矽谷頂級風投對機器學習的幾個預測

英文作者

Benedict Evans

著名風投Andreessen Horowitz的合伙人。

媒體和科技行業資深專家。

機器學習：下一個浪潮？

眼下火熱的機器學習，距離開始爆發已有四五年的時間，幾乎沒有人不知道了。成立公司，大型科技公司準備圍繞機器學習進行自我改造的事情，每天都在發生。

科技圈外人士可以在《經濟學人》或者《商業周刊》封面上讀到這方面的報道，除此之外，很多大公司都開展了各種項目。我們感覺下一個浪潮已經在向我們走來。

假如思考再深入一點，我們大都知道神經網路是什麼。從理論上講，我們知道它大概與模式和數據相關。機器學習讓我們在數據中找到不那麼明顯的，概率意義（因此是「推論」）的模式或者結構，在此之前，找到的都是明顯的，只能由人，而不是計算機來完成。機器學習可以解決以前「對計算機很難但是對人類很容易」的那類問題，更有用的說法是，「很難由人類描述給計算機的」。我們已經見到了一些很酷（或者讓有些人憂心忡忡的）的語音和視覺實例演示。

We"re now four or five years into the current explosion of machine learning, and pretty much everyone has heard of it. It"s not just that startups are forming every day or that the big tech platform companies are rebuilding themselves around it - everyone outside tech has read the Economist or BusinessWeek cover story, and many big companies have some projects underway. We know this is a Next Big Thing.

Going a step further, we mostly understand what neural networks might be, in theory, and we get that this might be about patterns and data. Machine learning lets us find patterns or structures in data that are implicit and probabilistic (hence 『inferred』) rather than explicit, that previously only people and not computers could find. They address a class of questions that were previously 『hard for computers and easy for people』, or, perhaps more usefully, 『hard for people to describe to computers』. And we』ve seen some cool (or worrying, depending on your perspective) speech and vision demos.

機器學習是什麼？這是個問題

不過，我不認為我們對機器學習意味著什麼已經瞭然於心 - 它對於科技公司或者非科技公司意味著什麼，如何系統地考慮它將帶來什麼新的東西，或者對我們來講，機器學習是什麼，它可能會解決什麼重要的問題。

「人工智慧」這個詞有點幫倒忙，基本上會把天聊死。一提到「AI」，彷彿《2001》開頭裡那塊黑色巨石出現了，我們都變成了猩猩，沖著它叫喊和揮舞拳頭。（譯註：《2001：星際冒險》是一部科幻小說，黑色巨石是外星人留在地球上的機器，用來促進人類演化。）你不可能分析「AI」。

I don"t think, though, that we yet have a settled sense of quite what machine learning means - what it will mean for tech companies or for companies in the broader economy, how to think structurally about what new things it could enable, or what machine learning means for all the rest of us, and what important problems it might actually be able to solve.

This isn"t helped by the term "artificial intelligence", which tends to end any conversation as soon as it"s begun. As soon as we say "AI", it"s as though the black monolith from the beginning of 2001 has appeared, and we all become apes screaming at it and shaking our fists. You can』t analyze 『AI』.

資料庫技術產生了高價值公司

的確，我覺得我可以列出一堆對機器學習的討論沒有幫助的事物。例如：

「數據是新的石油」；

「谷歌，或者臉書、亞馬遜、BAT掌握了所有數據「；

」AI將會奪走所有的工作「；

當然，還有提起AI這個詞。

更有用的事情，可能是這些：

」自動化「

」實現技術分層「

」關係資料庫「

為什麼是關係資料庫？它是新的、基礎層面的東西，改變了通過計算可以完成的任務。在二十世紀七十年代，關係資料庫剛剛出現，如果你需要調用「所有這個城市裡購買了這個產品的顧客」，你通常需要工程師為你專門處理。資料庫的設計使得它不能將一時興起的交叉分析變成很容易的、常規性工作。你如果提出問題，就要有人搭建資料庫。資料庫是用來存儲數據的系統，關係資料庫把它們變成了數據分析系統。

這個資料庫用途上的變化非常重要，催生了新的功能案例以及新生的、價值數十億的公司。 關係資料庫催生了Oracle，SAP和它的同儕，我們可以在全球運行just-in-time的供應鏈 - 我們還看到了Apple和星巴克的誕生。到90年代，幾乎所有的企業軟體都是關係資料庫 - 包括PeopleSoft、CRM還有SuccessFactors在內的十幾家企業都在使用關係資料庫。沒有人盯著SuccessFactors以及Salesforce說：「這根本行不通，Oracle掌握了所有的資料庫」 - 反而，這個技術成為了基礎，支撐著幾乎所有的東西。

Indeed, I think one could propose a whole list of unhelpful ways of talking about current developments in machine learning. For example:

『Data is the new oil』

『Google and China (or Facebook, or Amazon, or BAT) have all the data『

』AI will take all the jobs』

And, of course, saying AI itself.

