(source)

Facebook has been the hot networking site for quite some time now. Founded in 2004 by a teenager, this same teenager, Mark Zuckerberg, is now 23, has no degree, and is about 2300 times richer than I will ever be. (No, I am not bitter.)

Some colleagues asked me to join facebook today. My friends from MyDYO Inc. are there too. So I joined. Here are my impressions.

• The ads feel out-of-place. As a disclaimer, my blog is not any prettier, but I do not have millions and millions to spend on graphical design.
• It is far more popular than I expected. It seems that about 50% of everyone I know is on facebook. Including many people who do not have a web presence.
• Oddly, people seem to assume that the data put there is private.
• It is a walled garden. As far as I can tell, there is no way to share content through URIs without having visitors log into facebook. Not very RESTful. However, the application is very responsive.
• The search engine appears very limited. Running Google through this data would be much more fun!
• The first few minutes are fun. Finding out that you are more connected than you thought is always pleasing. However, I cannot see why I would spend much time in a walled garden where most of the content seems to be your list of friends you have no seen in years? There is a reason why I have forgotten all these names… I am busy.
• There is clearly a viral effect at work, but I do not understand why it would work better than with other networking sites I tried.
• I quickly browsed the applications. According to Seb, this is where the real value lies. And indeed, I was impressed. Thinking a bit more about it, I think that facebook serves as a form of OpenID: you sign on to facebook once and you can automagically use a large number of applications without having to create several accounts and reenter the same data, again and again. I see no reason why we can’t have an open-world non-proprietary facebook, other than the fact that we have not yet managed to get OpenID off the ground.

See also my post Academic blogging: why still bother?

(source)

A technological singularity is a rapid sequence of technological changes tearing apart our society. For example, imagine we can create smarter-than-human-beings machine. Suppose that, in turn, these machines can create other machines that are even smarter than they are. If the timing is just right, you could get infinite intelligence in a finite time. Of course, a technological singularity does not need to be so drastic. It suffices that we exceed the speed at which most human beings can adapt.

My own definition of a technological singularity is the achievement of such a high level of sophistication, that, as far as human beings are concerned, technological progress becomes irrelevant. It could be that we have such an advanced technology that our brain cannot even comprehend progress. Or maybe, we are all trying to kill each others so that the insanity can stop. Or it could happen on the day spammers find a way to get spam directly in our brains and we are all buying pills to get our penises to be longer.

It is hard to tell if such a singularity is a distinct possibility. For example, it could become increasingly expensive to improve our technological sophistication faster. One limit is the size of our brain. Your brain has a limited memory and processing speed. But we could possibly expand our brain or replace it with better hardware.

(As I said before, I do not care for AI. I do not want my laptop to be talking back to me. But I would not mind replacing my brain with a piece of hardware that gives me a photographic memory and twice the processing speed.)

For fun, assume that a technological singularity does happen. It may not be a catastrophe. For example, maybe we have the technology to keep us all alive nearly forever and in a quasi-paradise. What happens next? Clearly, our technology cannot improve further, and even if does, nobody cares.

My own prediction is that some strong religious figure would emerge and people would become highly spiritual. Science and technology would be frowned upon. It could even be that we would go back to a medieval state.

See also my posts Duck Typing, Artificial Intelligence and Philosophy, The Big Bang is Intelligent, and How artificial intelligences are already at war with us.

Michael has a long post on why it seems foolish to measure scientist according to one unidimensional metric (such as the H-index). His argument is mostly that you can game these metrics rather easily if you have a large enough social network. Given how hard people work at gaming the PageRank metric, and the often quoted fact that over 50% of all married people cheat on their spouse, we would be naive to think that researchers do not game the metrics. For that matter, it is known that several journals cheat to increase their impact factor (another unidimensional metric).

The question really is, does it hurt us that people play these games? After all, if we accept that the rule of the game is to get a high h-index, then why should I care how people go about it?

