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[文化博览] 【整理】2011-09-05 虚拟革命 免费的代价 The Cost of Free —16

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[文化博览] 【整理】2011-09-05 虚拟革命 免费的代价 The Cost of Free —16

 

 

虚拟革命 免费的代价  | The Virtual Revolution


    一个沉默的故事,一场无声的革命。影响了地球上的每个人。网络发明后,20多年过去了。我们一起探讨网络带来的深远影响——无论好坏,数字革命是如何改变了人类的生活呢?记者兼大学教师Aleks Krotoski博士走访全球,研究网络改变一切的意义,包括我们如何学习、购物、投票、交友等等。目前全球有四分之一的人上网,一起探讨当世界剩下的四分之三的人将要上网时,我们的网络又为他们准备了什么呢?互联网是免费的,但是有代价的!本期节目就google为例,为你揭示天下没有免费的午餐。而类似亚马逊网站的推荐引擎,可以建立用户数据库,那么,个人隐私是否受侵害呢?

  

   20多年前,英国人蒂姆·博纳斯李发明了互联网。“只是因为我自己需要”他对BBC说。从那时起世界不再是以前的世界。这20年在世界历史上转瞬即逝,但全球互联网却在这20年间高速发展。网络改变了全世界的社会组织形式。社会上越来越多的部门,以爆炸性的速度并通过各种形式与网络联系在一起。

 

In the third programme of the series, Aleks gives the lowdown on how, for better and for worse, commerce has colonised the web - and reveals how web users are paying for what appear to be 'free' sites and services in hidden ways. Joined by some of the most influential business leaders of today's web, including Jeff Bezos (CEO of Amazon), Eric Schmidt (CEO of Google), Chad Hurley (CEO of YouTube), Bill Gates, Martha Lane Fox and Reed Hastings (CEO of Netflix), Aleks traces how business, with varying degrees of success, has attempted to make money on the web. She tells the inside story of the gold rush years of the dotcom bubble and reveals how retailers such as Amazon learned the lessons. She also charts how, out of the ashes, Google forged the business model that has come to dominate today's web, offering a plethora of highly attractive, overtly free web services, including search, maps and video, that are in fact funded through a sophisticated and highly lucrative advertising system which trades on what we users look for. Aleks explores how web advertising is evolving further to become more targeted and relevant to individual consumers. Recommendation engines, pioneered by retailers such as Amazon, are also breaking down the barriers between commerce and consumer by marketing future purchases to us based on our previous choices. On the surface, the web appears to have brought about a revolution in convenience. But, as companies start to build up databases on our online habits and preferences, Aleks questions what this may mean for our notions of privacy and personal space in the 21st century.

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kinglimk在 整理的参考文本:


------------for reference only------------

Netflix now has over 12 million subscribers and a turnover of 1.5 billion dollars per year. Millions of people obviously enjoy these recommendation systems and are happy with what theyget in return. But I worry that in the process, perhaps we've lost something.


I wonder a recommendation system don't defeat the point of the web. Isn't it a vast possibility that the web offers for serendipity to bring us unexpected new ideas from accidental encounters, being replaced by a process that apparently broadens our horizon but actually sells us the same things.

Amazon, because we carry universal selection,really de-homogenize culture. Let's people pick the products that they want,you get to read books that you want, not just the books that were cherry-picked and hand-selected to fit into a store of a certain size.

But just because the web now enables us to choose from a vast of selection that doesn't mean we actually take up the opportunity.Faced with overwhelming choice, consumers tend to stick to what they know.

In practice, what's become apparent is that we still huddle together in groups that confirm our existing beliefs. Now for companies who want to sell us thing manipulating that little aspect of our psychology means massive financial returns.

Some of the chance and some of spontaneity has to be ironed of the system for it to work. I mean it's not a randomizer, I mean it's doing something very deliberate, it's doing a kind of collaborative filtering as it was called early on. And it's finding patterns, and it's trying to use those patterns to sell you something.

Recommendation engines are very good at figuring out what people like me would do and telling me what that is, so I can then find out what people like me do. I can become much like a person like me.

We are 100% about trying to improve our consumers' enjoyment of movies. And we help them get the movies that they are gonna laughat most, cry the most, love the most, it's all about pleasing the consumer andif that narrows, that's fine, if that broadens, that's fine.

