TED英語(yǔ)演講：你以為你點(diǎn)的“贊”就是單純的“贊”嗎
你喜歡吃炸薯?xiàng)l嗎?你在面譜網(wǎng)上給過它們“贊”嗎?下面是小編為大家收集關(guān)于TED英語(yǔ)演講：你以為你點(diǎn)的“贊”就是單純的“贊”嗎，歡迎借鑒參考。
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(wǎng)絡(luò)時(shí)代的頭十年，網(wǎng)絡(luò)是一個(gè)水盡鵝飛的地方。你可以上網(wǎng)，你可以瀏覽網(wǎng)頁(yè)，當(dāng)時(shí)的網(wǎng)站要么是由某個(gè)組織的專門團(tuán)隊(duì)建立，要么就是由真正的技術(shù)行家所做，這就是當(dāng)時(shí)情況。
And with the rise of social media and social networks in the early 2000s, 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(jì)初隨著社交媒體以及社交網(wǎng)絡(luò)的興起，網(wǎng)絡(luò)發(fā)生了翻天覆地的變化：如今網(wǎng)絡(luò)上大部分的互動(dòng)內(nèi)容都是由大眾網(wǎng)絡(luò)用戶提供，既有Youtube視頻,也有博客文章，既有產(chǎn)品評(píng)論,也有社交媒體發(fā)布。與此同時(shí)，互聯(lián)網(wǎng)成為了一個(gè)有更多互動(dòng)的地方，人們?cè)谶@里互相交流、互相評(píng)論、互相分享，而不只是閱讀信息。
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(gè)你可以做這些事情的地方，但它確實(shí)是最大的一個(gè)，并且它用數(shù)字來(lái)證明這點(diǎn)。面譜網(wǎng)每個(gè)月有12億用戶。由此可見，地球上一半的互聯(lián)網(wǎng)用戶都在使用面譜網(wǎng)。這些都是網(wǎng)站，允許人們?cè)诰W(wǎng)上創(chuàng)建不同的角色，但這些人又不需要有多少計(jì)算機(jī)技能，而人們的反應(yīng)是在網(wǎng)上輸入大量的個(gè)人信息。
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(jié)果是，我們擁有數(shù)以億計(jì)人的行為信息、喜好信息以及人口數(shù)據(jù)資料。這在歷史上前所未有。對(duì)于作為計(jì)算機(jī)科學(xué)家的我來(lái)說，這意味著我能夠建立模型來(lái)預(yù)測(cè)各種各樣的你或許完全沒有意識(shí)到的與你所分享的信息相關(guān)的隱藏信息。
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(xué)家，我們利用這些信息來(lái)幫助人們?cè)诰W(wǎng)上交流。但也有人用此來(lái)謀取自己的私欲，而問題是，用戶并沒有真正理解其中用到的技術(shù)和技術(shù)的應(yīng)用方式。即便理解了，也不見得他們有話事權(quán)。所以，我今天想談?wù)勎覀兡軌蜃龅囊恍┦虑?，也啟發(fā)我們?nèi)绾胃纳魄闆r、讓話事權(quán)回歸用戶。
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(biāo)。我并不單單把那個(gè)商標(biāo)放在這個(gè)可憐的孕婦的肚子上?；蛟S在福布斯雜志上你看過這么一則趣事：塔吉特百貨公司給這個(gè)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(yōu)惠券。這一切發(fā)生在她把懷孕消息告訴父母的兩周前。
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(cuò)，女孩的父親很生氣。他說：”塔吉特是如何在連這個(gè)高中女生的父母都尚未知情之前就知道她懷孕了?“ 原來(lái)，塔吉特有成千上萬(wàn)的顧客，并擁有他們的購(gòu)買歷史記錄，他們用計(jì)算機(jī)推算出他們所謂的“懷孕分?jǐn)?shù)”，不僅能知道一個(gè)女性是否懷孕，而且還能計(jì)算出她的分娩日期。他們計(jì)算出的結(jié)果不單單是基于一些顯而易見的事情，比如說，她準(zhǔn)備買個(gè)嬰兒床或孩子的衣服，更是基于其他一些事情，例如她比平時(shí)多買了維他命，或她買了一個(gè)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 insights.So 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(dú)來(lái)看這些消費(fèi)記錄或許并不能說明什么，但這確是一種行為模式，當(dāng)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(dāng)我們根據(jù)社交媒體來(lái)預(yù)測(cè)關(guān)于你的一些事情時(shí)，這便是我們常做的一類事情。