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[文化博览] 【整理】2011-09-02 虚拟革命 免费的代价 The Cost of Free —15

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[文化博览] 【整理】2011-09-02 虚拟革命 免费的代价 The Cost of Free —15

 

 

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------------

Like Google, today many online retailers have got clever in collecting and analyzing informationon their customers.


We study your past purchase history and then usethat in a statistical way to make predictions about what other things in this massive catalog of products that you might be interested in.

What Jeff Bezos is talking about is a whole new level of interaction with customers and something that's defining the new commercialized web--recommendation engines.

As you start looking for cameras, you start to see people who clicked, who looked at this also looked at that; people who bought this... people who could’ve just bought that. You know, in this critical course of your clicking, the service becomes more useful to you.

One way to think about that is a sort of redecorating the store for each customer who walks in. If you think about a physical store, that would be impossible. You can't run around and rearrange the furniture and put the products for that particular individual customer might like most upfront, very, very difficult. But in an online store, of course you can do that, you can redecorate the store for each individual customer, you can help people find things that they might not have ever been able to find any other way.

Recommendation engines enable businesses to constantly personalize their offerings to match our interests and behavior.This intimate knowledge of customers gives web companies a head start incompetition with real world retailers. One of the best examples is how it's helping NetFlix, an entirely web-based film rental company to rival the bricks and mortar giant Blockbuster.

We look at movies that are really rich area to try to understand human behavior and how to create a better experience than any other video system so that people watch more and more movies.

Fundamental to their business, is a computer algorithm called Cinematch that uses customers' preferences to identify other DVDs that they might like.

Movie taste is very personalized. But what we realized is that if we ask people to tell us what other movies they'd loved in the past, that our computer systems can do a really good job to help them choose movies that they are more likely to enjoy in the future.


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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]2011-09-02 虚拟革命 免费的代价 The Cost of Free —15

Like Google, today many online retailers have got clever in collecting and analyzing information on their customers.

We study your past purchase history and then use that in a statistical way to make predictions about what other things in this massive catalogue of products that you might be interested in.

What Jeff Bezos is talking about is a whole new level of interaction with customers and something that's defining the new commercialized web, recommendation engines.

As you start looking for cameras, you start to see people who clicked ... who looked this also looked that, people who bought... people who clicked this bought that.
In the course of your clicking, the service becomes more useful to you.

One way to think about that is we are sort of redecorating the store for each customer who walks in. If you think about a physical store, that would be impossible. You can't run around, and rearrange the furniture, and put the products that particular  individual customer might like most up front. Very very difficult. But in a online store, of course you can do that. You can redecorate the store for each individual customer. You can help people find things that they might not be able find in other way.

Recommendation engines enables businesses to constantly personalize their offerings to match our interests and behavior. This intimate knowledge of customers gives web companies a head start in competition with real-world retailers. One of the best examples is how it's helping NetFlix, an entirely web-based film rental company, to rival the brick-and-mortar giant Blockbuster.

* movies is a really rich area to try to understand human behavior and how to create a better experience than any other video system so that people watch more and more movies.  

Fundamental to their business is a computer algorithm called SynaMatch that uses customer preferences to identify other DVDs that they might like.

Movie taste is very personalized. But we realized is that if we ask people to tell use what other movies they loved in the past that our computer systems can do a really good job to help them choose movies they are more likely to enjoy in the future.

                                                   
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

Like Google, today many online retailers have got clever in collecting and analyzing information on their customers.
We study your past purchase history and then use that in a statistical way to make predictions about what other things in this massive catalog of products that you might be interested in.
What Jeff Bezos is talking about, it's a whole new level of interaction with customers and something that’s defining the new commercialized web, recommendation engines.
As you start looking for cameras, you start to see people who clicked who looked at this also looked at that. People who bought this, people who clicked at this bought that. Er, you know, in this the course of your clicking, the service becomes more useful to you.
One way to think about that is we are a sort of redecorating the store for each customer who walks in. If you think about a physical store, that will be impossible. You can't run around and rearrange the furniture and put the products that that particular individual customer might like most upfront very very difficult. But in an online store, of course you can do that, you can redecorate the store for each individual customer, you can help people find things that they might not have been able to find any other way.
Recommendation engines enable businesses to constantly personalize their offerings to match our interests and behavior. This intimate knowledge of customers gives web companies a head start in competition with real world retailers. One of the best examples is in how it's helping Netflix, an entirely web based film rental company to rival the bricks and mortar giant Blockbuster.
We look at movies as a really rich area to try and understand human behavior and how to create a better experience than any other video system, so that people watch more and more movies.
Fundamental to their business is a computer algorithm called Cinematch that uses customers’ preferences to identify other DVDs that they might like.
Movie taste is very personalized, but what we realized is if we asked people to tell us what other movies they’ve loved in the past, that our computer systems can do a really good job of helping them choose movies that are more likely to enjoy in the future.
1

