ion. Our current approximator paradigm is mostly the neural network, i.e. chaine= normalized weighted sums of real values that we adapt by changing the wei=hts with stochastic gradient descent, using the chain rule. This works wel= for linear algebra and the fat end of compact polynomials, but it does no= work well for conditional loops, r