More useful things to talk about, perhaps, might be:

『Automation』

『Enabling technology layers『

』Relational databases. 』

Why relational databases? They were a new fundamental enabling layer that changed what computing could do. Before relational databases appeared in the late 1970s, if you wanted your database to show you, say, "all customers who bought this product and live in this city", that would generally need a custom engineering project. Databases were not built with structure such that any arbitrary cross-referenced query was an easy, routine thing to do. If you wanted to ask a question, someone would have to build it. Databases were record-keeping systems; relational databases turned them into business intelligence systems.

This changed what databases could be used for in important ways, and so created new use cases and new billion dollar companies. Relational databases gave us Oracle, but they also gave us SAP, and SAP and its peers gave us global just-in-time supply chains - they gave us Apple and Starbucks. By the 1990s, pretty much all enterprise software was a relational database - PeopleSoft and CRM and SuccessFactors and dozens more all ran on relational databases. No-one looked at SuccessFactors or Salesforce and said "that will never work because Oracle has all the database" - rather, this technology became an enabling layer that was part of everything.

機器學習終將無處不在

因此，這是一個很好的基礎，可以用來思考眼下的機器學習 - 對於我們如何使用計算機，它邁進了一小步，未來還將成為不同企業的多個產品的一部分。最終，機器學習會無處不在，大家對它會習以為常。

有一個很重要的類比就是，雖然關係型資料庫有規模經濟的效應，但是網路效應和「贏家通吃」效應有限。A公司用的資料庫到了B公司的手中不會變得更好；Safeway用的資料庫到了Caterpillar那裡還是一樣。機器學習也是同樣道理：機器學習的核心是數據。但是數據與特定的應用緊密相關。更多的手寫體數據會幫助字體識別專家更好地工作，更多的燃氣渦輪機數據可以幫助系統更好地預測它失效的情況，但是數據相互之間並無益處，並且無法互相替換。

這就是機器學習討論中最常見的誤解關鍵所在 - 它是獨一無二的，普遍適用的事物，它正在演化成為HAL9000，Google或者微軟都已經建立了「One『，Google掌控著」所有的數據「，IBM的確有個叫「Watson」的東西。的確，對自動化的看法總是產生這些錯誤：每產生一波自動化浪潮，我們都想像自己在創造一個具有人類特徵或者一個有著通用智能的東西。在20世紀20和30年代，我們想像中，一個鋼鐵人手舉鋼錘，在工廠里到處巡視， 20世紀50年代，我們想像人形機器人在廚房裡忙乎家務。我們沒有發明機器人傭人 - 我們發明了洗衣機。

So, this is a good grounding way to think about ML today - it』s a step change in what we can do with computers, and that will be part of many different products for many different companies. Eventually, pretty much everything will have ML somewhere inside and no-one will care.

An important parallel here is that though relational databases had economy of scale effects, there were limited network or 『winner takes all』 effects. The database being used by company A doesn"t get better if company B buys the same database software from the same vendor: Safeway"s database doesn"t get better if Caterpillar buys the same one. Much the same actually applies to machine learning: machine learning is all about data, but data is highly specific to particular applications. More handwriting data will make a handwriting recognizer better, and more gas turbine data will make a system that predicts failures in gas turbines better, but the one doesn"t help with the other. Data isn』t fungible.

This gets to the heart of the most common misconception that comes up in talking about machine learning - that it is in some way a single, general purpose thing, on a path to HAL 9000, and that Google or Microsoft have each built *one*, or that Google "has all the data", or that IBM has an actual thing called 『Watson』. Really, this is always the mistake in looking at automation: with each wave of automation, we imagine we"re creating something anthropomorphic or something with general intelligence. In the 1920s and 30s we imagined steel men walking around factories holding hammers, and in the 1950s we imagined humanoid robots walking around the kitchen doing the housework. We didn"t get robot servants - we got washing machines.