评分次数

  • kinglimk

智乱天下 武逆乾坤
实现无障碍英语沟通
HW
Like Google, today many online retailers have got clever in collecting and analyzing information on their customers.

We study your past purchase history and then use that in a statistical way to make predictions about what other things in this massive catalog of products that you might be interested in.

What Jeff Bezos is talking about is a whole new level of interaction with customers and something that's defining the new commercialized web--recommendation engines.

As you start looking for cameras, you start to see people who clicked, who looked at this also looked at that; people who bought this... people who clicked this bought that. You know, in the course of your clicking, the service becomes more useful to you.

One way to think about that is a sort of redecorating the store for each customer who walks in. If you think about a physical store, that would be impossible. You can't run around and rearrange the furniture and put the products that that particular individual customer might like most upfront, very, very difficult. But in online store, of course you can do that, you can redecorate the store for each individual customer, you can help people find things that they might not have ever been able to find in the other way.

Recommendation engines enable businesses to constantly personalize their offerings to match our interests and behavior. This intimate knowledge of customers gives web companies a head start in competition with real world retailers. One of the best examples is how it's helping NetFlix, an entirely web-based film rental company to rival the bricks and mortar giant Blockbuster.

We look at movies that are really rich area to try to understand human behavior and how to create a better experience than any other video system so the people watch more and more movies.

Fundamental to their business, is a computer algorithm called Cinematch that uses customers' preferences to identify other DVDs that they might like.

Movie taste is very personalized. But what we realized is that if we ask people to tell us what other movies they'd loved in the past, that our computer systems can do a really good job to help them choose movies that they are more likely to enjoy in the future.
1

评分次数

  • kinglimk

口译专员推荐—>口译训练软件IPTAM口译通

[Homework]2011-09-02 虚拟革命 免费的代价 The Cost of Free —15

HW FIRST TIME
Like google,today many online retailers have got clever in collecting and analysing information on their customers .With the study of pass fortunes history,and then use that in * way to make predictions of what other things in this massive catalog of product * to maybe interested in.What JACON'S talking about is the whole new level interaction with customers and something like defining the new commercial  * web ,recommendation agent.As you start looking for * to see people who collect you who look this also like that people bought this for that .You know,in this causing,the service they can to use for deal.One way to think about that is to * the store.For each custom who works in,you think about the physics store ,you  can't run around and rerich your French and put the products that particular individual customer might like most of front very very difficult.But then,online store,because you cann't do that,you cann't *the store for each individual customer you can help people to find things,but they might not very able to find in the other way.Recommendation agents and able business concerns personalizing their offering to match our interests and behavior.If the web I hate to start in competition with customers In to make knowledge of customers,the web companies are hating starts in competition with real world retailers.One of the best example is how we helping make places and in / web based flim/ company to rab the break and more to join in lock/ .We look at movies ,it's really rich area to try to understand human behavior and how to creat better expercience than any other video system ,so the  people watch more and more videos.From the mental to their business is computer /hold / match they use customers preference to identify other Dvd they may like.Movie takes very personal /for what we realize is to ask people what the other movies take a lot in the past,then our computer systems can do a really good job can help choose movies more likely do enjoy in the future.

