2023年TED英语演讲你以为你点的赞就是单纯的赞.docx

此资料由网络收集而来，如有侵权请告知上传者立即删除。资料共分享，我们负责传递知识。 TED英语演讲：你以为你点的“赞〞就是单纯的“赞〞吗 　　你喜欢吃炸薯条吗你在面谱网上给过它们〞;赞〞;吗下面是小编为大家收集关于TED英语演讲：你以为你点的〞;赞〞;就是单纯的〞;赞〞;吗，欢迎借鉴参考。 　　演说题目：Your social media &likes& expose more than you think 　　演说者：Jennifer Golbeck 　　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 individuals who were really tech-savvy for the time. 　　如果你还记得网络时代的头十年，网络是一个水尽鹅飞的地方。你可以上网，你可以浏览网页，当时的网站要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 2023s, 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. 　　但在二十一世纪初随着社交媒体以及社交网络的兴起，网络发生了翻天覆地的变化：如今网络上大局部的互动内容都是由群众网络用户提供，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，互联网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、互相分享，而不只是阅读信息。 　　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。面谱网每个月有12亿用户。由此可见，地球上一半的互联网用户都在使用面谱网。这些都是网站，允许人们在网上创立不同的角色，但这些人又不需要有多少计算机技能，而人们的反响是在网上输入大量的个人信息。 　　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. 　　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 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. 　　作为科学家，我们利用这些信息来帮助人们在网上交流。但也有人用此来谋取自己的私欲，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 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. 　　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那么趣事：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 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. 　　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，并拥有他们的购置历史记录，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，比方说，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 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. 　　单独来看这些消费记录或许并不能说明什么，但这确是一种行为模式，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, 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