‘术业’ 的存档; 分类
Human computer interactive真是有不少有趣的方向….这几天看的很多的是Augmented Reality(AR), 翻译叫做增强现实,或者扩增实境之类的.
AR要讨论的问题很简单,如何更好的连接虚拟世界和现实世界? 如何更好的让虚拟世界的信息以更直接更自然的帮助现实中的人类. 它的实现也很简单,改变你眼睛所见的 — 把虚拟的物体附加在真实场景的影像中,或者说,存在于现实之上的虚拟.
叽里呱啦的定义跳过,看一个AR最简单而又都熟悉的例子:

像这样的鬼知道这橄榄球里面的叫什么黄线,还有足球转播中的越位线,就是AR概念的一个简单应用. 存在于现实之上的虚拟.
它与“Virtual Environment (虚拟环境)”的最大不同之处,AR是建立在真实世界之上的,在定义概念中它是一个比真实更虚拟,比虚拟更真实的中间地带.

现实世界 虚拟环境

Augmented Reality(AR) 增强现实
AR更多的是一个关于“视觉”的游戏,它改变的是你所看到的而不是真实的部分,所以如何把结果“显示”给用户是系统设计最重要的部分之一。第一种,也是最早提出并使用的一种的“重量级”的方案多是这样的 "眼镜":
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请忽略掉这个2B英国佬身上其它的部分,管用的东西只有那个眼镜而已. 1992年Augmented Reality这个术语第一次被Tom Caudell和David Mizell提出的论文中,他们就是使用的这样的头戴系统. 那篇论文中他们为波音公司的制造工人设计了一种可以直接在要施工的材料上显示设计方案的系统 (比如,通过眼镜工人可以直接看到在什么这块机身钢板的这个位置需要钻个洞,而不用跑去电脑面前对着CAD设计图看了再跑回来量 )。
这是前几年在youtube上相当红火的日本隐身衣视频,来自庆应义塾大学媒体设计系Susumu Tachi教授的实验室:

直到现在不少看过视频的人都会以为是小日本在材料学上又有了无敌突破,这Tachi老头该坐等诺贝尔了. 但结果这是大衣只是一件普通的大衣,整个的透明效果是AR技术投影到衣服上的影像.
每一个researcher都会说自己做的东西很有趣,就算它是谢尔顿的理论物理,但是哥说这是一个有趣的方向是因为它真的有爱。比如这是南澳洲大学WeaableComputer实验室几年前搞出来的玩意,叫做ARQuake(AR毁灭公爵):


不知道你们是不是还记得玩具兵大战,经典不衰,但以后的小p孩可以这样玩


Augmented Reality第一次被Ronald Azuma定义是在1997年,作为名词第一次出现是1992年, 类似的想法出现的时间更早,所以并算不上什么非常新颖的概念,但如果你看一看它的google trends却会发现一个有趣的增长,
这样的数据还明明白白的告诉你,它在最近两年突然红了,还是在商用大众中红了。它不仅会在在学术会议和论文中出现,你在Engadget这样的消费电子新闻站里也会看到它。 为毛?
08/09年的另外几个红人就是答案, 是的, iphone 3gs, android, ipad…. 通过这样的手持设备一直是AR第二种主要“显示”方式. 而最近年出现的这些有摄像有GPS有各种sensor有大触摸屏的牛x的手持设备带来了牛x的移动AR程序. 普通人不会接受背着傻逼的大包和大眼罩在街上跑,但是在大街上玩iphone?很叼.
比如Sekai Camera,一个在日本火热的Iphone app,它是一个基于地理位置的移动AR程序,你可以把你照的照片图片或者写的贴纸通过这个程序“贴”在一个地理位置上,其它的Sekai Camera用户可以通过摄像头的画面看到别人贴出来的东西在空中飘来飘去,像这样


相当好玩的东西, 如果你在东京涩谷那个著名的109大厦附近转悠再打开sekai camera,你会看到这样的有趣场面

其它的一些有趣mobile AR app还有像用iLiving尝试摆放摆放你的新家,IKEA好像也在试着做这样的手机app,不知道正式推出了没有.

