TED英语演讲你以为你点的赞就是单纯的赞吗文档格式.docx

1、 演讲稿 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 inpiduals who were really tech-savvy for the time. 如果你还记得#络时代的头十年，#络是一个水尽鹅飞的地方。你可以上#，你可以浏览#页，当时的#站要

2、么是由某个组织的专门团队建立，要么就是由真正的技术行家所做，这就是当时情况。 And with the rise of social media and social networks in the early 20xxs, the web was pletely 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 soc

3、ial media postings. And its also bee a much more interactive place, where people are interacting with others, theyre menting, theyre sharing, theyre not just reading. 但在二十一世纪初随着社交媒体以及社交#络的兴起，#络发生了翻天覆地的变化：如今#络上大部分的互动内容都是由大众#络用户提供，既有Youtube视频,也有博客文章，既有产品评论,也有社交媒体发布。与此同时，互联#成为了一个有更多互动的地方，人们在这里互相交流、互相评论

4、、互相分享，而不只是阅读信息。 So Facebook is not the only place you can do this, but its the biggest, and it serves to illustrate the numbers. Facebook has billion users per month. So half the Earths Internet population is using Facebook. They are a site, along with others, that has allowed people to create an on

5、line persona with very little technical skill, and people responded by putting huge amounts of personal data online. Facebook不是唯一一个你可以做这些事情的地方，但它确实是最大的一个，并且它用数字来证明这点。面谱#每个月有12亿用户。由此可见，地球上一半的互联#用户都在使用面谱#。这些都是#站，允许人们在#上创建不同的角色，但这些人又不需要有多少计算机技能，而人们的反应是在#上输入大量的个人信息。 So the result is that we have behavio

6、ral, preference, demographic data for hundreds of millions of people, which is unprecedented in history. And as a puter scientist, what this means is that Ive been able to build models that can predict all sorts of hidden attributes for all of you that you dont even know youre sharing information ab

7、out. 结果是，我们拥有数以亿计人的行为信息、喜好信息以及人口数据资料。这在历史上前所未有。对于作为计算机科学家的我来说，这意味着我能够建立模型来预测各种各样的你或许完全没有意识到的与你所分享的信息相关的隐藏信息。 As scientists, we use that to help the way people interact online, but theres less altruistic applications, and theres a problem in that users dont really understand these techniques and how

8、they work, and even if they did, they dont have a lot of control over it. So what I want to talk to you about today is some of these things that were 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. 作为科学家，我们利用这些信息来帮助人们在#上交流。但也有人用此来

9、谋取自己的私欲，而问题是，用户并没有真正理解其中用到的技术和技术的应用方式。即便理解了，也不见得他们有话事权。所以，我今天想谈谈我们能够做的一些事情，也启发我们如何改善情况、让话事权回归用户。 So this is Target, the pany. I didnt just put that logo on this poor, pregnant womans belly. You may have seen this anecdote that was printed in Forbes magazine where Target sent a flyer to this 15-year-

10、old girl with advertisements and coupons for baby bottles and diapers and cribs two weeks before she told her parents that she was pregnant. 这是塔吉特百货公司的商标。我并不单单把那个商标放在这个可怜的孕妇的肚子上。或许在福布斯杂志上你看过这么一则趣事：塔吉特百货公司给这个15岁女孩寄了一份传单，传单上都是婴儿奶瓶、尿布、婴儿床的广告和优惠券。这一切发生在她把怀孕消息告诉父母的两周前。 Yeah, the dad was really upset. He

11、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 pute what they call a pregnancy score, which is not just whether or not a womans pregnant, but what he

12、r due date is. And they pute that not by looking at the obvious things, like, shes buying a crib or baby clothes, but things like, she bought more vitamins than she normally had, or she bought a handbag thats big enough to hold diapers. 没错，女孩的父亲很生气。他说：塔吉特是如何在连这个高中女生的父母都尚未知情之前就知道她怀孕了? 原来，塔吉特有成千上万的顾客，

13、并拥有他们的购买历史记录，他们用计算机推算出他们所谓的怀孕分数，不仅能知道一个女性是否怀孕，而且还能计算出她的分娩日期。他们计算出的结果不单单是基于一些显而易见的事情，比如说，她准备买个婴儿床或孩子的衣服，更是基于其他一些事情，例如她比平时多买了维他命，或她买了一个新的手提包大得可以放尿布。 And by themselves, those purchases dont seem like they might reveal a lot, but its a pattern of behavior that, when you take it in the context of thousan

14、ds of other people, starts to actually reveal some thats the kind of thing that we do when were predicting stuff about you on social media. Were looking for little patterns of behavior that, when you detect them among millions of people, lets us find out all kinds of things. 单独来看这些消费记录或许并不能说明什么，但这确是

15、一种行为模式，当你有大量人口背景作比较，这种行为模式就开始透露一些见解。当我们根据社交媒体来预测关于你的一些事情时，这便是我们常做的一类事情。我们着眼于零星的行为模式，当你在众人中发现这些行为模式时，会帮助我们发现各种各样的事情。 So in my lab and with colleagues, weve developed mechanisms where we can quite accurately predict things like your political preference, your personality score, gender, sexual orientat

