Two years ago, I asked whether academic blogging was still relevant. At the time, two famous bloggers had stopped (Sébastien Paquet and Stephen Downes). Evidently, I kept on blogging. I even took up microblogging.

Let me revisit some of the benefits.

• Bloggers are more visible. This blog has over 900 readers. Some are students, others are engineers, teachers, entrepreneurs, researchers or professors.
• Blogging is good for knowledge management: leaving a trace of your thoughts is always a good idea. You are also forced to flush out your ideas and you get immediate feedback.

However, reading Daniel Tunkelang today, I realized that the most important benefit is the networking effect. A blog without a social network is nothing. Nobody wants to read a list of random thoughts. What makes blogging rewarding for me are the comments and the link I receive. But I enjoy even more reading others and commenting elsewhere.

In effect, good blogging makes you part of a rich and open community. That is very valuable.

How an individual evaluates his work is a fundamental intrinsic characteristic. If I had to classify researchers, for example, I would look at how they argue for the quality of their research.

Let me ask you, how do you know how well you are doing? 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. Can you quantify how good is one of my research papers, or this blog post? Even when you can use a quantitative measure, it is sometimes prohibitively expensive to compute it. Correctness is also somewhat relative. Most non-trivial work is not without errors. The best way to avoid errors is to do trivial work. Popularity is also relative: is McDonald’s better than the best restaurant in my neighborhood?

I think you can change the direction your career takes by changing your metrics.

A secondary characteristic is what you believe makes them good. Are you doing poorly because of the working conditions, or because you are an idiot? Most research papers spend little time on failures, but it would be interesting to see how people describe their failures. I find it interesting to hear famous people tell us why they are doing well. Some stess how brilliant they are. Others stress how much they worked. Others thank others for helping them.

It looks like the government is bailing us out. Wow! $400 millions. To put things in perspective, that is about 2000 times less than the recent bank bail-out in the USA. And I feel better about a university bail-out than a bank bail-out. But that’s just me.

• There is no such thing as an unbiased expert outside the Mathematics department. When your banker told you to borrow money and to invest it in the stock market, you did not really think he had your best interest in mind, did you? I am always amazed by how definitive the financial advice bankers give out. Ask to see their mathematical models and question their assumptions! Most often, you will find out that they have no model and they are just repeating corporate lines.
• Reproducibility is a lot harder than it sounds. Times and times again, I have been surprised by how difficult it is to reproduce the results of a given research paper. Financial experts often base their advice on case studies and they assume that these are reproducible. If people twenty years ago managed to get rich by buying cheap houses and reselling them for a profit, can we reproduce this scenario in 2008? Maybe. Maybe not.
• Long-term plans are much less useful than you think. As a researcher, I often make up plans. I am forced, every 5 years, to plan for the next 5 years so that my funding agency will give me some money. However, these plans always fall flat. The truth is that I am terrible at predicting the future reliably. Feel free to set goals for yourself however. Just do not be surprised if you have to reinvent your plans and your goals every 3 months.

Jeff Dalton reports on a presentation by Dave Jensen and David Smith on the Myths of Research in Computer Science. A key insight is that Computer Science Research is not about systems. Designing a better spam filter is a great idea, and might make you wealthy, but it is not what Computer Science is about.

I like the approach described by Jeff’s post:

Design an experiment to learn regardless of the outcome.

Of course, we all want to improve computations. However, you do not have to prove that your way is better than their way (the macho approach). This sort of contest gets boring rather quickly.

Unsurprisingly, almost all students at the University of Wisconsin-Madison say they prefer courses with webcasted lectures as opposed to campus-only lectures. What is more interesting is that 60 percent said they were willing to pay more for them.

Considering how much students pay for overpriced textbooks, it is maybe not so surprising that they are willing to pay extra for online content.

Source: Downes.

Industry has always advertised to scientists. However, this ad targeting biologists is… peculiar:

Source: Owen Kaser.