Well the FPGA is a closer analogy to the environment for the organisms. Organisms were heavily optimized for that specific environment. It would be like if you took a species of fish that only ever lived in a specific lake, and put them into a different lake that had a slightly higher PH, and they weren’t able to survive as well.
But I don’t disagree with your general point, evolution is surprisingly robust. Geoffrey Hinton has a very interesting theory about this here. That sexual reproduction forces genes to randomly recombine each generation, and so it prevents complicated co-dependencies between multiple genes.
He applies a similar principle to neural networks and shows it vastly improves their performance (the method is now widely used to regularize NNs.) Presumably it also makes them far more understandable like you mention, since each neuron is forced to provide useful outputs on it’s own, without being able to depend on other neurons.
Well the FPGA is a closer analogy to the environment for the organisms. Organisms were heavily optimized for that specific environment. It would be like if you took a species of fish that only ever lived in a specific lake, and put them into a different lake that had a slightly higher PH, and they weren’t able to survive as well.
But I don’t disagree with your general point, evolution is surprisingly robust. Geoffrey Hinton has a very interesting theory about this here. That sexual reproduction forces genes to randomly recombine each generation, and so it prevents complicated co-dependencies between multiple genes.
He applies a similar principle to neural networks and shows it vastly improves their performance (the method is now widely used to regularize NNs.) Presumably it also makes them far more understandable like you mention, since each neuron is forced to provide useful outputs on it’s own, without being able to depend on other neurons.