Recommendation engines by telling me what people like me do and encouraging me to be like a person like me. They help me to become more prototypically one of my kind of person and the more like one of my kind of person I become, the less me I am, and the more I am a demographic type.

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支持普特英语听力就多多发帖吧!您们的参与是对斑竹工作最大的肯定与支持!如果您觉得还不错,推荐给周围的朋友吧~

[Homework]2011-09-05 虚拟革命 免费的代价 The Cost of Free —16

Micolis know has a retrial million for scriber and turn it over of one and half of billion dollars per year. Millions of people obviously enejoy this record mindation systerm. And they don't have to pay for what they get returned, but I worry that in the process perhaps we've lost something. I wonder a break mindation systerm don't have to free the point of the web, isn't that responsity that the web offer serene di to bring the unexprisive new idea from acciendital in conter being replaced by the process that a perantly broaden her rising. But actually tell the same thing, analyizing because we carry univeral selection roll dea mo cultural relax people pick the products that they want.You get the books that you want not just the books that chary picked and hand selected to fit into a store abserted size. But just because the web now enable us to choose my vast selection. And that means we actually pick up the oppotuinity. Face was over willing the chocie consumers tend to stick what they know. In practive, what become perant is that we feel how to gather improved facting from our christin believe, Now for company who wants to sell a thing, me  means lasted college by fininal return. Some of the chance and some of the brands ID has to be eraned a systerm for to work, I mean it's, it's not around minder.It's doing something very delivered. It's doing the chemical liberary to show to then as was cordedly on and it's, it's finding ped and it's trying to use those ped to sell you something. Record mindation engnees are very good at figuring out what people like me with due and tell me what that is, so I can men finding that what people like me due, I can become much more like a personal like me. We are hundred precent about trying to improve our consumers enjoyment of movies, and we help them get movies that they're are going to the life most, cryed most, loved most, it's all about pleasing the consumer. And if that narrow is fine, if that blogen's fine. Record mindation engiees by telling me what people like due and encourage me to be like the person like me. They help us to become more proto tipically one of my kind of person and more like one of my kind of person I become than less me I am and more I am demo   

This post was generated by put listening repetition system,  Check the original dictation thread!
1

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  • kinglimk

立即获取| 免费注册领取外教体验课一节
homework:

Micolis know has a retrial million for scriber and turn it over of one and half of billion dollars per year. Millions of people obviously enejoy this record mindation systerm. And they don't have to pay for what they get returned, but I worry that in the process perhaps we've lost something. "I wonder a break mindation systerm don't have to free the point of the web, isn't that responsity that the web offer serene di to bring the unexprisive new idea from acciendital in conter being replaced by the process that a perantly broaden her rising. But actually tell the same thing, "analyizing because we carry univeral selection roll dea mo cultural relax people pick the products that they want.You get the books that you want not just the books that chary picked and hand selected to fit into a store abserted size." But just because the web now enable us to choose my vast selection. And that means we actually pick up the oppotuinity. Face was over willing the chocie consumers tend to stick what they know. In practive, what become perant is that we feel how to gather improved facting from our christin believe, Now for company who wants to sell a thing, me  means lasted college by fininal return. Some of the chance and some of the brands ID has to be eraned a systerm for to work, I mean it's, it's not around minder.It's doing something very delivered. It's doing the chemical liberary to show to then as was cordedly on and it's, it's finding ped and it's trying to use those ped to sell you something. Record mindation engnees are very good at figuring out what people like me with due and tell me what that is, so I can men finding that what people like me due, I can become much more like a personal like me. We are hundred precent about trying to improve our consumers enjoyment of movies, and we help them get movies that they're are going to the life most, cryed most, loved most, it's all about pleasing the consumer. And if that narrow is fine, if that blogen's fine. Record mindation engiees by telling me what people like due and encourage me to be like the person like me. They help us to become more proto tipically one of my kind of person and more like one of my kind of person I become than less me I am and more I am demo
实现无障碍英语沟通

