Artificial Intuition a key to AI?

Convergence08 was a great conference with many interesting people and ideas. Thankfully the number of crackpots was very low, and even the “new age” mysticism stuff was at a minimum. Instead I found hundreds of authors, doctors, biologists, programmers, engineers, physicists, and more clear thinking folks all interested in how the new technologies will shape our world in ways more profound than we have ever experienced before.

My favorite insights came from Monica Anderson’s presentation on her approach to AI programming, which she calls “Artificial Intuition“. Unlike all other approaches to AI I’m familiar with Anderson uses biological evolution as her main analogs for conceptualizing human intelligence. I see this approach as almost a *given* if you have a good understanding of humans and thought, but it’s actually not a popular conceptual framework for AI, where most approaches rely on complex algorithmic logic – logic that Anderson argues clearly did not spawn human intelligence via evolution. Yet Anderson is by no means a programming neophyte – she’s a software engineer who has researched AI for some time, then spent two years programming at Google and then quit to start her own company, convinced that her AI approaches are on the right track.

Anderson’s work is especially impressive to me because as someone with a lot of work in biology under my belt (academically as well as corporeally) it has always surprised me how poorly many computer programmers understand even rudimentary biological concepts such as the underlying simplicity of the human neocortex and the basic principles of evolution which I’d argue emphatically have defined *every single aspect* of our human intelligence over a slow and clumsy, hit and miss process operating over millions of years. I think programmers tend to focus on mathematics and rule systems which are great modelling systems but probably a very poor analog for intelligence. This focus has in many ways poisoned the well of understanding about what humans and other animals do when they … think… which I continue to maintain is “not all that special”.

Anderson’s conceptual framework eliminates what I see as a key impediment to creating strong AI with conventional software engineering – ie having to build a massively complex programmable emulation of human thought. Instead, her approach ties together many simple routines that emulate the simple ways animals have developed to effectively interact with a changing environment.

Combining Anderson’s approach to the programming with the physical models of the neocortical column such as IBM Blue Brain would be my best bet for success in the AI field.