時刻的到來，你呢？
Craig: Yeah absolutely. Given health care costs in the United States.
Craig：當然啦。考慮到美國醫療成本這麼高。
Well, that’s it for this week’s podcast. Thanks again, Jack, for making the time. For those of you who want to go into greater depth about the things we talked about today, you can find a transcript of this show in the program notes along with a link to Jack’s newsletter. I encourage you to subscribe. Let us know whether you find the podcast interesting or useful and whether you have any suggestions about how we can improve.
好，這就是本週的播客了。Jack，再次感謝您抽出時間。如果你想要更深度地瞭解我們今天所談的內容，你可以在 https://www.eye-on.ai/找到本節目的轉錄文字。希望你也能訂閱 Jack的新聞源：https://jack-clark.net/。你覺得本期播客有哪些你感興趣或覺得有用的內容，你是否有幫助我們改進節目的建議，請與我們分享。
The singularity may not be near, but AI is about to change your world. Pay attention.
請注意，奇點也許尚未臨近，但人工智慧即將改變這個世界。