[Homework]2011-09-05 虚拟革命 免费的代价 The Cost of Free —16

Netplace now has over 12 million subscribers and a turn over of 1.5 billion dollars for a year. Millions of people obviously enjoy this recommendation system and are happy with what they get in return, but I worry that in the process perhaps we lost something. I wonder if the recommendation system don't fit the point of web, isn't a vast possibility that the web offers for serendipity to bring us unexpected new ideas from accidental and counters being replaced by a process that apparently broadens our horizon, but actually sells us the same things. -In Amazon because we carry universal selection, really de-homogenizes culture let people pick the products that they want. You got to read the books that you want not just the books that were Cherry picked and Hand selected to fit into a store of certain size.
But just because of the web now enable us to choose some vast selection, that doesn't mean we actually take up the opportunity. Face with overwhelming choice, consumer tend to stick to what they know. In practice what become apparent is that we feel huddle together improve section *** existing beliefs. Now, for companies who want to sell us things manipulating that little aspect of our psychology means massive financial return.
-Some of the chains and some of the * has to be ironed at this system. Fair to work, I mean it's not a randomizer, I mean it's doing something very deliberate, it's doing a kind of collaborate to fill during as it was called early on. It's finding patterns and it's trying to use those patterns to sell you something.
-Recommendation engines are very good, and figuring out what people like me would do, and telling what that is. So I can then find out what people like me do. I can become much more like a person like me.
-We are a hundred percent about trying to improve our consumers' enjoyment of movies. And we help them get the movies that they are going to laugh at most, cry the most, love the most. It's all about completing the consumer and if that narrows as find, if that broadens as find.
-Recommendation engines by telling me what people like me do, and encouraging me to be like a person like me. They help me to become more * typically one of my kind of person. And more like one of my kind of person I become, the less me I am, and more I am a demographic type.

This post was generated by put listening repetition system,  Check the original dictation thread!
1

评分次数

  • kinglimk

口译专员推荐—>口译训练软件IPTAM口译通
Netflix now has over 12 million subscribers and a turnover of 1.5 billion dollars per year. Millions of people obviously enjoy these recommendation systems and are happy what they get in return. But I worry that in the process, perhaps we've lost something.
I wonder a recommendation system don't defeat the point of the web. Isn't the vast possibility that the web offer for serendipity to bring us unexpected new ideas from accidental encounters, being replaced by a process that apparently broadens our horizon but actually sells us the same things.
Amazon, because we carry universal selection, really de-homogenize culture. It lets people pick the product that they want, you get to read books that you want, not just the books that were cherry-picked and hand-selected to fit into a store of a certain size.
But just because the web now enables us to choose a vast of selection and doesn't mean we actually take up the opportunity. Faced with overwhelming choice, consumers tend to stick to what they know.
In practice, what's become apparent is that we still huddle together in groups that confirm our existing belief. Now for companies who want to sell us thing manipulating that little aspects of our psychology means massive financial returns.
Some of the chance, and some of spontaneity has to be ironed out of the system for it to work. I mean it's not a randomizer, I mean it's doing something very deliberate, it's doing a kind of collaborative filtering as it was called early on. And it's finding patterns, and it's trying to use those patterns to sell you something.
Recommendation engines are very good at figuring out what people like me would do and telling me what that is, so I can then find out what people like me do. I can become much like a person like me.
We are 100% about trying to improve our consumers' enjoyment of movies. And we help them get the movies that they are gonna laugh at most, cry the most, love the most, it's all about pleasing the consumer and if that narrows, that's fine, if that broadens, that's fine.
Recommendation engines by telling me what people like me do and encouraging me to be like a person like me. They help me to become more prototypically one of my kind of person and more like one of my kind of person I become, the less me I am, and more I am a demographic type.
1

评分次数

  • kinglimk

智乱天下 武逆乾坤
on 1977:

Netflix now has over 12 million subscribers and a turnover of 1.5 billion dollars per year. Millions of people obviously enjoy these recommendation systems and are happy with what they get in return. But I worry that in the process, perhaps we've lost something.

I wonder a recommendation system don't defeat the point of the web. Isn't a vast possibility that the web offers for serendipity to bring us unexpected new ideas from accidental encounters, being replaced by a process that apparently broadens our horizon but actually sells us the same things.

Amazon, because we carry universal selection, really de-homogenize culture. Let's people pick the products that they want, you get to read books that you want, not just the books that were cherry-picked and hand-selected to fit into a store of a certain size.

But just because the web now enables us to choose from a vast of selection and that doesn't mean we actually take up the opportunity. Faced with overwhelming choice, consumers tend to stick to what they know.

