VirgoLab

Money is the poor man’s credit card.

The car has become the carapace, the protective and aggressive shell, of urban and suburban man.

Why is it so easy to acquire the solutions of past problems and so difficult to solve current ones?

The trouble with a cheap, specialized education is that you never stop paying for it.

The future of the book is the blurb.

"I may be wrong, but I’m never in doubt.”

Politics offers yesterday’s answers to today’s questions.

When all men think alike, no one thinks very much.

As you can see from above, it is certainly possible to have a good collaborative filtering system without a recommendation engine

Collaborative Filtering (Wikipedia definition) is a mechanism used to filter large amounts of information by spreading the process of filtering among a large group of people.

The important thing, one that not many social sites realize, is that a (CF) system that doesn't automatically match content to your preferences, is inherently flawed. The reason for this is simple: Unless you can achieve perfect diversity and independence of opinion, one point of view will always dominate another on a particular platform. The dominant point of view on the social web is a left-leaning one, and without the ability to get the most appropriate pieces of content to the people that care most about them, the right-wing point of view gets buried almost every time.

Detection

Knowing the anatomical reasons for decision support and analytics in brain function gives a higher importance to create decision support systems

1918年晋北肺疫流行，疫情过后，关于这次疫情的所有情况，同样编成了三本一套的《山西省疫事报告书》（王承基总编纂，中华民国八年六月中华书局承印）

传统的“社区”早就具备网络营销的基本条件，猫扑和天涯都 是上一个时代不错的例子。那么更加新潮的SNS怎么会不行呢？当然行，问题还是在人，或者说在于资源。网络营销对于大多数年轻的团队来说，都是一件很头疼 的事情。把一个社区运营好，靠的是线上的组织策划能力。而要把网络营销业务做得有声有色，那却要靠现实资源的整合。

Peter Norvig, Google's research director, offered an update to George Box's maxim: "All models are wrong, and increasingly you can succeed without them."

Once you have a model, you can connect the data sets with confidence. Data without a model is just noise.

But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete.

There is now a better way. Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

What can science learn from Google?

This kind of thinking is poised to go mainstream.

without the testable predictions made by the theory, we'll never be able to tell how precisely it is wrong