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

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  • kinglimk

[Homework]2011-09-02 虚拟革命 免费的代价 The Cost of Free —15

Like Google, today many online retailers have got clever in collecting analyzing information on their customers.
-We study a past purchase history and then use that in a statistical way to make predictions of what other things in this massive catalog of products that you might be interested in.
What JB is talking about is a whole new level of interaction with customer and something that defining the new commercialize the web, recommendation engine.
-As you start looking for cameras, you start to see people who clicked who, who look at this also look at that, people who bought this and people who could have bought that. In this credit cause of you are clicking, the service becomes more useful to you.
-One way to think about that is a sort of redecorating the store for each customer who walks in it. If you think about a physical store, that will be impossible. You can't run around and rearrange the furniture and put the products for that particular individual customer might like most upfront, very very difficult. But in online store, of course you can do that, you can redecorate the store for each individual customer, you can help people find things. But they might not ever been able to find in the other way.
Recommendation engines enable businesses constantly personalize their offering to match our interest and behavior. This intimate knowledge of customers gives web companies a head start in competition with real world retailers. One of the best example is how it's helping Necklace, a entirely web-based film rental company, to rival the bricks and mortar giant LockBuster.
-We look at movies as a really rich area to try to understand human behavior and how to create a better experience than any other video system that people watch more and more movies.
Fundamental to their business is a computer algorithm call S M that uses customers preferences to identify other DVDs that they might like.
-Movie taste is very personalized. But what we realized is that if we ask people to tell us what other movies they've loved in the past. Then our computer systems can do a really good job to help them to choose movies that they are more likely to enjoy in the future.
                                                   
This post was generated by put listening repetition system,  Check the original dictation thread!
1

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  • kinglimk

on qiapa:

Like Google, today many online retailers have got clever in collecting and analyzing information on their customers.

We study your past purchase history and then use that in a statistical way to make predictions about what other things in this massive catalog of products that you might be interested in.

What Jeff Bezos is talking about is a whole new level of interaction with customers and something that's defining the new commercialized web--recommendation engines.

As you start looking for cameras, you start to see people who clicked, who looked at this also looked at that; people who bought this... people who clicked this bought that. You know, at the course of your clicking, the service becomes more useful to you.

One way to think about that is a sort of redecorating the store for each customer who walks in. If you think about a physical store, that would be impossible. You can't run around and rearrange the furniture and put the products that that particular individual customer might like most upfront, very, very difficult. But in an online store, of course you can do that, you can redecorate the store for each individual customer, you can help people find things that they might not have ever been able to find any other way.

Recommendation engines enable businesses to constantly personalize their offerings to match our interests and behavior. This intimate knowledge of customers gives web companies a head start in competition with real world retailers. One of the best examples is how it's helping NetFlix, an entirely web-based film rental company to rival the bricks and mortar giant Blockbuster.

We look at movies that are really rich area to try to understand human behavior and how to create a better experience than any other video system so that people watch more and more movies.

Fundamental to their business, is a computer algorithm called Cinematch that uses customers' preferences to identify other DVDs that they might like.

Movie taste is very personalized. But what we realized is that if we ask people to tell us what other movies they'd loved in the past, that our computer systems can do a really good job to help them choose movies that they are more likely to enjoy in the future.
1

评分次数

  • kinglimk

实现无障碍英语沟通

[Homework]2011-09-02 虚拟革命 免费的代价 The Cost of Free —15

Today many online retailers have got clever in collecting and analysing information on their customs.:We started a year past for purchase history and then used up the statistical way to prediction of what other things in the massive catalogue report would may interesting in .talking about it's a whole new level interaction with customs and somethings that defining the new commercial life web recommendation engine.As you start to look into cameras, you start to see people who collect to who ,who look also for their dad,people will boot this ,people will boot that,you know,in the costless cooking, the serving becomes to a more useful deal ,one way to dig about that was of store, for each customer who works thinks about physical store, that would be impossible ,you can run round put the for particular individual customer like most old friend very very difficult,but in my store, the question cannot do that,you can redecreat the store for each individual custom help people find things,my not believable for the find in your other way,recommendation engine enable business consequently personlizes their offering tomag our interest and behavior.This knowledge of customs give web companys a head star incorportition with retailers.One of the best example is Howard and entirely web based winter company to rival the modern lock faster. Look at the movie he is really a rich area to try to understand he will be heavier and create better a experience than other opinion system, so the people watch more and more movies,some the mental to the business is a computer called pretty much their use conference ,other identify other DVD that they may like,movie took very personal eyes, but what we realize if we ask people what other movie they could love from the past,they computer system can do real a good job,the movie would more likely to enjoy in the future.
This post was generated by put listening repetition system,  Check the original dictation thread!
1

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  • kinglimk

普特听力大课堂
HW

Like Google, today many online retailers have got clever in collecting and analyzing information on their customers.