In practice, what's become apparent is that we still huddle together in groups that confirm our existing beliefs. Now for companies who want to sell us thing manipulating that little aspect of our psychology means massive financial returns.
Some of the chance, and some of spontaneity has to be ironed of the system for it to work. I mean it's not a randomizer, I mean it's doing something very deliberate, it's doing a kind of collaborative filtering as it was called early on. And it's finding patterns, and it's trying to use those patterns to sell you something.
Recommendation engines are very good at figuring out what people like me would do and telling me what that is, so I can then find out what people like me do. I can become much like a person like me.

We are 100% about trying to improve our consumers' enjoyment of movies. And we help them get the movies that they are gonna laugh at most, cry the most, love the most, it's all about pleasing the consumer and if that narrows, that's fine, if that broadens, that's fine.

Recommendation engines by telling me what people like me do and encouraging me to be like a person like me. They help me to become more prototypically one of my kind of person and more like one of my kind of person I become, the less me I am, and more I am a demographic type.
1

评分次数

  • kinglimk

HW

Netflix now has up to 12 million subscribers and a turnover of 1.5 billion dollars per year. Millions of people obviously enjoy this recommendation system and are happy with what they get in return. But I worry that in the process, perhaps, we’ve lost something.

I wonder if recommendation systems don’t defeat the point of web, is there the vast possibility that the web offers for // to bring us unexpected new idea from accidental encounters being replaced by a process that apparently broaden us horizon but actually sell us the same things?

Amazon, because we carry universal selection, really dehomogenizous culture.  Let’s people pick the products they want. You get to read the books that you want, not just the books we were cherry picked and hands selected to fit into a store of certain size.

But just because the web now enables us to choose from the vast selection that doesn’t mean we take up the opportunity. Faced with overwhelming choice, consumers tend to stick to what they know.

In practice what become apparent is that we still hurdle together in groups that confirm our existing beliefs. Now for companies who want to sell us things, manipulating that little // of our psychology means massive financial returns.

Some of the chance, and some of the spontaneity has to be ironed as a system for it to work. I mean it’s not a randomizer. It’s doing something very deliberate. It’s doing a kind of collaborate filtering as it was called very early on. And it’s finding patterns and it’s trying to use these patterns to sell you something.

Recommendation engines are very good at figuring out what people like me would do, and telling me what that is. So I can then find out what people like me do. I can become much more like a person like me.

We are 100% about trying to improve our consumers’ enjoy mental movies and we help them get movies that they are // laughed most, cried most, loved most. It’s all about pleasing the consumer. And if it that narrows, that’s fine; and if that broadens, that’s fine.

Recommendation engines by telling me what people like me do and encouraging me to be like a person like me. they help me to become more protal typically one of my kind of person, and more like one of my kind of person I become, the less me I am and more I am a demographic type.
1

评分次数

  • kinglimk

实现无障碍英语沟通
On Elainewjy

Netflix now has up to 12 million subscribers and a turnover of 1.5 billion dollars per year. Millions of people obviously enjoy this recommendation system and are happy with what they get in return. But I worry that in the process, perhaps, we’ve lost something.

I wonder if recommendation systems don’t defeat the point of web, is there the vast possibility that the web offers for serendipities to bring us unexpected new idea from accidental encounters being replaced by a process that apparently broaden us horizon but actually sell us the same things?

Amazon, because we carry universal selection, really dehomogenizous culture.  Let’s people pick the products they want. You get to read the books that you want, not just the books we were cherry picked and hands selected to fit into a store of certain size.

But just because the web now enables us to choose from the vast selection that doesn’t mean we take up the opportunity. Faced with overwhelming choice, consumers tend to stick to what they know.

In practice what become apparent is that we still hurdle together in groups that confirm our existing beliefs. Now for companies who want to sell us things, manipulating that little aspect of our psychology means massive financial returns.

Some of the chance, and some of the spontaneity has to be ironed as a system for it to work. I mean it’s not a randomizer. It’s doing something very deliberate. It’s doing a kind of collaborate filtering as it was called very early on. And it’s finding patterns and it’s trying to use these patterns to sell you something.

Recommendation engines are very good at figuring out what people like me would do, and telling me what that is. So I can then find out what people like me do. I can become much more like a person like me.

