VirgoLab

1、你一定要觉得你自己的问题有趣而又重要，否则你就不会有激情，不会投入。 2、你一定要有能力解决问题。假如不能完全解决问题，起码你要问自己能够做些什么。 3、除了你自己的肯定，如果还有人也认为你的问题很重要，且这样的人越多越好。

C代表Creative Power创造力，K代表Knowledge知识，I代表Information信息，T则是Thinking思考，Q是Quarrel讨论，L是Logic Reasoning逻辑推理，E是Experiments实验，W是Wide Field广博的领域，H则代表Hell地狱。这样看来，创造力就等价于：汇集知识与信息，然后不断思考并反复讨论，再利用逻辑推理和实验在广博的领域里积分的结果。

His basic thesis is that when you have so much data you can map out every connection, every correlation, then the  data becomes the model. No need to derive or understand what is actually happening — you have so much data that you can already make all the predictions that a model would let you do in the first place. In short — you no longer need to develop a theory or hypothesis - just map the data!

First, in order for this to work you need to have millions and millions and millions of data points. You need, basically, ever single outcome possible, with all possible other factors. Huge amounts of data. That does not apply to all branches of science.

The second problem with this approach is you will never discover anything new. The problem with new things is there is no data on them!

Now, typically library catalogs use traditional information retrieval techniques over professionally produced metadata. This is not a lot of data to play with! We have just begun to see interesting things being done with the other types of data as libraries explore the use of transactional data for recommendations and look to incorporate contributed data.

Google, Amazon and other sites license professionally produced metadata. But in different ways they also use the other types of data also.

Suggestion, or recommendation, is becoming increasingly a part of our everyday web experience,and improving the quality of suggestion has become an important goal for many services. Clearly, there are commercial interests riding on this.

我其实一直对“草根”这词很不感冒，这个称谓更象是媒体赋予的骗局。事实上，多数的“草根”仍是被忽视或仅仅被当作基数提及的，而以“草根”身份成名的作者，正逐渐演变为新的“精英”，甚至这些知名“草根”们都有成为“精英”的欲望

“草根”是媒体赋予那些未成名的作者的一个定义，但真正的“草根”其实是那些被当作基数的网民，我们当然不可能忽视“草根”群体的存在，我们只是不可避免地忽视了他们做为个体存在的意义。

互联网是为人服务的，因此不要妄想互联网改变人性，事实上互联网放大了群体的人性特征，基数越大，金字塔的顶端越是高得结实。理论上说，任何一个领域都不存在直上直下的基数与顶点的形态，更不用说倒金字塔形态了，“草根”压倒“精英”这根本就是违背人性的伪命题。

连线》杂志母公司CondéNast宣布收购Ars Technica，据TechCrunch最先的报道称大概在2500万美元左右，跟2006年收购Wired.com的价格齐平。Ars每月的独立用户在450万左右，略低于Wired.com。

优秀的设计往往源于巧妙的减法

导航必须提供有意义的跳转链接； 导航必须要传达出各链接之间的重要级别和相互联系； 导航必须要传达出与用户当前所浏览页面的关系。

一个良好的解决方案应该是用减法来提炼任务过程而不是用加法来增加用户的认知负担

Some of us run to each new service, play around for a bit and then quickly abandon it.

scientists are using blogs, wikis, and social networking and bookmarking applications primarily for professional reasons. Results show that these social media applications have provided scientists and researchers with additional resources to help them collaborate, connect, share and discover information.

Over 50% of respondents see web-based social applications playing a key role in shaping the future of research. The largest influence will be on critical analysis and evaluation of research data, professional networking and collaboration, dissemination of research output, career development, as well as grant application and funding.

Comments from survey respondents identified several issues need to be addressed before mass acceptance by the research community is possible – namely the need for specialist tools, higher security, and validation of users. However, these concerns were not seen as insurmountable obstacles, and many anticipated tremendous potential for social media.

Taleb makes a convincing case that most real-world phenomena we care about actually inhabit Extremistan rather than Mediocristan. In these cases, you can make quite a fool of yourself by assuming that the future looks like the past.

The current generation of machine learning algorithms can work well in Mediocristan but not in Extremistan.

It has long been known that Google's search algorithm actually works at 2 levels: An offline phase that extracts "signals" from a massive web crawl and usage data. An example of such a signal is page rank. These computations need to be done offline because they analyze massive amounts of data and are time-consuming. Because these signals are extracted offline, and not in response to user queries, these signals are necessarily query-independent. You can think of them tags on the documents in the index. There are about 200 such signals. An online phase, in response to a user query. A subset of documents is identified based on the presence of the user's keywords. Then, these documents are ranked by a very fast algorithm that combines the 200 signals in-memory using a proprietary formula.

