Without seeing the demo myself, I couldn't tell you for sure -- I wonder if anyone has ported that demo or a similar demo?
Anyway, just remember that conceptually neural nets just take an input and produce an output. The input might be the angle of the seal's nose to the center of the ball, a measure of roll on the ball and whatever else you can think of, and at some point there's a threshold that's going to get crossed. Based on the input and output values, you just quantify the error and then the network basically takes care of the rest internally.
If there's an enthusiastic enough response to this intro to neural nets, I'm sure we'll be able to get you more good stuff on AI -- Baysean lets, decision trees, genetic algorithms, Markov processes, etc. -- it's all good stuff worth knowing about and it's becoming more pervasive in society than you'd think -- your car's transmission, that computer game you love playing, your spam filter, ..., and the list just goes on and on.
If anyone has any specific requests on future AI pieces, feel free to mention them below.