TED英語演講：你以為你點的“贊”就是單純的“贊”嗎

你喜歡吃炸薯條嗎?你在面譜網上給過它們“贊”嗎?下面是小編為大家收集關於TED英語演講：你以為你點的“贊”就是單純的“贊”嗎，歡迎借鑑參考。

演說題目：Your social media "likes" expose more than you think

演說者：Jennifer Golbeck

演講稿

If you remember that first decade of the web, it was really a static place. You could go online, you could look at pages, and they were put up either by organizations who had teams to do it or by individuals who were really tech-savvy for the time.

如果你還記得網路時代的頭十年，網路是一個水盡鵝飛的地方。你可以上網，你可以瀏覽網頁，當時的網站要麼是由某個組織的專門團隊建立，要麼就是由真正的技術行家所做，這就是當時情況。

And with the rise of social media and social networks in the early 20xxs, the web was completely changed to a place where now the vast majority of content we interact with is put up by average users, either in YouTube videos or blog posts or product reviews or social media postings. And it's also become a much more interactive place, where people are interacting with others, they're commenting, they're sharing, they're not just reading.

但在二十一世紀初隨著社交媒體以及社交網路的興起，網路發生了翻天覆地的變化：如今網路上大部分的互動內容都是由大眾網路使用者提供，既有Youtube視訊,也有部落格文章，既有產品評論,也有社交媒體釋出。與此同時，網際網路成為了一個有更多互動的地方，人們在這裡互相交流、互相評論、互相分享，而不只是閱讀資訊。

So Facebook is not the only place you can do this, but it's the biggest, and it serves to illustrate the numbers. Facebook has 1.2 billion users per month. So half the Earth's Internet population is using Facebook. They are a site, along with others, that has allowed people to create an online persona with very little technical skill, and people responded by putting huge amounts of personal data online.

Facebook不是唯一一個你可以做這些事情的地方，但它確實是最大的一個，並且它用數字來證明這點。面譜網每個月有12億使用者。由此可見，地球上一半的網際網路使用者都在使用面譜網。這些都是網站，允許人們在網上建立不同的角色，但這些人又不需要有多少計算機技能，而人們的反應是在網上輸入大量的個人資訊。

So the result is that we have behavioral, preference, demographic data for hundreds of millions of people, which is unprecedented in history. And as a computer scientist, what this means is that I've been able to build models that can predict all sorts of hidden attributes for all of you that you don't even know you're sharing information about.

結果是，我們擁有數以億計人的行為資訊、喜好資訊以及人口資料資料。這在歷史上前所未有。對於作為電腦科學家的我來說，這意味著我能夠建立模型來預測各種各樣的你或許完全沒有意識到的與你所分享的資訊相關的隱藏資訊。

As scientists, we use that to help the way people interact online, but there's less altruistic applications, and there's a problem in that users don't really understand these techniques and how they work, and even if they did, they don't have a lot of control over it. So what I want to talk to you about today is some of these things that we're able to do, and then give us some ideas of how we might go forward to move some control back into the hands of users.

作為科學家，我們利用這些資訊來幫助人們在網上交流。但也有人用此來謀取自己的私慾，而問題是，使用者並沒有真正理解其中用到的技術和技術的應用方式。即便理解了，也不見得他們有話事權。所以，我今天想談談我們能夠做的一些事情，也啟發我們如何改善情況、讓話事權迴歸使用者。

So this is Target, the company. I didn't just put that logo on this poor, pregnant woman's belly. You may have seen this anecdote that was printed in Forbes magazine where Target sent a flyer to this 15-year-old girl with advertisements and coupons for baby bottles and diapers and cribs two weeks before she told her parents that she was pregnant.

這是塔吉特百貨公司的商標。我並不單單把那個商標放在這個可憐的孕婦的肚子上。或許在福布斯雜誌上你看過這麼一則趣事：塔吉特百貨公司給這個15歲女孩寄了一份傳單，傳單上都是嬰兒奶瓶、尿布、嬰兒床的廣告和優惠券。這一切發生在她把懷孕訊息告訴父母的兩週前。

Yeah, the dad was really upset. He said, "How did Target figure out that this high school girl was pregnant before she told her parents?" It turns out that they have the purchase history for hundreds of thousands of customers and they compute what they call a pregnancy score, which is not just whether or not a woman's pregnant, but what her due date is. And they compute that not by looking at the obvious things, like, she's buying a crib or baby clothes, but things like, she bought more vitamins than she normally had, or she bought a handbag that's big enough to hold diapers.

