All of those orange, cyan, and yellow dots represent digital ants fighting for supremacy. This is a match to see who’s AI code is better in the Google backed programming competition: The AI Challenge. Before you go on to the next story, take a hard look at giving this a try for yourself. It’s set up as a way to get more people interested in AI programming, and they claim you can be up and running in just five minutes.

Possibly the best part of the AI Challenge is the resources they provide. The starter kits offer example code as a jumping off point in 22 different programming languages. And a quick start tutorial will help to get you thinking about the main components involved with Artificial Intelligence coding.

The game consists of ant hills for each team, water as an obstacle, and food collection as a goal. The winner is determined by who destroyed more enemy ant hills, and gathered more resources. It provides some interesting challenges, like how to search for food and enemy ant hills, how to plot a path from one point to another, etc. But if you’re interested in video game programming or robotics, the skills you learn in the process will be of great help later in your hacking exploits.

In a little more than a month, tens of thousands of people around the world will attend a class on Artificial Intelligence at Stanford. Registration for this class is still open for both class ‘tracks’. The “basic” track is simply watching lectures and answering quizzes, or a slightly more advanced version of MIT OpenCourseware or Khan Academy. The “advanced” track is the full class, requires homework and exams, and aspires to Stanford difficulty.

With thousands of people taking this class, there’s bound to be a few study groups popping up around the web. The largest ones we’ve seen are /r/aiclass on Reddit and the stack overflow style aiqus. The most common reply to ‘what language should I learn from this class?’ is Python, although there’s an online code repo that has the text’s working code in Lisp, Java, C++ and C#.

If AI doesn’t float your boat, there are two more classes being taught from Stanford this fall: machine learning and introduction to databases. Any way you look at it, you’re getting to take a class from one of the preeminent instructors in the field for free. Do yourself a favor and sign up.

Thanks to everyone who sent this in. You can stop now.

Can Super Mario teach you to think? That’s the idea behind using a simple version of the game to teach artificial intelligence. [Oddball] calls this The Mario Genome and wrote at program that can take on the level with just two controls, right and jump. He gave the script 1000 Marios to run through the level. It then eliminates the 500 least successful and procreates back to 1000 using the 500 most successful. In this way the program completed the level in 1935 generations and completed it in the quickest possible time in 7705 generations. He’s posted the script for download so that you can try it yourself. It’s an interesting exercise we’d love to see applied to more random games, like Ms. Pac-Man.

[via Reddit]

[McLurkin] was an entertaining speaker and had an interesting view of robotics. He is optimistic that robot parts will become more modular, so it will be easier to build them, and more importantly, faster to design them.

Some quotes:

• “There’s more sensors in a cockroach’s butt than any robot”
• “12 engineer years to design, 45 minutes to build”
• “If it can break your ankle, it’s a real [rc] car.”

His swarm (pictured above) is made up of over a hundred small identical bots, but he only brought about a dozen with him. The demo was still quite impressive. He had the robots spread out, clump together, play follow the leader and circle the wagons. Each behavior had a very simple rule behind it. To spread out, for example, each robot tries to move away from it’s nearest neighbor. The really fun part was when he had the robots perform a physical bubble sort. The rule for this was that each bot tried to put a higher-id bot on one side and a lower-id bot on the other. After a minute or so of bumping around the bots all lined up in id order.

I was interested in the details of the robot itself. Here’s a picture with the parts labeled.

Each robot has a unique ID number. They communicate with each other via IR and have sensors so that they can tell which direction and how far away the other bots are. The lights on top are just indicators so you can tell what the bots are doing. A mesh network is rebuilt several times a second, creating a directed graph from the ‘leader’ (which can be any arbitrary bot) that connects to each bot in the swarm. Any bot can act as a repeater, relaying instructions to bots that can’t talk to the leader directly.

Robot swarms are not a new idea: they’ve been floating around as concepts for many years. However, [McLurkin] was one of the first to actually build and program a large swarm (at one time he held the record for the largest robot swarm in the world). The idea caught on with researchers and today there’s even an open source robot swarm project. If you’re not up to building a whole bunch of robots, there are also simulators.

After the demo, we asked [McLurkin] about the cost of the robots. He said he didn’t know for sure, but estimated at least $2000 per bot. When we commented that “that’s a lot of money for 100 bots”, he pointed out that compared to the $20K+ that research robots can go for, it’s a bargain. He also said “This whole new world of hobby robotics just didn’t exist in the 90′s”. For robots to be deployed in swarms of hundreds or even thousands, in situations where they can get damaged or lost (search and rescue, military exercises) the cost will need to drop dramatically.

Here he is packing up his robot swarm. After the demo, we half expected them to pack themselves – no, they don’t.

For more info on robot swarms, their inspiration and possible uses take a look at [McLurkin]‘s web site.