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While the continual one-upmanship of ever more intricate biologically plausible learning rules is interesting to observe (and I played around at one point with
by taliesinb 6y ago
While the continual one-upmanship of ever more intricate biologically plausible learning rules is interesting to observe (and I played around at one point with a variant of the original feedback alignment), I think OP's alternative view is more plausible.
Fwiw I am involved in an ongoing project that is investigating a biologically plausible model for generating connectomes (as neuroscientists like to call them). The connectome-generator happens (coincidentally) to be a neural network. But exactly as the OP points out, this "neural network" need not actually represent a biological brain -- in our case it's actually a hypernetwork representing the process of gene expression, which in turn generates the biological network. Backprop is then applied to this hypernetwork as a (more efficient) proxy for evolution. In the most extreme case there need not be any learning at all at the level of an individual organism. You can see this as the ultimate end-point of so-called Baldwinian evolution, which is the hypothesized process whereby more and more of the statistics of a task are "pulled back" into genetically encoded priors over time.
But for me the more interesting question is how to approach the information flow from tasks (or 'fitness') to brains to genes on successively longer time scales. Can that be done with information theory, or perhaps with some generalization of it? I also think it is a rich and interesting challenge to parameterize learning rules in such a way that evolution (or even random search) can efficiently find good ones for rapid learning of specific kinds of task. My gut feeling is that biological intelligence has many components that are ultimately discrete computations, and we'll discover that those are reachable by random search if we can just get the substrate right, and in fact this is how evolution has often done it -- shades of Gould and Eldredge's "punctuated equilibrium".
(if anyone is interested in discussing any of these things feel free to drop me an email)
- timlarshanson 6y agoI don't follow how punctuated equilibrium fits in here, but I do agree with your general intuition. Evolution 'likes' spaces that are navigable. Protein evolution is, in my mind, the paragon of this: even though the space of possible amino acid sequences is tremendously huge, since 2010 relatively few new folds have been discovered, and it seems that there are only ~ 100k of them in nature. See https://ebrary.net/44216/health/limits_fold_space https://ebrary.net/44216/health/limits_fold_space Proteins get the substrate right, and a handful of folds are sufficient for all the interactions an organism could need -- so evolution can find new solutions quickly. (It only took hundreds of millions of years for LUCA's parents to figure /that/ out.) It seems that being able to parameterize the problem space such that solutions are plentiful and accessible via random search is nearly equivalent to solving the problem... In this case, using an ANN to stand in for ('parameterize') organismal development is entirely reasonable (and would hence 'solve' the problem), look forward to seeing the results of that. But as with the OP I'm cautious as to the efficiency of backprop vs evolution.