我們著眼于零星的行為模式，當(dāng)你在眾人中發(fā)現(xiàn)這些行為模式時(shí)，會(huì)幫助我們發(fā)現(xiàn)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(shí)驗(yàn)室，在同事們的合作下，我們已經(jīng)開發(fā)了一些機(jī)制來(lái)較為準(zhǔn)確地推測(cè)一些事情，比如你的政治立場(chǎng)、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(duì)認(rèn)識(shí)的人的信任程度、你的人際關(guān)系程度。我們能夠很好地完成這些推測(cè)。我在這里在強(qiáng)調(diào)一遍，這種推測(cè)并基于在你看來(lái)顯而易見的信息。
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(lái)自今年發(fā)表在美國(guó)國(guó)家論文集上的一個(gè)研究。你可以在谷歌搜索找到這篇文章。這篇文章總共四頁(yè)，容易閱讀。他們僅僅研究了人們?cè)贔acebook上的“贊”，也就是你在Facebook上喜歡的事情。他們利用這些數(shù)據(jù)來(lái)預(yù)測(cè)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(gè)“贊”。在這五項(xiàng)中贊“炸扭薯”頁(yè)面的是其中之一。炸扭薯很好吃，但喜歡吃炸扭薯并不一定意味著你比一般人聰明。那么為什么喜歡某個(gè)頁(yè)面就成為顯示你智商的重要因素，盡管該頁(yè)面的內(nèi)容和所預(yù)測(cè)的屬性與此毫不相干?事實(shí)是我們必須審視大量的基礎(chǔ)理論，從而了解我們是如何做到準(zhǔn)確推測(cè)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(gè)基礎(chǔ)理論是社會(huì)學(xué)的同質(zhì)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，你傾向于和聰明的人交朋友。如果你還年輕，你傾向于和年輕人交朋友。這是數(shù)百年來(lái)公認(rèn)的理論。我們很清楚信息在網(wǎng)絡(luò)上傳播的傳播途徑。結(jié)果是，流行的視頻、臉書上得到很多“贊”的內(nèi)容、或者其他信息的傳播，同疾病在社交網(wǎng)絡(luò)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 happen.So 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?cè)谶@方面已經(jīng)研究很久了，我們己經(jīng)建立了很好的模型。你能夠?qū)⑺羞@些事物放在一起，看看為什么這樣的事情會(huì)發(fā)生。如果要我給你一個(gè)假說的話，我會(huì)猜測(cè)一個(gè)聰明的人建立了這個(gè)頁(yè)面，或者第一個(gè)喜歡這個(gè)頁(yè)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(gè)頁(yè)面，然后他們的朋友看到了，根據(jù)同質(zhì)性理論，我們知道這些人可能有聰明的朋友， 然后他們看到這類信息，他們中的一部分人也喜歡，他們也有聰明的朋友，所以這類信息也傳到其他朋友那里，所以信息就在網(wǎng)絡(luò)上在聰明人的圈子里流傳開來(lái)了，因此到了最后，喜歡炸扭薯的這個(gè)頁(yè)面就成了高智商的象征，而不是因?yàn)閮?nèi)容本身，而是“喜歡”這一個(gè)實(shí)際行動(dòng)反映了那些也付諸同樣行動(dòng)的人的相同特征。
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(lái)很復(fù)雜，對(duì)吧?對(duì)于一般用戶來(lái)說它比較難解釋清楚，就算你解釋清楚了，一般用戶又能利用它來(lái)干嘛呢?你又怎么能知道你喜歡的事情反映了你什么特征，而且這個(gè)特征還和你喜歡的內(nèi)容毫不相干呢?用戶其實(shí)沒有太多的能力去控制這些數(shù)據(jù)的使用。我把這個(gè)看作將來(lái)的真實(shí)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(rèn)為，要是我們想讓用戶擁有使用這些數(shù)據(jù)的能力，那么有幾條路徑我們需要探究，因?yàn)檫@些數(shù)據(jù)并不總是用來(lái)為他們謀利益。