out the functionality by themselves. Second order Al is automated function approximation. Learning has existed for a long time in AI of course, but Deep Learning means compositional function approximation. Our current approximator paradigm is mostly the neural network, i.e. chained normalized weighted sums of

out the functionality by themselves. Second order AI is automated function approximation. Learning has existed for a long time in AI of course, but Deep Learning means compositional function approximation. Our current approximator paradigm is mostly the neural network, i.e. chained normalized weighted sums of

out the functionality by themselves. Second order AI is automated function approximation. Learning has existed for a long time in AI of course, but Deep Learning means compositional function approximation. > Our current approximator paradigm is mostly the neural network, i.e. chained normalized weighted sums

out the functionality by themselves. Second order Al is automated function approximation. Learning has existed for a long time in AI of course, but Deep Learning means compositional function approximation. > Our current approximator paradigm is mostly the neural network, i.e. chained normalized weighted sums

out the functionality by themselves. Second ord=r Al is automated function approximation. Learning has existed for a long =ime in Al of course, but Deep Learning means compositional function approx=mation. Our current approximator paradigm is mostly the neural network, i.e. chaine= normalized weighted sums of

out the functionality by themselves. =econd order Al is automated function approximation. Learning has =xisted for a long time in Al of course, but Deep Learning means =ompositional function approximation. > Our current approximator paradigm is mostly the neural network, i.e. =hained normalized weighted sums

out the functionality by themselves. Secon= order Al is automated function approximation. Learning has existed for a =ong time in Al of course, but Deep Learning means compositional function a=proximation. > Our current approximator paradigm is mostly the neural network, i.e. c=ained normalized weighted sums