We are 100% about trying to improve our consumers’ enjoy mental movies and we help them get movies that they are going to laugh at most, cried most, loved most. It’s all about pleasing the consumer. And if it that narrows, that’s fine; and if that broadens, that’s fine.

Recommendation engines by telling me what people like me do and encouraging me to be like a person like me. they help me to become more protal typically one of my kind of person, and more like one of my kind of person I become, the less me I am and more I am a demographic type.
1

评分次数

  • kinglimk

普特听力大课堂

[Homework]2011-09-05 虚拟革命 免费的代价 The Cost of Free —16

Netfix now has over 12 million subscribers and turned over 1.5 billion dollars per year. Millions of people obviously enjoyed this recommendation system, and are all happy with what they get returned. But I worried that in the process perhaps they have lost something.
I wondered recommendation system don't defeat the point of web. Is not it vast possibility that web offers for serendipity to bring a unexpected new idea for accidental encounter been a place by process that apparently broaden the horizon. But actually sells are the same thing.
Amazon, because we carry universal selection really de-homogenizes culture. let's people pick up the products they want, not just the book cherry-picked and hand-selected to fit into a store or a certain size.
Because the web enables us to choose in a vast selection, that doesn't we actually pick up the opportunity. Face the overwhelming choice, consumers tend to stick to what they know.
In practice, what becomes apparent is that we feel huddle together in groups that confirm from our primary beliefs. Now for companies who want to sell us thing manipulating that little aspect of our psychology in massive financial return.
Some of the chances and some of the spontaneity has to be ironed out of the system for it to work. It is now randomize, I mean it is doing something very deliberate. It is doing it kind of like collaborative filtering as it was called early on. And it is founding patterns. And they are trying to use these patterns to sell you something.
Recommendation system engine was very good as figuring out what people like me what to do, and tell me what that is. So I can find out what like me do and become much more like a person like me.
We are a hundred percent about trying to prove our consumers enjoy our movies,we help them to get movies that they are gonna laugh at most, cry most, love most is all about pleasing consumer and if that narrows as fine and that broadens as fine.
Recommendation engine by telling what people like me do, urging me to be like a person like me. They help me to become more prodotypically one of my kind of person and more like one of my kind of person I become, the less me I am, and more I am a demographic type.


This post was generated by put listening repetition system,  Check the original dictation thread!
1

评分次数

  • kinglimk

好栏目推荐之美国口语俚语

[Homework]【整理】2011-09-05 虚拟革命 免费的代价 The Cost of Free —16

Netflix now has over 12 million subscribers and a turnover of one and a half billion dollars per year. Millions of people obviously enjoy these recommendation systems and are happy with what they get in return. But I worry that in the process perhaps we've lost something.
I wonder if recommendation systems don't defeat the point of the web. Isn't it a vast possibility that the web offers for serendipity to bring us unexpected new ideas from accidentally encounters, being replaced by a process that apparently broaden our horizen but actually sells us the same thing.
Amazon, because we carry universal selection, really dehomogenizes culture . Let's, people, pick the products that they want, you get to read the books that you want, not just the books that Cherry picked and hence selected to fit into a store of a certain size.
But just because the web now enables us to choose from a vast selection, that doesn't mean we actually take up the opportunity. Faced with overwhelming choice, consumers tend to stick to what they know.
In practice, what becomes apparent is that we still huddle together in groups that confirm our existing belief. Now for companies who want to sell us things, manipulating that little aspect of our psychology means massive financial return.
Some of the chance, and some of the sponsornatee, has to be ironed of the system, for it to work. I mean it's not a randomizer. I mean it's doing something very deliberate, it's doing a kind of collaberative filtering as it was called early on. It's finding patterns, and it's trying to use those patterns to sell you something.
Recommendation engines are very good at figuring out what people like me would do and telling me what that is. So I come in find out people like me do , I can become much more like a person like me.
We are a hundred percent about trying to improve our consumers' enjoyment of movies. And we help them get the movies that they are gonna laugh at most, cry the most, love the most. It's all about pleasing the consumers. If that narrows, that's fine. If that broadens, that's fine.
Recommendation engines, by telling me what people like me do, and encouraging me to be like a person like me, help me to become more prototypically one of my kind of person. The more like one of my kind of person I become, the less me I am, and the more I am a demographic type.

This post was generated by put listening repetition system,  Check the original dictation thread!
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