找出兩個事物之間的關係

洗衣機是機器人，但是卻不「智能」。它們不知道什麼是水或者衣服。而且，它們也不能什麼都洗 - 比如餐具，洗碗機也洗不了衣服（或者，可以，但是達不到你需要的效果）。它們就是一種自動化裝置，在概念上與傳送帶和分揀機沒有兩樣。同樣，機器學習使我們可以解決計算機無法很好地解決的一系列問題，但是每一個問題都需要區別對待，不同的執行、數據、市場路徑，通常情況下，企業不同。每一個都是一個自動化工作，每一個都是一個洗衣機。

因此，討論機器學習時面臨的挑戰之一就是在對數學原理機械的解釋和對人工智慧的普遍想像之間找到中間地帶。機器學習不會產生HAL9000（至少，專業人士中很少有人覺得它會很快發生），但是稱之為「只是些統計數據」也沒有幫助。回到關係資料庫的類比上，這也許很像1980年的時候對SQL的討論 - 你是怎麼從解釋table joins跳躍到思考Salesforce.com的? 當然，你可以說「它讓我開始有新的問題」，但是問題是什麼並不顯而易見。那麼，通常公司會怎麼做？不久前，一個知名媒體公司團隊對我說：「我們可以用機器學習將十年來對球員的訪談加上index - 但是怎麼做呢？」

Washing machines are robots, but they"re not 『intelligent』. They don"t know what water or clothes are. Moreover, they"re not general purpose even in the narrow domain of washing - you can"t put dishes in a washing machine, nor clothes in a dishwasher (or rather, you can, but you won』t get the result you want). They"re just another kind of automation, no different conceptually to a conveyor belt or a pick-and-place machine. Equally, machine learning lets us solve classes of problem that computers could not usefully address before, but each of those problems will require a different implementation, and different data, a different route to market, and often a different company. Each of them is a piece of automation. Each of them is a washing machine.

Hence, one of the challenges in talking about machine learning is to find the middle ground between a mechanistic explanation of the mathematics on one hand and fantasies about general AI on the other. Machine learning is not going to create HAL 9000 (at least, very few people in the field think that it will do so any time soon), but it』s also not useful to call it 『just statistics』. Returning to the parallels with relational databases, this might be rather like talking about SQL in 1980 - how do you get from explaining table joins to thinking about Salesforce.com? It"s all very well to say "this lets you ask these new kinds of questions", but it isn"t always very obvious what questions. You can do impressive demos of voice recognition and image recognition, but again, what would a normal company do with that? As a team at a major US media company said to me a while ago: "well, we know we can use ML to index ten years of video of our talent interviewing athletes - but what do we look for?』

關於數據和問題類型的思考

那麼，對於一家真實的企業而言，什麼是機器學習領域的洗衣機呢？我認為思考這個事情，有兩套工具可以用。第一套是一系列關於數據和問題類型的思考：

僅僅作為分析和優化技術，機器學習可能會為你已經提出的數據問題提供更好的結果。例如，我們投資的公司Instacart搭建了一套系統，優化個人在超市購物時的路線，改善程度達到50% （這套系統由三名工程師，使用谷歌的開源工具Keras和Tensorflow搭建）。

機器學習可以讓你重新審視手上已有的數據。例如，一位做調查的律師也許會搜索帶有「憤怒」字眼的郵件，或者「焦慮」，或者非尋常的線索、各種文檔，以及關鍵詞搜索。

最後，機器學習使得新類型數據的分析成為可能 - 以前，計算機不能真正讀取音頻、圖像或者視頻，如今這種可能性大大加強了。

以上提到的這些，我發現圖像更讓人感到興奮。計算機自從被發明以來，一直可以處理文字和數據，但是不能很好地處理圖像（以及視頻）。現在，計算機像能夠「讀」一樣能夠去「看」。這意味著圖像感應器（還有麥克風）成為新的輸入機制 - 不是「照相機」，而是新的、功能強大而且靈活的感應器，可以持續輸出（未來）可以為機器讀取的數據。所有的東西，最後都會成為計算機識別問題，雖然今天它們看上去都不是計算機識別問題。

What, then, are the washing machines of machine learning, for real companies? I think there are two sets of tools for thinking about this. The first is to think in terms of a procession of types of data and types of question:

Machine learning may well deliver better results for questions you"re already asking about data you already have, simply as an analytic or optimization technique. For example, our portfolio company Instacart built a system to optimize the routing of its personal shoppers through grocery stores that delivered a 50% improvement (this was built by just three engineers, using Google"s open-source tools Keras and Tensorflow).

Machine learning lets you ask new questions of the data you already have. For example, a lawyer doing discovery might search for "angry』 emails, or "anxious』 or anomalous threads or clusters of documents, as well as doing keyword searches.