We’ve studied your past purchase history and then use that in a statistical way to make predictions about what other things in this massive catalogue of products that you might be interested in.

What Jeff Bezos is talking about is a whole new level of interaction with customers and something that’s defining the new commercialized web, recommendation engines.

As you start looking for cameras, you start to see people who clicked, who looked this also looked that. People who clicked this bought that. You know, in this critical course of your clicking, the service becomes more useful to you.

One way to think about that is a sort of redecorating a store for each customer who walks in, if you think a physical store that can be impossible. You can’t run around and rearrange the furniture and put the products that particular individual customers might like most upfront, very very difficult. But in online store, of course you can do that, you can redecorate the store for each individual customer, you can help people find things that they might not be able to find in other way.

Recommendation engines enable business to constantly personalize their offerings to match our interests and behavior. This intimate knowledge of customers gives web companies a head start in competition with real world retailers. One of the best examples is how it’s helping Netflix, an entirely web-based film rental company to rival the bricks and mortal giant Blockbuster.

We look at movies as a really rich area to try to understand human behavior and how to create a better experience than any other video system so that people watch more and more movies.

Fundamental to their business is a computer organism called Cinematch that uses customers’ preferences to identify other DVDs that they might like.
,
Movie tastes are very personalized. But what we realized is if we ask people to tell us what other movies they’ve loved in the past that our computer systems can do a really good job help them choose movies that they are more likely to enjoy in the future.
1

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  • kinglimk

好栏目推荐之美国口语俚语

[Homework]2011-09-02 虚拟革命 免费的代价 The Cost of Free —15

Homework
Like google,today many online retaliers have got clever in collecting and analysing infoormatioon their customers,we've started history and use it in this typical way to make predictions about other things in this massive catalogue of product you may be interested in  what jazz beis office talking about is a new level interreaction with customers and something that defining the new commerical web,recommendition as you started to look for  camears,you started to see people who collect you who look at this also look at that ,people who bought this people bought that,in this of you cooking,0000 one way to think about that is redirect the the store for each customer who walks in ,if you think about physical store that would be impossible,you cannot run around and rerange the 00and put the product that particular individual custom might most,veryvery difficult ,but in on line store,of course u can do that,you can redirect the store for each individual custom,u can help people find things that they might not be able to find any other way,recommenditions engines enable  business to competely personaize their offerings to match our interests and behaviour,this 111knowledge of customers give the web companies a head start in competitions with the real world retailer,one of the best example is how it  helping 222and entirely 333company to rival the break 33,we look at movies as a really rich area to try to understand human behavour and how it create a better apperance than any other vido system ,so that people watch more and more movies,find the mental for their business is a computer called cinder that use customer's preferance to inentify other dvds that they might like,movie taste is very personal ,what we realize is to ask people to tell us what other movie they love  in the past that our computer system can do a really good job helping them choose movies they are more likely to enjoy in the future.

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

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  • kinglimk

[Homework]2011-09-02 虚拟革命 免费的代价 The Cost of Free —15

本帖最后由 shihongmei2828 于 2011-9-3 17:31 编辑

Like Google, today many online retailers have got clever in collecting and analysing information on their customers.
We study your past purchase history and then use that in a statistical way to make predictions about what other things in this massive catalogue of products that you might be interested in.
What Jeff Bezos is talking about is a whole new level of interaction with customers and something that's defining the new commercialised web, recommendation engines.
As you start looking for cameras, you start to see people who clicked, who looked at this also looked at that, people who bought this, people who clicked at this, bought that. You know, in this the course of your clicking, the service becomes more useful to you. One way to think about that is we are sort of redecorating the store for each customer who walks in. If you think about a physical store that would be impossible. You can't run around and re-arrange the furniture and put the products that the particular individual customer might like most up front, very very difficult. But in an online store, of course you can do that. You can redecorate the store for each individual customer and you can help people find things that they might not have been able to find any other way.
Recommendation engines enable businesses to constantly personalise their offerings to match our interests and behaviour. This intimate knowledge of customers gives web companies a head start in competition with real-world retailers. One of the best examples is in how it's helping Netflix, an entirely web-based film rental company, to rival the brick and mortar giant Blockbuster.
We look at movies as a really rich area to try to understand human behaviour and how to create a better experience than any other video system so that people watch more and more movies.
Fundamental to their business is a computer algorithm called Cinematch that uses customers' preferences to identify other DVDs that they might like.
Movie taste is very personalised, but what we realised is if we asked people to tell us what other movies they've loved in the past. That our computer systems can do a really good job of helping them choose movies that they're more likely to enjoy in the future.