再比如Wikitude Drive,这样的界面会不会比唧唧歪歪的车载GPS更好用。

iHandy Level,现在的智能手机才应该叫瑞士军刀..

第三类的AR显示方式是所谓的"Spatial Displays (空间显示)", 把画面在显示器或者通过投影仪之类的东西上显示. 比如PS3上这个叫做Eye of Judgment的纸牌游戏,听说国内去年开始流行一个叫三国杀的翻版游戏, 不知道那公司有没有想法花点时间把这个做做. 毕竟跟认识的朋友玩起来比较high..



再比如日本女人的玩意,商场中各种化妆品试用.

包括之前看到过的sixth sense…

这个概念下的技术还有很多…不过…还是先去睡觉了…..小日本刚刚3:1搞定了丹麦出线….看来居酒屋的半价要延长了….. =d
视频来自Viemo的Kate Ray. 采访文本来自katray.net.
采访人物:
Tim Berners-Lee
Clay Shirky
Chris Dixon
David Weinberger
Nova Spivack
Jason Shellen
Lee Feigenbaum
John Hebeler
Alon Halevy
David Karger
Abraham Bernstein
————————————————————————–
THE PROBLEM
John Hebeler (0:02): The core problem is, our ability to create information has far exceeded our ability to manage it. It’s kind of like we’re drowning in our richness, that’s kind of what’s happening, cause you have all this data, all these access points, and there’s really no way to really help you deal with it except for stuff you can pull into your human brain. And you can only pull in so much. So you’ve got this massive amount of potential, but there’s not any real tools to harness it.
David Weinberger (0:33): We have so much stuff that we have to deal with. Individually, as a culture. So much – that it just bursts the bounds of any physical library. You know if we had a Dewey Decimal System for everything on the web, the trillion pages and all the subpages and all that, we wouldn’t find a thing, that system simply can’t work.
Footage of HE.Net Data Center: 800 cabinets = 9,600 terabytes = 9.6 billion thick books = 1,690 Libraries of Congress.
Clay Shirky (1:09): The amount of media that’s available to the average user is a vastly much larger superset than anything that’s ever existed in human history. If I was going to start a news business tomorrow, I would start a news business designed to produce not one new bit of news, but instead to aggregate news for individuals in ways that mattered to them.
Nova Spivack (1:32): Google really was more important as the web was in millions of pages. Now we’re entering a web that’s going to be billions – well, it already is – that’s going to be billions and billions of pages, and soon trillions of pages. Because a tweet is actually, every individual item is a page. Every product in the world, everything you can name or address is going to have a page. And so that’s trillions of things. And Google doesn’t scale to that.
Hebeler (1:59): There should be enough information out there that you should be able to ask for something extraordinarily specific, but you can’t. You pretty much have to do all the integration in your own head, you’ve gotta come back and see all the stuff that comes back from Google, and say, Oh, I wonder how I could ask that, cause this was kinda right but this was wrong…Oh, I see why it came back, came this out, that isn’t what I want though.”
Tim Berners-Lee (2:18): And so that’s not really a search, I think people use the word search to mean this sort of parachuting in, crossing your fingers, and hoping to land somewhere really good.
Chris Dixon (2:25): You know when you’re looking for a camera and you go to some place and there’s like ten thousand cameras and you’re overwhelmed, and sort of studies show that people are actually less likely to buy something when they’re overwhelmed by these things and less likely to actually be happy with what they buy afterward.
Weinberger (2:37): We have too many emails, so we start to tag them or label them, Gmail calls them labels. And we start to apply labels. And then we get, maybe we start to get hundreds of labels and we think, Oh jeez, now I gotta label my labels.
Hebeler (2:48): All the tweets and all the MySpace and you start to think, What if I could start to put things together in all that flow of information? And in order to do that, you need some structure.
Alon Halevy (2:59): It’s clear that something needs to be done with more structured data.