沒錯，女孩的父親很生氣。他說：”塔吉特是如何在連這個高中女生的父母都尚未知情之前就知道她懷孕了?“ 原來，塔吉特有成千上萬的顧客，並擁有他們的購買歷史記錄，他們用計算機推算出他們所謂的“懷孕分數”，不僅能知道一個女性是否懷孕，而且還能計算出她的分娩日期。他們計算出的結果不單單是基於一些顯而易見的事情，比如說，她準備買個嬰兒床或孩子的衣服，更是基於其他一些事情，例如她比平時多買了維他命，或她買了一個新的手提包大得可以放尿布。

And by themselves, those purchases don't seem like they might reveal a lot, but it's a pattern of behavior that, when you take it in the context of thousands of other people, starts to actually reveal some that's the kind of thing that we do when we're predicting stuff about you on social media. We're looking for little patterns of behavior that, when you detect them among millions of people, lets us find out all kinds of things.

單獨來看這些消費記錄或許並不能說明什麼，但這確是一種行為模式，當你有大量人口背景作比較，這種行為模式就開始透露一些見解。當我們根據社交媒體來預測關於你的一些事情時，這便是我們常做的一類事情。我們著眼於零星的行為模式，當你在眾人中發現這些行為模式時，會幫助我們發現各種各樣的事情。

So in my lab and with colleagues, we've developed mechanisms where we can quite accurately predict things like your political preference, your personality score, gender, sexual orientation, religion, age, intelligence, along with things like how much you trust the people you know and how strong those relationships are. We can do all of this really well. And again, it doesn't come from what you might think of as obvious information.

在我的實驗室，在同事們的合作下，我們已經開發了一些機制來較為準確地推測一些事情，比如你的政治立場、你的性格得分、性別、性取向、宗教信仰、年齡、智商，另外還有：你對認識的人的信任程度、你的人際關係程度。我們能夠很好地完成這些推測。我在這裡在強調一遍，這種推測並基於在你看來顯而易見的資訊。

So my favorite example is from this study that was published this year in the Proceedings of the National Academies. If you Google this, you'll find it. It's four pages, easy to read. And they looked at just people's Facebook likes, so just the things you like on Facebook, and used that to predict all these attributes,along with some other ones.

我最喜歡的例子是來自今年發表在美國國家論文集上的一個研究。你可以在谷歌搜尋找到這篇文章。這篇文章總共四頁，容易閱讀。他們僅僅研究了人們在Facebook上的“贊”，也就是你在Facebook上喜歡的事情。他們利用這些資料來預測之前所說的所有特性，還有其他的一些特性。

And in their paper they listed the five likes that were most indicative of high intelligence. And among those was liking a page for curly fries. (Laughter) Curly fries are delicious, but liking them does not necessarily mean that you're smarter than the average person. So how is it that one of the strongest indicators of your intelligence is liking this page when the content is totally irrelevant to the attribute that's being predicted? And it turns out that we have to look at a whole bunch of underlying theories to see why we're able to do this.

在文章中列舉了最能夠顯示高智商的五個“贊”。在這五項中贊“炸扭薯”頁面的是其中之一。炸扭薯很好吃，但喜歡吃炸扭薯並不一定意味著你比一般人聰明。那麼為什麼喜歡某個頁面就成為顯示你智商的重要因素，儘管該頁面的內容和所預測的屬性與此毫不相干?事實是我們必須審視大量的基礎理論，從而瞭解我們是如何做到準確推測的。

One of them is a sociological theory called homophily, which basically says people are friends with people like them. So if you're smart, you tend to be friends with smart people, and if you're young, you tend to be friends with young people, and this is well establishedfor hundreds of years. We also know a lot about how information spreads through networks. It turns out things like viral videos or Facebook likes or other information spreads in exactly the same way that diseases spread through social networks.