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

评分次数

  • kinglimk

[Homework]2011-09-02 虚拟革命 免费的代价 The Cost of Free —15

Like Google, today many online retailers have got clever in collecting and analyzing information on their customers.
We study your past purchase history and then use that in a statistical way to make predictions about what other things in this massive catalog of products that you might be interested
in.
What Jeff Bezos is talking about, it's a whole new level of interaction with customers and something that’s defining the
new commercialized web, recommendation engines.
As you start looking for cameras, you start to see people who clicked who looked at this also looked at that. People who bought this, people who clicked at this bought that. Er, you know, in this the
course of your clicking, the service becomes more useful to you.
One way to think about that is we are a sort of redecorating the store for each customer who walks in. If you think about a physical store, that will be impossible. You can't run around and rearrange the furniture and put the products that that particular individual customer might like most upfront very very difficult. But in an online store, of course you can do that, you can redecorate the store for each individual customer, you can help people find things that they might not have been able to find any other way
.
Recommendation engines enable businesses to constantly personalize their offerings to match our interests and behavior. This intimate knowledge of customers gives web companies a head start in competition with real world retailers. One of the best examples is in how it's helping Netflix, an entirely web based film rental company to rival the bricks and
mortar giant Blockbuster.
We look at movies as a really rich area to try and understand human behavior and how to create a better experience than any other video system, so that people watch more and more
movies.
Fundamental to their business is a computer algorithm called Cinematch that uses customers’ preferences to identify other DVDs that they might like
.
Movie taste is very personalized, but what we realized is if we asked people to tell us what other movies they’ve loved in the past, that our computer systems can do a really good job of helping them choose movies that are more likely to enjoy
in the future.

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

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  • kinglimk

每天半小时 轻松提高英语口语

[Homework]2011-09-02 虚拟革命 免费的代价 The Cost of Free —15

Like Google, today many online retailers have got clever in collecting and analyzing information on their customers.
We study your past purchase history and then use that in a statistical way to make predictions about what other things in this massive catalog of products that you might be interested in.
What Jeff Bezos is talking about is a whole level of interaction with customers and something that's defining the new commercialized web: recommendation engines.
As you start looking for cameras, you start to see people who looked at this also looked at that, people who clicked this bought that. You know, in this course of your clicking, the service becomes more useful to you.
One way to think about that is we're sort of redecorating the store for each customer who walks in. If you think about a physical store, that would be impossible. You can't run around and rearrange the furniture and put the products that particular individual customer might like most, upfront, very very difficult. But in online store, of course you can do that, you can redecorate the store for each individual customer, you can help people find things that they might not have ever been able to find any other way.
Recommendation engines enable businesses to constantly personalize their offerings to match our interest and behavior. This intimate knowledge of customers gives web companies a head start in competition with real world retaliers. One of the best examples is how it's helping Netflix and entirely web based film rental company to rival the brick-and-motar giant Blockbuster.
We look at movies as a really rich area to try to understand human behavior and how to create a better experience than any other video system so that people watch more and more movies.
Fundamental to their business is a computer algorithm called Synamatch that uses customers' preferences to identify other DVDs that they might like.
Movie taste is very personalized. But what we realized is if we ask people to tell us what other movies they've loved in the past, that our computer systems can do a really good job to help them choose movies they are more likely to enjoy in the future.

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

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  • kinglimk

[Homework]2011-09-02 虚拟革命 免费的代价 The Cost of Free —15

Like Google,today many online retailers have got clever in collecting and analyzing information on the customer.We study your past purchase history and then used a statstical way to make predictions about what other things in this massive catalogy of the products that you might be interested in.What Jeff is talking about is a whole new level of interaction with customers and  something that defines the new commercial lines of web,recommendation engines.
One way to think about that is

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