Dixon (3:03): Like all the information might be out there, it’s just if it’s indexed in a really inaccessible form, you know a lot of times it might as well not be out there, right?
Shirky (3:09): That is, in many ways, the problem of the age. Right, content, as it turns out, is not king.
Weinberger (3:15): We are always going to be filtering the filters that filter our filters. That filter our filters.
Hebeler (3:20): How do I find the right file? How do I know that all those files belong there?
Spivack (3:24): How do you integrate data?
Jason Shellen (3:25): How do I keep up with all these new sources of information?
Shirky (3:29): How do you filter things to create more value than you can currently get?
Hebeler (3:33): And that is what the Semantic Web could eventually promise to do.
THE VISION
In 1989, a physicist named Tim Berners-Lee invented something called the World Wide Web
Berners-Lee (3:49): I wanted to reframe the way we use information, the way we work together.
It made the Internet pretty popular…But Tim wasn’t finished.
Berners-Lee at TED, 2009:
Berners-Lee (4:03): Now, twenty years on, I want to ask your help in a new reframing. I want you to put your data on the web.
Berners-Lee (4:10): Okay, data is brown and boxy and boring and that’s how we think of it, isn’t it, “data”. But in fact, data is about our lives. You just, you log onto your social networking site, pick your favorite one, you say, this is my friend. Bing! Relationship. Data. You say, this photograph, Oh it’s about, it depicts this person, Bing! That’s data. Data, data, data…
Hebeler (4:30): The Semantic Web, at it’s lowest level, is just an expression of information, that’s all it is. So the, how the web works today, for the most part, is human to human. A human being puts something in some format, the computer is, all it knows about is formatting information. It knows it’s supposed to make this bold, it knows it’s supposed to underline this, the computer doesn’t know anything more than it’s just a bunch of bits. So semantics merely adds extra information to help you with the meaning of the information.
Spivack (4:56): It’s really just like transforming the web into something that’s a little bit more like a database…Trying to make it a lot easier to find stuff, because we have an understanding and an index of what’s out there.
Lee Feigenbaum (5:11): So you have specific data items, whether they’re books or songs or news articles or people. And linking them together. And with Semantic Web technologies, the links mean something.
Hebeler (5:31): It’s all about relationships, it’s about relationships of one string to another string, or one number to another number…And if I have enough of those relationships, I can start to build context, and context is what it’s all about…If I said any kind of word, it’s the context that surrounds the word that really gave you the meaning. What your brain has really done is connected that one word with all kinds of relationships. In a technical sense, all the Semantic Web does is start to give all these relationships.
Berners-Lee (6:12): If you look at the original proposal for the web, there are different shapes for different things, like people and documents. And there are arrows going between them and the arrows are labeled. Sort of this includes this, this describes this. So I think the idea of wanting to capture the meaning of the relationships, capture that actual data, has been there for ages.
Hebeler (6:42): We know that there’s a structure to this, there’s a structure to all the information on the Internet.
THE CRITICS
Shirky (7:00): The reason there aren’t too many criticisms of the Semantic Web yet is that it operates in its own bubble. I think I’m unusual in having, six or eight years ago, gone out with a set of opinions that said this isn’t working because it’s not a good idea and it’s never going to work.