Third, machine learning opens up new data types to analysis - computers could not really read audio, images or video before and now, increasingly, that will be possible.

Within this, I find imaging much the most exciting. Computers have been able to process text and numbers for as long as we』ve had computers, but images (and video) have been mostly opaque. Now they』ll be able to 『see』 in the same sense as they can 『read』. This means that image sensors (and microphones) become a whole new input mechanism - less a 『camera』 than a new, powerful and flexible sensor that generates a stream of (potentially) machine-readable data. All sorts of things will turn out to be computer vision problems that don』t look like computer vision problems today.

有趣的案例：自動檢驗褶皺

這不是如何識別貓的照片的問題。我最近遇到一家企業，為汽車廠商製造座椅。他們用廉價的DSP晶元和智能手機圖像感應器，加上神經網路，來識別布料上是否有褶皺（我們應該預期未來會有很多類似的使用場景，利用機器學習和小型便宜的工具，只完成一個任務，就像這裡描述的一樣）。稱其為「人工智慧」沒有什麼幫助：這是將以前無法自動完成的任務進行了自動化。如果是人工，就必須親自查看。

這種意義上的自動化就是考慮機器學習的第二套工具。察覺到布料上的褶皺不需要20年經驗 - 真正需要的是一個哺乳類動物的大腦。的確，我的一位同事認為通過機器學習，你訓練自家狗做的事情也都可以完成。這又是一個有用的視角，來看待人工智慧領域的偏見（狗學到的具體是什麼？訓練數據里有什麼？你確定嗎？你怎麼提問？）但是這個視角有局限性，因為狗有一定的智商，也有常識，和我們搭建的神經網路不同。Andrew Ng認為機器學習可以在不到一秒鐘的時間內完成你所做的任何事情。對機器學習的討論很像是在搜羅比喻，我偏愛的比喻是，它可以給你提供用不完的實習生。

This isn』t about recognizing cat pictures. I met a company recently that supplies seats to the car industry, which has put a neural network on a cheap DSP chip with a cheap smartphone image sensor, to detect whether there』s a wrinkle in the fabric (we should expect all sorts of similar uses for machine learning in very small, cheap widgets, doing just one thing, as described here). It』s not useful to describe this as 『artificial intelligence』: it』s automation of a task that could not previously be automated. A person had to look.

This sense of automation is the second tool for thinking about machine learning. Spotting whether there』s a wrinkle in fabric doesn"t need 20 years of experience - it really just needs a mammal brain. Indeed, one of my colleagues suggested that machine learning will be able to do anything you could train a dog to do, which is also a useful way to think about AI bias (What exactly has the dog learnt? What was in the training data? Are you sure? How do you ask?), but also limited because dogs do have general intelligence and common sense, unlike any neural network we know how to build. Andrew Ng has suggested that ML will be able to do anything you could do in less than one second. Talking about ML does tend to be a hunt for metaphors, but I prefer the metaphor that this gives you infinite interns.

任務的批量自動化

五年前，如果你給計算機一堆照片，除了按照大小分揀一下，它做不了太多的事情。一個十歲的孩子可以將照片按照男女分開，十五歲的孩子會分成酷的和不那麼酷的，實習生會說：「這個挺有意思。」今天，有了機器學習，計算機可以像十歲或者十五歲的孩子那樣完成任務。但可能永遠都到不了實習生的程度。不過，如果你有一百萬名十五歲的孩子查看數據的時候，你會怎麼做？你接聽哪通電話，看哪個圖像，檢查哪個文檔的傳輸或者哪筆信用卡付款？

如此，機器學習不需要比肩專家或者數十年積累的經驗和判斷。我們不是在將專家自動化。而是要求「聽聽所有來電，找出那些怒氣沖沖的人。」「讀讀所有郵件，找出那些讓人感覺焦慮不安的。」 「看看這幾十萬張照片，找到那些酷的（或者至少奇怪的）人。」

某種意義上講，這是自動化經常會完成的事情。Excel為我們提供的不是人工會計，Photoshop和Indesign提供的不是人工設計師，蒸汽機不是提供人工馬匹。（在早一點的人工智慧浪潮中，象棋計算機不是給我們一個裝在盒子里的，人到中年、脾氣糟糕的俄國大叔。） 而是，我們將一個個的任務批量自動化了。

Five years ago, if you gave a computer a pile of photos, it couldn』t do much more than sort them by size. A ten year old could sort them into men and women, a fifteen year old into cool and uncool and an intern could say 『this one』s really interesting』. Today, with ML, the computer will match the ten year old and perhaps the fifteen year old. It might never get to the intern. But what would you do if you had a million fifteen year olds to look at your data? What calls would you listen to, what images would you look at, and what file transfers or credit card payments would you inspect?