其中一個基礎理論是社會學的同質性理論，主要意思是人們和自己相似的人交朋友。所以說，如果你很聰明，你傾向於和聰明的人交朋友。如果你還年輕，你傾向於和年輕人交朋友。這是數百年來公認的理論。我們很清楚資訊在網路上傳播的傳播途徑。結果是，流行的視訊、臉書上得到很多“贊”的內容、或者其他資訊的傳播，同疾病在社交網路中蔓延的方式是相同的。

So this is something we've studied for a long time. We have good models of it. And so you can put those things together and start seeing why things like this if I were to give you a hypothesis, it would be that a smart guy started this page, or maybe one of the first people who liked it would have scored high on that test.

我們在這方面已經研究很久了，我們己經建立了很好的模型。你能夠將所有這些事物放在一起，看看為什麼這樣的事情會發生。如果要我給你一個假說的話，我會猜測一個聰明的人建立了這個頁面，或者第一個喜歡這個頁面的人擁有挺高的智商得分。

And they liked it, and their friends saw it,and by homophily, we know that he probably had smart friends, and so it spread to them, and some of them liked it, and they had smart friends, and so it spread to them, and so it propagated through the network to a host of smart people, so that by the end, the action of liking the curly fries page is indicative of high intelligence, not because of the content, but because the actual action of liking reflects back the common attributes of other people who have done it.

他們喜歡了這個頁面，然後他們的朋友看到了，根據同質性理論，我們知道這些人可能有聰明的朋友， 然後他們看到這類資訊，他們中的一部分人也喜歡，他們也有聰明的朋友，所以這類資訊也傳到其他朋友那裡，所以資訊就在網路上在聰明人的圈子裡流傳開來了，因此到了最後，喜歡炸扭薯的這個頁面就成了高智商的象徵，而不是因為內容本身，而是“喜歡”這一個實際行動反映了那些也付諸同樣行動的人的相同特徵。

So this is pretty complicated stuff, right? It's a hard thing to sit down and explain to an average user, and even if you do, what can the average user do about it? How do you know that you've liked somethingthat indicates a trait for you that's totally irrelevant to the content of what you've liked? There's a lot of power that users don't have to control how this data is used. And I see that as a real problem going forward.

聽起來很複雜，對吧?對於一般使用者來說它比較難解釋清楚，就算你解釋清楚了，一般使用者又能利用它來幹嘛呢?你又怎麼能知道你喜歡的事情反映了你什麼特徵，而且這個特徵還和你喜歡的內容毫不相干呢?使用者其實沒有太多的能力去控制這些資料的使用。我把這個看作將來的真實問題。

So I think there's a couple paths that we want to look at if we want to give users some control over how this data is used, because it's not always going to be used for their benefit. An example I often give is that, if I ever get bored being a professor, I'm going to go start a company that predicts all of these attributes and things like how well you work in teams and if you're a drug user, if you're an alcoholic.

我認為，要是我們想讓使用者擁有使用這些資料的能力，那麼有幾條路徑我們需要探究，因為這些資料並不總是用來為他們謀利益。這有一個我經常舉的例子，如果我厭倦了當一名教授，我會選擇自己開家公司這家公司能預測這些特性和事物，例如你在團隊裡的能力，例如你是否是一個吸毒者或酗酒者。

We know how to predict all that. And I'm going to sell reports to H.R. companies and big businesses that want to hire you. We totally can do that now. I could start that business tomorrow, and you would have absolutely no control over me using your data like that. That seems to me to be a problem.

我們知道如何去預測這些特性，然後我就會把這些報告賣給那些人力資源公司和想要僱傭你的大公司。我們完全可以做到這點。我明天就能開始這個專案，並且你對我這用使用你的資料是一點辦法也沒有的。這對我來說是一個問題。

So one of the paths we can go down is the policy and law path. And in some respects, I think that that would be most effective, but the problem is we'd actually have to do it. Observing our political process in action makes me think it's highly unlikely that we're going to get a bunch of representatives to sit down, learn about this, and then enact sweeping changes to intellectual property law in the U.S. so users control their data.

所以我們可選的其中一條路徑是政策和法律這條途徑。某程度上我覺得這可能是最有效的。但問題是，事實上我們將不得不這麼做。觀察我們目前的政治程序讓我覺得在美國，把一幫代表們聚在一起，讓他們坐下來理解這個問題，然後頒佈有關智慧財產權法方面的顛覆性條例，讓使用者掌控自己的資料，這似乎是不可能的。

We could go the policy route, where social media companies say, you know what? You own your have total control over how it's used. The problem is that the revenue models for most social media companies rely on sharing or exploiting users' data in some way. It's sometimes said of Facebook that the users aren't the customer, they're the product. And so how do you get a company to cede control of their main asset back to the users? It's possible, but I don't think it's something that we're going to see change quickly.