Dixon (7:18): Semantic Web is a word that began with a technical meaning…Now that word has morphed into a marketing term that’s sort of abused and thrown around and so I would almost argue to the extent that it’s maybe not a useful word anymore.
Shellen (7:33): In terms of the Semantic Web, you know the idea is that everything is linked. I still like that idea. I think potentially that’s a Utopian idea to strive for.
Halevy (7:44): In an ideal world, yes. If everybody was trained in database and knowledge representation technology, that’s how we would do it.
Shirky (7:53): I’ve often joked that the Semantic Web is a witness protection program for AI researchers. That what the Semantic Web held out was the possibility that instead of making machines think like people, we could describe the world in terms that machines were good at thinking about. So we would switch from trying to build up brains in silicon, and instead rerender the actual world as information. And that gets very quickly to one of the deepest, you know, questions in all of Western philosophy, which is: Does the world make sense? Or do we make sense of the world? I don’t think you can unambiguously describe the world. I don’t think you can describe the world, or even large subdomains of the world, in a way that all observers or even most observers will agree with.
THE SCHISM
Abraham Bernstein (9:05): I guess we’re more on the skeptic’s end -
David Karger (9:07): – although I wouldn’t actually express it as a skepticism, I would say that we’re enthusiasts for a particular piece of the Semantic Web, which some people are skeptical about. Which is the sort of sloppy, or scruffy, Semantic Web.
Karger (9:28): So the panel, the panel was a panel titled, “Does the Semantic Web need Ontologies?” And everybody on the panel said,
Tom Heath (9:35): Yes, I think we’re all unanimous about that.
Michael Witbrock (9:38): The Semantic Web does need ontologies.
Frank Van-Harmelon (9:40): This only makes me think of the following question: Is the pope a Catholic?
Karger (9:44): So, that sort of is at the far formal end of the Semantic Web.
Bernstein (9:47): I guess what we both believe more in is, you know, a little structure goes a long way if you combine it with, for example, a human being that has a lot of intelligence between his or her ears.
Karger (10:05): I was in the audience but they had a microphone for the audience and I sort of got up and said:
Karger, at the panel (10:10): I’m going to dissent.
Karger (10:12): No, the Semantic Web does not need ontologies. I know that there are some people who feel, like the panelists, who feel very strongly that ontologies are a must.
Heath (10:21): I think most of us in this room disagree with David, and I think we need to show him. Take a school analogy, and take him out to the playground and show him we can do much more.
Bernstein (10:32): So when you do parenting there are only two people fighting unless the grandparents are at home, right, and they’ll be fighting with you. But this is, you know, a whole community of what was it, five-hundred odd, six-hundred people, who are fighting about a baby called the Semantic Web.
Karger (10:45): Right, I mean, we could all just sort of sit back and do the work that we like to do and not care what everybody else is doing, but we’re believers in the potential of this Semantic Web thing, that some wonderful things can come out of it, and that makes us care how it’s pursued.
THE FUTURE
Berners-Lee (11:18): What’s the funny thing about the web is that it seeps in from the bottom. But for every person, they said, well, Tim, you know, what did you feel in 1993 when the web really exploded? And generally that meant, it was when I found out about it. Everybody, different people found out about the web at different times. Or different people had this ‘aha’ moment at different times.
