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It seems clear to me that the biggest advances will be made through evolutionary and developmental neural networks where evolution lays down the algorithm that
by rdlecler1 8y ago
It seems clear to me that the biggest advances will be made through evolutionary and developmental neural networks where evolution lays down the algorithm that builds the gross neural network architecture and learning then refines this. However this will need massive amounts of computational power because you have G generations of population size P, and each individual phenotype needs to go through a developmental step (neurogensis) and then an evaluation step. On top of that we need a good genotype to phenotype map specifically for neural networks.
Conveniently gene regulatory networks that would control cell growt, division, and wiring up the neurons are themselves represented mathematically as neural network so in effect you’re evolving one class of neural networks the build another class of neural networks. Nature is quite elegant.
- MrQuincle 8y agoCheck hyperneat and for example also the work from Josh Bongard on GRNs. Shorthand is evo-devo. https://m.youtube.com/playlist?list=PLAuiGdPEdw0iRhEnF5yPuqegKVjKktDni https://m.youtube.com/playlist?list=PLAuiGdPEdw0iRhEnF5yPuqe... I once built a simulator that used evo-devo to evolve morphology of modular robot organisms in response to environmental factors, completing evo-devo-eco. If I remember it right it was creating a snake at night and disassembling during the day. The fitness function becomes complicated though. I think it was something like a distance function in body morphology plus environmental condition (light).
- Eridrus 8y agoEveryone who has an approach that hasn't worked likes to say "the right thing to do is to combine the best of both worlds", and it's sort of tautologically true that if you have orthogonal approaches that can be meaningfully combined you will do better. The unanswered implicit question is how to do this combination, and whether the actual gain will be large enough to justify a mor.complicated system. So, yes, we should try combining symbolic AI, decision trees, genetic algorithms, etc with deep learning, but the results from these combinations haven't all been super convincing.