Michael is actually reacting on an article, The Mismeasurement of Science, which identifies several ill-effects of these unidimensional measures, including the facts that:

• many authors ignore or hide results that do not fit with the story being told in the paper because doing so makes the paper less complicated and thus, more appealing;
• science is becoming a more ruthlessly self-selecting field where those who are less aggressive and less self-aggrandizing are also less likely to receive recognition.

In turn, I conjecture that we have the following measurable effects:

• Science is becoming less attractive as a career. If you are going to pursue a high H-index, if this becomes your goal, then how is this more interesting, as a game, than to make a lot of money? Should we be surprised that Science Faculties are bleeding students while Business Schools are turning down students? When accounting becomes sexier than Physics, we have a problem. Women, who are less attracted to career where you compare the size of your appendage, are harder to find than ever in Computer Science. Should we get a clue?
• Research papers, while becoming easier to read and cite, fail to provide us with enough data to correctly appreciate the results and their applications. In particular, research papers are increasingly dismissed by practitioners who need not only a nice story, but also the full story, including the dirty secrets.

Whatever rules we set, they have consequences. I am particularly worried about the fact that we are making science uninteresting by redefining it from “scientific discovery” to “achieving a high H-index”.

Maybe we have to go back and ask fundamental questions. Why do we do science? What do we really expect from scientists? What should we really reward

See also my posts Are we destroying research by evaluating it?, On the upcoming collapse of peer review, and Assessing a researcher… in 2007.

I reminded a member of our staff of the following quote yesterday:

Computer science is no more about computers than astronomy is about telescopes. (Dijkstra)

But what happens to astronomy when everyone owns a telescope?

If you had millions of people using telescopes for 8 hours a day, what would happen of the astronomers? Can you still continue to do astronomy as if only there were only a few serious astronomers in the world?

See also my post The medium is the message, in Computer Science?

A common problem in information retrieval is that words are ambiguous. That is a fancy way of saying that you cannot tell the meaning of a word when you take it out of context. Some people claim that this problem must be solved by using the Semantic Web. I have long advocated that the Semantic Web is more of a solution in search of a problem.

We already have some good strategies regarding disambiguation, but I have wondered recently why we can’t use wikipedia to disambiguate words. After all, wikipedia knows the difference between Java (the island) and Java (the programming language). It turns out that Google has implemented and patented this very idea!

Bunescu, R. and Pasca, M., Using Encyclopedic Knowledge for Named Entity Disambiguation, EACL-06, 2006.

See? Who needs RDF to disambiguate words?

(Source.)

We just found a major MacOS bug, but there seems to be no trace of it on the Web, so I am posting this here hoping that someone can help. We tested several machines and whenever you have an ethernet connection, trying to do an HTTP POST request with a sizeable load (such as editing a large article on wikipedia) will fail. This does not happen with WiFi, only ethernet (with a cable).

We tested with several browsers. Apparently, some version of Safari would not suffer from this bug, but I could not confirm it.

The problem is not hardware-bound because running Windows XP on the same machine fixes the issue. So, it seems there is a major bug in Apple’s ethernet driver.

Help me!

Erik Duval asks for help. He points out that it is extremely difficult to figure out who cites him, how often, and so on. Using a tool offered by librarians (Web of Science) gives highly accurate, but also highly incomplete results. Meanwhile, Google Scholar fares better, but gives noisy data which overestimate how many people cite your papers. An astute reader comments on his article saying that you still have to take into account the blogosphere and other media.

My take on this? What tool actually matters is the tool other researchers use. If everyone uses Web of Science daily, and you are not there, then too bad.

I have one paper on PubMed, so, at least, I vaguely exist, as far as medical researchers are concerned. I have 5 papers on MathSciNet, so I exist a bit more for mathematicians. And so on.

Myself? I use Google Scholar. If you are not on Google Scholar, you do not exist for me.

How much impact you are having as a researcher is a fundamentally multidimensional problem. No researcher dominates any other researchers in every way. Trying to find one scalar measure that sums it up is futile, though it can be fun.

See also my posts Are we destroying research by evaluating it? and On the upcoming collapse of peer review.