I believe neural networks are not quite the right approach, and their success is misleading. Historically, almost all ML approaches were based on separation in

I believe neural networks are not quite the right approach, and their success is misleading. Historically, almost all ML approaches were based on separation in Euclidean (vector) space, which is understandable because they were developed for much weaker computers. However, really useful ML tasks require to deal with huge nonlinearities, and the fact that you're training in linear space becomes less relevant. Neural networks have surmounted the nonlinear difficulty by increasing the number of layers. But it's a question whether a similar result couldn't be achieved with Bayesian networks on binary representations (which is approach I favor). There is some evidence that the precision of linear calculation in neuron (for example, resolution of the weights) doesn't really make much difference in the performance of neural network. Could it be that the neural learning through vector manipulation is just an artifact of the origin of the neural networks, and the really important thing is the overall organization of the network (the layers)?