16、ion, 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 doesnt e from what you might think of as obvious information. 在我的实验室，在同事们的合作下，我们已经开发了一些机制来较为准确地推测一些事情，比如你的政治立场、你的性格

17、得分、性别、性取向、宗教信仰、年龄、智商，另外还有：你对认识的人的信任程度、你的人际关系程度。我们能够很好地完成这些推测。我在这里在强调一遍，这种推测并基于在你看来显而易见的信息。 So my favorite example is from this study that was published this year in the Proceedings of the National Academies. If you Google this, youll find it. Its four pages, easy to read. And they looked at just peo

18、ples Facebook likes, so just the things you like on Facebook, and used that to predict all these attributes,along with some other ones. 我最喜欢的例子是来自今年发表在美国国家论文集上的一个研究。你可以在谷歌搜索找到这篇文章。这篇文章总共四页，容易阅读。他们仅仅研究了人们在Facebook上的赞，也就是你在Facebook上喜欢的事情。他们利用这些数据来预测之前所说的所有特性，还有其他的一些特性。 And in their paper they listed t

19、he 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 youre smarter than the average person. So how is it that one of the strongest indicators of your intel

20、ligence is liking this page when the content is totally irrelevant to the attribute thats being predicted? And it turns out that we have to look at a whole bunch of underlying theories to see why were able to do this. 在文章中列举了最能够显示高智商的五个赞。在这五项中赞炸扭薯页面的是其中之一。炸扭薯很好吃，但喜欢吃炸扭薯并不一定意味着你比一般人聪明。那么为什么喜欢某个页面就成为显

21、示你智商的重要因素，尽管该页面的内容和所预测的属性与此毫不相干?事实是我们必须审视大量的基础理论，从而了解我们是如何做到准确推测的。 One of them is a sociological theory called homophily, which basically says people are friends with people like them. So if youre smart, you tend to be friends with smart people, and if youre young, you tend to be friends with young

22、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. 其中一个基础理论是社会学的同质性理论，

23、主要意思是人们和自己相似的人交朋友。所以说，如果你很聪明，你倾向于和聪明的人交朋友。如果你还年轻，你倾向于和年轻人交朋友。这是数百年来公认的理论。我们很清楚信息在#络上传播的传播途径。结果是，流行的视频、脸书上得到很多赞的内容、或者其他信息的传播，同疾病在社交#络中蔓延的方式是相同的。 So this is something weve 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

24、 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. 我们在这方面已经研究很久了，我们己经建立了很好的模型。你能够将所有这些事物放在一起，看看为什么这样的事情会发生。如果要我给你一个假说的话，我会猜测一个聪明的人建立了这个页面，或者第一个喜欢这个页面的人拥有挺高的智商得分。 And they liked it, an

25、d 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

26、 the curly fries page is indicative of high intelligence, not because of the content, but because the actual action of liking reflects back the mon attributes of other people who have done it. 他们喜欢了这个页面，然后他们的朋友看到了，根据同质性理论，我们知道这些人可能有聪明的朋友， 然后他们看到这类信息，他们中的一部分人也喜欢，他们也有聪明的朋友，所以这类信息也传到其他朋友那里，所以信息就在#络上在聪明

27、人的圈子里流传开来了，因此到了最后，喜欢炸扭薯的这个页面就成了高智商的象征，而不是因为内容本身，而是喜欢这一个实际行动反映了那些也付诸同样行动的人的相同特征。 So this is pretty plicated stuff, right? Its 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 youve liked somethingthat indicates a

28、trait for you thats totally irrelevant to the content of what youve liked? Theres a lot of power that users dont have to control how this data is used. And I see that as a real problem going forward. 听起来很复杂，对吧?对于一般用户来说它比较难解释清楚，就算你解释清楚了，一般用户又能利用它来干嘛呢?你又怎么能知道你喜欢的事情反映了你什么特征，而且这个特征还和你喜欢的内容毫不相干呢?用户其实没有太多

29、的能力去控制这些数据的使用。我把这个看作将来的真实问题。 So I think theres a couple paths that we want to look at if we want to give users some control over how this data is used, because its not always going to be used for their benefit. An example I often give is that, if I ever get bored being a professor, Im going to go st

30、art a pany that predicts all of these attributes and things like how well you work in teams and if youre a drug user, if youre an alcoholic. 我认为，要是我们想让用户拥有使用这些数据的能力，那么有几条路径我们需要探究，因为这些数据并不总是用来为他们谋利益。这有一个我经常举的例子，如果我厌倦了当一名教授，我会选择自己开家公司这家公司能预测这些特性和事物，例如你在团队里的能力，例如你是否是一个吸毒者或酗酒者。 We know how to predict al

31、l that. And Im going to sell reports to . panies 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. 我们知道如何去预测这些特性，然后我就会把这些报告卖给那些人力资源公

32、司和想要雇佣你的大公司。我们完全可以做到这点。我明天就能开始这个项目，并且你对我这用使用你的数据是一点办法也没有的。这对我来说是一个问题。 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 wed actually have to do it. Observing our political process in action makes me think its highly unlikely that were going to get a bunch of representatives to sit down, learn about this, and then enact sweep