根据SCI数据，王鼎盛分析全世界200多种发表研究论文的物理期刊时发现，期刊总的引用＝1.5×论文数量×影响因子×半衰期（寿命）。

因此即使以总的引用作为期刊影响力的衡量标准，期刊寿命也与影响因子有同等重要的地位。然而，期刊寿命常常会被忽视。王鼎盛认为，搞科学的人应该更看重期刊的寿命。

国人对研究论文评价时的期刊崇拜，和对评价期刊时的影响因子崇拜，不但有碍我国物理期刊的提高，而且助长了科研中的浮躁情绪。中国物理界更该提倡追求影响的持续年限，并进而追求经典。

The heart of the matter is this: how do you measure the quality of search results

The first is that we have all been trained to trust Google and click on the first result no matter what. So ranking models that make slight changes in ranking may not produce significant swings in the measured usage data. The second, more interesting, factor is that users don't know what they're missing.

here's the shocker -- these metrics are not very sensitive to new ranking models! When Google tries new ranking models, these metrics sometimes move, sometimes not, and never by much

为什么这么多人不相信个人媒体，而要相信大众媒体？另外一个重要的原因就是对个体的潜意识排斥：个人怎么能成为媒体呢？个人成为媒体了，是不是有什么“特殊用心”呢？是不是为了欺骗我呢？这一系列问题的提出，其实是有原因的。这要追溯到90年代的疯狂传销所埋下的祸根。

大多数人，仍然坚持相信机构媒体、机构内容。这些人，没有自己的观点，也不愿意相信其他个体的观点。只要他们不被机构媒体害得没饭吃，他们就不会有什么抱怨。

Goldstein is expected to announce "social banners," or display ads that turn you or your friends into the hook of a marketing message. In tandem, SocialMedia will announce that it's developed a patent-pending algorithm called FriendRank to power those social banners. It's like Google's PageRank, but instead of ranking pages for their popularity, it ranks friendships.

Doing well can mean several things: What you state is factually correct. Many people know or appreciate your work. The performance of your tool  or system is competitive with respect to some measure. You are getting a lot done. You are making a lot of money.

For most tasks we accomplish, no quantitative measure is satisfying

Most non-trivial work is not without errors. The best way to avoid errors is to do trivial work.

Technology allows us to be “always on”. To be part of a never ending conversation. Simply plug in, anywhere, and you can join in. Friends are spread out across every timezone, so there always are people available to interact with.

Any respectable  web 2.0 service is based upon the premise that we all want to share anything with the rest of the world.

I can’t predict the future, but I find it useful to think in extremes and see if it can help me get a better understanding of the present.

Many types of recommender systems exist such as non-personalized, demographic, content based, content based, collaborative (user based), collaborative (item based) and model based. Item based collaborative models have been applied successfully in commercial settings thanks to scalability and quality advantages as compared to others. Model based approaches differ from the rest which rely on memory of events. Instead they involve the creation of a probabilistic, decision tree or neural net model that attempts to identify the underlying logic to users’ choices.

Sparsity, Scalability, Cold-start, Implicit Ratings, Dealing with Multiple Criteria, Context-dependant Recommendations.

因为懒惰没有技术上的解决办法。

大多数人既然是人就继承了作为生物而应该天生拥有的进化本能和进化能力。长久的观察使我得出一个结论，大多数学生实际上是勤奋的，他们并不懒惰，他们只是花费了很多时间精力却没有收获而已。过分简单地把最终的收获归结于“勤奋”，而把颗粒无收归结于“懒惰”，只不过是于事无补的空洞评论而已，不为解决问题提供任何线索。

1) 不知道该干什么

弄不好，所謂的「多角度思考」其實就是一堆觀點和資料的羅列，而那些觀點和資料，不消說，全部來自互聯網

一篇東西有不同甚至彼此矛盾的論點不一定就能顯示你懂得「多角度思考」，假如沒有一套邏輯清晰的架構安放它們的話，這只能叫做混亂，或者「短路」

每個人處理資訊的方式都帶著點個性

The Core Sample is -- like its name sake -- a snapshot of a point in time. It captures the various levels of "knowledge" and where they reside. The diagram also illustrates the rationalization and codification of knowledge as it rises through the layers.

Starting at the bottom, at the very core, are people. This is where true knowledge exists.

The next layer up is where that personal communication is expanded to allow people to "talk" to others they do not know or cannot meet in person.