That is, machine learning doesn"t have to match experts or decades of experience or judgement. We』re not automating experts. Rather, we』re asking 『listen to all the phone calls and find the angry ones』. 『Read all the emails and find the anxious ones』. 『Look at a hundred thousand photos and find the cool (or at least weird) people』.

In a sense, this is what automation always does; Excel didn"t give us artificial accountants, Photoshop and Indesign didn』t give us artificial graphic designers and indeed steam engines didn』t give us artificial horses. (In an earlier wave of 『AI』, chess computers didn』t give us a grumpy middle-aged Russian in a box.) Rather, we automated one discrete task, at massive scale.

未來的十到十五年

這個比喻不能自洽（就像所有的比喻一樣）的地方就是在某些領域，機器學習不能只是找到那些我們已經可以辨識的東西，而是找到人類無法辨識的東西，或者找到圖案的層次，推斷或者隱喻，那些十歲孩子（或者五十歲）都不能辨識出的東西。最好的例子就是阿爾法狗。阿爾法狗不像計算機下象棋那樣下Go - 按照序列分析每一個可能走出的步法。而是，取得規則和棋盤之後，需要自己設計策略，自己和自己下比一個人一生里能夠下的多很多的棋局。如此，這不是一個非常非常快手的實習生頂得上一千個實習生的情況，你給你的實習生一千萬張圖像，他們回復你說「這是個很funny的東西。但是當我看到第三百萬張時，這個規律自己開始顯露出來。「 所以，問題是哪個領域足夠狹窄，我們可以為機器學習系統提供規則（或者給它一個分值），但是這個領域又足夠垂直，這樣機器學習可以查看所有的數據，完成之前依靠人完成不了的工作，揭示一些新的東西？

我花很多時間接觸大公司，了解他們技術上的需求。通常，他們會有一些垂手可得的機器學習方面的成果。有很多明顯的分析和優化問題，很多明顯是圖像識別以及語音分析的問題。同樣的，我們談論自動駕駛，混合現實的唯一原因是因為機器學習（也許）使它們成為可能 - 機器學習提供了一條路徑，讓汽車可以分辨周圍的物體，了解人類的司機可能做什麼，讓混合現實推斷出我應該看到什麼，如果我可以有一個可以看到一切事物的眼鏡。但是，在我們討論了布料上的褶皺或者電話中心的情感分析之後，它還會發現什麼我們不知道的東西？我們距離厭倦這個討論的時候，可能還有十到十五年。

Where this metaphor breaks down (as all metaphors do) is in the sense that in some fields, machine learning can not just find things we can already recognize, but find things that humans can』t recognize, or find levels of pattern, inference or implication that no ten year old (or 50 year old) would recognize. This is best seen Deepmind』s AlphaGo. AlphaGo doesn』t play Go the way the chess computers played chess - by analysing every possible tree of moves in sequence. Rather, it was given the rules and a board and left to try to work out strategies by itself, playing more games against itself than a human could do in many lifetimes. That is, this not so much a thousand interns as one intern that』s very very fast, and you give your intern 10 million images and they come back and say 『it』s a funny thing, but when I looked at the third million images, this pattern really started coming out』. So, what fields are narrow enough that we can tell an ML system the rules (or give it a score), but deep enough that looking at all of the data, as no human could ever do, might bring out new results?

I spend quite a lot of time meeting big companies and talking about their technology needs, and they generally have some pretty clear low hanging fruit for machine learning. There are lots of obvious analysis and optimisation problems, and plenty of things that are clearly image recognition problems or audio analysis questions. Equally, the only reason we』re talking about autonomous cars and mixed reality is because machine learning (probably) enables them - ML offers a path for cars to work out what』s around them and what human drivers might be going to do, and offers mixed reality a way to work out what I should be seeing, if I』m looking though a pair of glasses that could show anything. But after we』ve talked about wrinkles in fabric or sentiment analysis in the call center, these companies tend to sit back and ask, 『well, what else?』 What are the other things that this will enable, and what are the unknown unknowns that it will find? We』ve probably got ten to fifteen years before that starts getting boring.

英文作者個人主頁

https://www.ben-evans.com/benedictevans/2018/06/22/ways-to-think-about-machine-learning-8nefy

人類：留言請回復1

機器：留言請回復2

喜歡這篇文章嗎？立刻分享出去讓更多人知道吧！