Media has previously reported on the Ultraswarms project, aiming to create swarms of Linux-powered miniature helicopters with a hive mind. For those who wonder how the project is coming along, I can report that we have been able to use artificial evolution to automatically create neural networks to control the helicopters. In other words, the helicopters learn to fly all by themselves through trial-and-error, which is useful as they are pretty darn hard to control manually. In fact, the evolved networks work better than the best human-made solutions. Work continues...

They say Digg is bigger than Slashdot than these days. Bigger, better, newer, 2.0 and what not.

Indeed, I find myself scanning the Digg front page more often than Slashdot's ditto. Far too often, actually. Obsessively often? Well well, I actually get proper work done now and then.

Still, it's simply not true that the quality of the "news" on Digg's frontpage is higher than the news on Slashdot. On the contrary, Digg suffers from a disgusting amount of mob mentality. Half of what's on the front page is not news at all, merely short text snippets propagating rumours that everybody's already heard or views that most people already agree with. Which is why they get digged to the front page: people like to hear/read what they already believe in.

And this is not really a problem with Digg, it's a problem with people. (And like all problems we can't really do anything about, it's not even worth thinking of it as a problem, just as a fact.)

Slashdot, on the other hand, has sometimes-competent editors that sometimes put an effort into selecting and editing stories. While I might learn something I didn't already know from either Digg or Slashdot, I'm far more likely to learn something new about something I didn't already know anything about from Slashdot. Crucial difference.

So, back to the question: Why do I keep reading Digg when Slashdot is better? Because the Digg is updated more often, and the items are shorter. It's that simple. I think.

Who believes in artificial intelligence nowadays? Not many, it seems. Some say that if we were so simple that we could understand ourselves, we would be so stupid that we couldn't. So we have to make the AI construct itself! That's the topic of my latest blog post, where I also claim that the secret lies in using computer games to do this.

Me and Renzo are working on a paper on how to automatically create fun racing tracks. And not only that, but racing tracks that are fun just for you - which means that we must first model how you drive with a neural network, and then use this model of your driving to create the tracks.

Grand plans, but will it work? Well, we'll see soon. In the meantime, here's a question for all of you: what exactly is it that makes a racing game fun? And what is it that makes a particular track/circuit in racing game fun?

Of course, in the end we want some quantitative measure we can put into our code, but the quantitative part is really up to us. What we're looking for right now is suggestions, ideas. Any sort. What makes racing fun? Please leave your suggestion below, or in the comments to this blog post.

With all the attention (rightly) given to the DARPA Grand challenge(s), it is a bit surprising that current car racing AI is so sloppy. Computer-controlled cars typically drive according to predefined racing lines and don't learn or adapt their driving styles to your driving. One notable exception is Microsoft's inventive AI in Forza Motorsport, where you can train "drivatars" to drive just like you.

At the University of Essex we are instead working on using evolutionary computation (such as genetic algorithms) and neural networks to control cars. The objective is to have the networks learning robust competitive driving behaviour without human input, and further to continue refining its behaviour when facing new tracks and opponents. So far we have come pretty far in the case of a single car on a track, and we are making progress on the two-car case. More details here, including videos and original papers.