Like many people, it seems, my passion for artificial intelligence has been re-kindled recently. Douglas Hofstadter and Jeff Hawkins were the main re-kindlers for me. Now I’ve set off exploring.
I’m hoping to push the boundaries of artificial intelligence, creating something that can learn, explore, generalise, theorise, and surprise.
Because it would be so cool. Also, it seems self-evident that the tools we build should have some basic intelligence. Software and games use tricks to appear intelligent, but they are mindless zombies, unable to deal with even slightly new situations, and will happily bang their heads against a brick wall until, um, they’re taken out by a zombie hunter, or something. Computational intelligence has the potential to make technology do what we want, with robust adaptability. The cost is giving up precisely defined behaviour, as well as vastly more computation and memory requirements. Not to mention the difficulty of developing all this. But smart people are working on it. And it is fun.
I’m convinced that Jeff Hawkins’ Hierarchical Temporal Memory (HTM) is the proper basis for such computational intelligence. Of course, HTM is a general theory and has not yet been worked out at a level of detail applicable to most interesting problems. My strategy will be to focus on examples. To attack specific problems as a way to inspire development of the theory. I’ll design simulations with opportunities for learning and watch what goes wrong.
HTM is based on a memory system, arranged in a hierarchy of increasing abstraction, where the memory is simultaneously a predictive function. So it can recognise things, or situations or whatever, and infer what is happening and what may happen next. There is no emotion, no desire, no fear. Just recognition, generalisation, analogy. Is that scary? Of course every technology will be put to nasty uses, but I think it’s hard to argue that more intelligence is a bad thing.
Anyway, looks like we’ve got a lot of work to do. Better get on with it.