Feigenbaum (11:44): I think the web, the World Wide Web, is a couple of different things. From a technology sense, it’s some extraordinary successful protocols and communication methods that mean that my web browser can go out to any web server in the world and get it back and show it to me. From a more social sense, the web is Facebook and MySpace, and blogs and news sites, right? And it’s all the things we do on the web. And I think it’s similar with the Semantic Web.
Spivack (11:20): The first step is evolution, the second step is revolution. When, you know, once there’s enough good content out there we can make some systems that can reason across the web and solve problems, answer complicated questions, make amazing discoveries and linkages between things. That’ll be cool. That is off in the future.
Weinberger (12:38): I have no idea what’s going to happen. But in terms of the openness of the web and our ability to access it and sort of the fundamental features of the Internet that made it the Internet, what happens to those features depends upon economics and politics and culture and technology. And it could easily change in radical ways through an invention that somebody in a garage is inventing now.
Berners-Lee (13:06): It’s a platform. Just like the web. The idea of it is not that it should promote one particular sort of application. Just as the Internet didn’t promote a particular application, so I could design the web on it without asking anyone’s permission. Same way, Semantic Web is sort of built on top of web, it should just allow you to build whatever you’d like on top of it. What we – at this conference, I think, people can’t imagine, because they’re trying to make it work so much, they’re not going to imagine what things people will be able to do with it once it’s working and it’s well-deployed.
Me (13:36): Do you think you can imagine?
Berners-Lee (13:39): Nope, I can’t. If we end up building all the things I can imagine we’ll have failed.
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”NarayanaMurthy被誉为印度的“比尔·盖茨”,他创立了印度的软件巨头Infosys。Murthy的儿子想报考India Institute of Technology的计算机专业,但被 IIT拒绝,结果却被美国的常春藤学校康奈尔大学录取,最后遗憾地到康大读书.“
看到这一段话的时候着实一怔,然后开始查这印度理工大学到底是个什么状况. 结果同样意外.
我想在很多人考虑亚洲力量的时候,都有些容易忽略印度,就算知道是与中国竞争中的主要力量,但是对肤色的偏见也好,或者对穷困印度电影的印象也好,我们还是更多的把眼光都注视在东亚国家身上。
我在很长时间里对亚洲理工大学的印象就是下面这些名字,东大,京都,东工大,大阪,港科大,新加坡国立,南洋理工,韩国高等科学技术学院, 清华,以色列理工. 现在我也很奇怪我之前为什么从没有疑问过印度这样一个强劲的国家的大学哪去了. 我一直嘲笑日本人眼光的狭隘与自闭,现在想想也是开始不要只是用”说奇怪英语的阿三“这样眼光审视印度的时候了. 在很多方面,他们比我们更强.
印度理工学院(भारतीय प्रौद्योगिकी संस्थान, India Institute of Technology),简称为IIT,是由印度政府所建设和组成的七间自治工程与技术学院。1951年8月以MIT为蓝本设立,在印度东西南北部各设分校。现今大学生人数有大约一万七千名,包括研究学者在内的研究生有一万三千名。
在Times全球大学排名,科学与工程技术大学排行中,7所分院全部进入世界前200名,最高的孟买分院排名30。在排名上看并没有到达多么醒目的程度,这大概也是常常忽略它的原因。IIT的治学理念以教授效绩为中心,并不重视论文发表量,所以在以论文数量作为重要参数的上交排行榜中他们的排名更惨不忍睹,几乎勉强进入500. 我无法评价上交排名的意义,但一个实际现象是,中国大学的论文发表数量已经是世界第二,但是论文引用率(你可以简单理解为这篇论文对这个学术方向上其它学者有用或者认同的程度)低下的让人面红耳赤,同时中国的学术造假说不定早也是世界第一.
下面是关于印度理工一些让人吃惊的东西:
IIT的7个分校,每年共招收4000多名新生,每年报名考试人数超过30万. 录取率约1.67%,同时目前times排名世界第一的哈佛录取率为9.1%,times工程大学排名第一的MIT为12.5%,亚洲第一的东京大学为33.3%。
印度政府每年给其它几千所理工学院的补助,加起来只有IIT的3%.
每年的12月,各跨国公司会纷纷进驻7所分校征招人才。通常在两个星期之内,所有学生都会被“抢购一空”
IIT的入学考试单独进行,每年有30万经过预选的高中毕业生考生参加IIT的专门考试,只有2%能拿到IIT的录取通知书。一些被IIT拒绝的学生拿到美国麻省理工学院、普林斯顿、加州理工学院等 美国名校的奖学金。换句话说,IIT是在与美国的名校竞争生源之中获得胜利。
印度理工学院的一个特点,是由教授兼做行政工作。印度令人摇头的官僚主义,在这里受到抑制。盛行的贪污和政治党派纷争,也被拒于校院之外。有人把它誉为印度今天最没有贪污的机构.
為了要爭取更多所謂金雞蛋的IIT畢業生,以歐美各國為主,來自全球的企業紛紛前來校園徵才。除了微軟、雅虎、IBM、英特爾、西門子等和科技相關的企業之外,由於近年來畢業生耀眼的成就,連美林投資、花旗銀行等大型金融公司都趕忙地投入徵 才的行列。歐美企業一般來說都是以年薪的方式告知薪水,所以有不少企業第一年的年薪就開出了一千萬日圓的價碼。聽說這和美國名校哈佛大學畢業生的待遇是一樣的。
他并不担心IIT的毕业生到美国求职,并留在那里定居,他认为,这些年美国各大公司纷纷在印度本土设立研究和开发基地,在美国各大公司高层任职的IIT毕 业生无疑起到了重要的作用。
“上至总理的儿子,下至校长、教授的小孩,不论是谁,要进印度理工学院,考试成绩一定要 在前2%,”德里校区注册组长辛格不无骄傲地说,在印度,第一流的学生进印度理工学院,二流的才出国念美国名校。