MEAIs can help embed some useful ways of thinking about artificial intelligences in general. Each matchbox corresponds to a 'state' in which a decision has to be made. For most real-life problems (and indeed most games other than very simple ones) there are a vast number of states, making MEAIs an impractical implementation method. But by helping us conceive of an artificial intelligence as any machine that turns 'states' (or information sets) into decisions, with performance - the quality of the intelligence - being gauged according to how closely certain 'desirable' states (e.g. winning games) are achieved, we can learn to avoid our near-compulsion to anthropomorphise artificial intelligences, and perhaps think about our own intelligences - their design trade-offs and clever workarounds - in a different way too.
MEAIs also point us to the importance of learning in artificial intelligence design. Specifically, they highlight that learning is a way of converging on intelligent behaviour without needing to have that intelligent behaviour 'built in' by the designer. Learning is not necessary for artificial intelligence; a matchbox AI could simply be pre-programmed with optimal moves. However, learning becomes a more important design principle when the 'game' becomes sufficiently complex that working out optimal behaviour is simply too difficult. AIs which learn are much more interesting, because they can surprise us by finding innovative strategies.
|"Hi, I'm the Cyber Research Systems T-850 Terminator. |
I'm here to tell you that by-and-large, articles about
artificial intelligence that feature pictures of me or my
colleagues are probably not worth reading. Except this one."