我們可以走政策途徑，這樣社交媒體公司就會告訴你，你知道嗎?你的確擁有你的資料。你絕對能自己決定要怎麼去用。但問題在於大部分的社交媒體公司，他們的盈利模式在某方面取決於分享或挖掘使用者的資料資料。所以有時會說面譜網的使用者並不是顧客，而是產品。那麼你要怎樣讓一個公司將他們的主要資產控制權雙手拱讓給使用者呢?這是可能的，但我不覺得我們能很快見證這種改變。

So I think the other path that we can go down that's going to be more effective is one of more 's doing science that allowed us to develop all these mechanisms for computing this personal data in the first place. And it's actually very similar research that we'd have to do if we want to develop mechanisms that can say to a user, "Here's the risk of that action you just took." By liking that Facebook page, or by sharing this piece of personal information, you've now improved my ability to predict whether or not you're using drugs or whether or not you get along well in the workplace.

所以我認為我們得走另一條途徑，一條更有效的途徑，一條更加科學的途徑。這途徑是開發一種技術讓我們能夠發展所有這些機制來首先處理自己的個人資訊資料。而這很接近我們必須做的研究，要是我們想要發展這些機制跟使用者說明，“這樣做你需要承擔那樣的風險。” 你在Facebook上點“贊” 或者分享一些私人資訊，就相當於增強了我的能力去預測你是不是在吸毒或者你在工作中是否順利。

And that, I think, can affect whether or not people want to share something, keep it private, or just keep it offline can also look at things like allowing people to encrypt data that they upload, so it's kind of invisible and worthless to sites like Facebook or third party services that access it, but that select users who the person who posted it want to see it have access to see it. This is all super exciting research from an intellectual perspective, and so scientists are going to be willing to do it. So that gives us an advantage over the law side.

我覺得，這樣做能夠影響人們分享的決定：是要保持私隱，還是在網上隻字不提。我們也可以探究一些別的，例如，讓人們去給上傳的東西加密，那麼像面譜網這樣的網站或其他能獲取資訊的第三方來說，這些資訊就隱祕很多，也少了很多意義，而且只有上傳人指定的使用者才有瀏覽的許可權。從智慧的角度來看，這是一個非常振奮人心的研究，而且科學家們也會樂意去做這樣的事。這樣在法律方面，我們就有優勢了。

One of the problems that people bring up when I talk about this is, they say, you know, if people start keeping all this data private, all those methods that you've been developing to predict their traits are going to fail. And I say, absolutely, and for me, that's success, because as a scientist, my goal is not to infer information about users, it's to improve the way people interact online. And sometimes that involves inferring things about them, but if users don't want me to use that data, I think they should have the right to do that. I want users to be informed and consenting users of the tools that we develop.

當我談論到這個話題時，人們提到的其中一個問題，就是如果當人們開始把這些資料進行保密，那些你研發的用來預測人們特性的手段都會作廢。我會說，絕對會作廢，但對我來說，這是成功，因為作為一個科學家，我的目標不是去推測出使用者的資訊，而是提高人們在網上互動的方式。雖然有時涉及到推測使用者的資料，但如果使用者不希望我們用他們的資料，我覺得他們應該有權去拒絕。我希望使用者能被告知並且贊同我們開發的這種工具。

And so I think encouraging this kind of science and supporting researchers who want to cede some of that control back to users and away from the social media companies means that going forward, as these tools evolve and advance, means that we're going to have an educated and empowered user base,and I think all of us can agree that that's a pretty ideal way to go forward.

所以我認為，鼓勵這類科學，支援這些研究者們這些願意放棄部分控制，退還給使用者們，並且不讓社交媒體公司接觸資料的研究者們。隨著這些工具的進化和提高，這一切意味著向前的發展，意味著我們將會擁有一個有素質有權力的使用者基礎，我覺得我們都會同意這是一個理想的前進目標。

Thank you.(Applause)

謝謝。(掌聲)