這有一個(gè)我經(jīng)常舉的例子，如果我厭倦了當(dāng)一名教授，我會(huì)選擇自己開家公司這家公司能預(yù)測(cè)這些特性和事物，例如你在團(tuán)隊(duì)里的能力，例如你是否是一個(gè)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(yù)測(cè)這些特性，然后我就會(huì)把這些報(bào)告賣給那些人力資源公司和想要雇傭你的大公司。我們完全可以做到這點(diǎn)。我明天就能開始這個(gè)項(xiàng)目，并且你對(duì)我這用使用你的數(shù)據(jù)是一點(diǎn)辦法也沒有的。這對(duì)我來(lái)說是一個(gè)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(shí)上我們將不得不這么做。觀察我們目前的政治進(jìn)程讓我覺得在美國(guó)，把一幫代表們聚在一起，讓他們坐下來(lái)理解這個(gè)問題，然后頒布有關(guān)知識(shí)產(chǎn)權(quán)法方面的顛覆性條例，讓用戶掌控自己的數(shù)據(jù)，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 data.You 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(huì)告訴你，你知道嗎?你的確擁有你的數(shù)據(jù)。你絕對(duì)能自己決定要怎么去用。但問題在于大部分的社交媒體公司，他們的盈利模式在某方面取決于分享或挖掘用戶的數(shù)據(jù)資料。所以有時(shí)會(huì)說面譜網(wǎng)的用戶并不是顧客，而是產(chǎn)品。那么你要怎樣讓一個(gè)公司將他們的主要資產(chǎn)控制權(quán)雙手拱讓給用戶呢?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 science.It'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(rèn)為我們得走另一條途徑，一條更有效的途徑，一條更加科學(xué)的途徑。這途徑是開發(fā)一種技術(shù)讓我們能夠發(fā)展所有這些機(jī)制來(lái)首先處理自己的個(gè)人信息資料。而這很接近我們必須做的研究，要是我們想要發(fā)展這些機(jī)制跟用戶說明，“這樣做你需要承擔(dān)那樣的風(fēng)險(xiǎn)。” 你在Facebook上點(diǎn)“贊” 或者分享一些私人信息，就相當(dāng)于增強(qiáng)了我的能力去預(yù)測(cè)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 altogether.We 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(wǎng)上只字不提。我們也可以探究一些別的，例如，讓人們?nèi)ソo上傳的東西加密，那么像面譜網(wǎng)這樣的網(wǎng)站或其他能獲取信息的第三方來(lái)說，這些信息就隱秘很多，也少了很多意義，而且只有上傳人指定的用戶才有瀏覽的權(quán)限。從智能的角度來(lái)看，這是一個(gè)非常振奮人心的研究，而且科學(xué)家們也會(huì)樂意去做這樣的事。這樣在法律方面，我們就有優(yōu)勢(shì)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(dāng)我談?wù)摰竭@個(gè)話題時(shí)，人們提到的其中一個(gè)問題，就是如果當(dāng)人們開始把這些數(shù)據(jù)進(jìn)行保密，那些你研發(fā)的用來(lái)預(yù)測(cè)人們特性的手段都會(huì)作廢。我會(huì)說，絕對(duì)會(huì)作廢，但對(duì)我來(lái)說，這是成功，因?yàn)樽鳛橐粋€(gè)科學(xué)家，我的目標(biāo)不是去推測(cè)出用戶的信息，而是提高人們?cè)诰W(wǎng)上互動(dòng)的方式。雖然有時(shí)涉及到推測(cè)用戶的資料，但如果用戶不希望我們用他們的數(shù)據(jù)，我覺得他們應(yīng)該有權(quán)去拒絕。我希望用戶能被告知并且贊同我們開發(fā)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(rèn)為，鼓勵(lì)這類科學(xué)，支持這些研究者們這些愿意放棄部分控制，退還給用戶們，并且不讓社交媒體公司接觸數(shù)據(jù)的研究者們。隨著這些工具的進(jìn)化和提高，這一切意味著向前的發(fā)展，意味著我們將會(huì)擁有一個(gè)有素質(zhì)有權(quán)力的用戶基礎(chǔ)，我覺得我們都會(huì)同意這是一個(gè)理想的前進(jìn)目標(biāo)。
Thank you.(Applause)
謝謝。(掌聲)
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