Dynamical approaches to intelligence

I had an enjoyable visit to Goettingen (Stadt der Wissenschaft, or City of Science) last week, where I was visiting the Max Planck Institute for Dynamics and Selforganisation. I had interesting discussions with people there and I even had the chance to walk around and see the city. Among other nerdy things, I visited the memorial of Gauss (my academic ancestor).

On the technical side, I learned (or more accurately, was reminded in substantial detail) about dynamical approaches to learning and intelligence. These approaches are based on the view that much of what we attribute to intelligence may be explained in terms of phenomena that arise in nonlinear dynamics and “complex systems” theory. So, for instance, I learned about notions such as “unstable attractors”, a seemingly paradoxical phrase but one that explains how fleeting structures can arise in an otherwise deterministic process.

Most substantially, I finally understood the core ideas behind the whole business of self-organization principles. If one were to begin with a dynamical system, induce it to become weakly chaotic so that it can explore a subset of its phase space effectively and then constrain the system to stick to regions where it has some notion of predictability of behavior – then the system will automatically hone in on a variety of attractors (stable or intermittent) that correspond to interesting behaviors. One can define processes for generating such weak chaos and for selecting actions to improve predictability without committing to any specific task. Then – if one does this right – one can extend this whole notion to high-dimensional systems, where the criticality of the behavior also yields scale-free search of the space of possible behaviors. I find this really neat because this answers one of the questions I have been pondering for a while; and provides what I consider to be a better answer than random or evolutionary search.

Of course, while this concept is a nice approach to bottom-up emergence of behaviors, it does not quite explain how more sophisticated and hierarchical planned behaviors come about. Nonetheless, it was good (and very timely) for me to learn about these bottom-up notions – I can now go explore how this ties in with other top-down notions that I have already been studying. In the end, I am fairly non-partisan in that I believe the real answer is most likely in between the two extremes that people sometimes gravitate towards.

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