Following up on Notions on AI and Elixir, redux, I’ve been speculating about what an interactive testbed for cortical modeling might look (and act) like.

In Media for Thinking the Unthinkable, Bret Victor discusses and demonstrates how interactive, symbolic, and visual modes can act in concert to aid in thinking. If you’ve never watched this talk, please do so now (I’ll wait…).

Although realistic modeling of the Neocortex isn’t currently (and may never be) practical, a spherical cow model might still be useful as a testbed:

The spherical cow is a humorous metaphor for highly simplified scientific models of complex phenomena.

Details

The human neocortex contains ~200 billion neurons, but only ~150K cortical columns. So, we could model each column as a (BEAM) process. If need be, subsidiary processes could be used to handle finer details.

Although the neocortex isn’t rectangular, we could use rectangular data structures to aggregate overall state. For example, a 400 x 400 tensor could contain data on 160K columns. This data could then be presented visually (e.g., one pixel per column).

Given this visual approach, optical character recognition (OCR) might be a useful test case. Generating test data (e.g., rasterized and/or distorted renderings of fonts) would be easy, making this a good fit for supervised learning.

With appropriate support infrastructure (e.g., for recording and playback), users might be able to explore various data sets, modeling approaches, etc. (And a pony…)

In any case, that’s more than enough speculation for now. As always, helpful comments and suggestions are welcome…