Geoffrey Hinton and Jürgen Schmidhuber are convinced that neural nets trained with backprop will likely lead to AI with minimal additional fixed-function structure. Schmidhuber's intuition is that recurrent neural networks (RNNs) are capable of universal computation, so given sufficient computational resources the RNN can learn any task, including generalization, goal and action selection and everything else that we associate with intelligence. Hinton's intuition is that RNNs essentially accumulate representations in their hidden state vectors that are very similar to human thoughts ("thought vectors"). Hinton thinks that thought vectors will naturally lead to the kind of reasoning that humans are capable of. In his view the human brain is basically a reinforcement-modulated recurrent network of stacks of autoencoders (needed for unsupervised representation learning).
Pedro Domingo sees such universally that may lead to AI in all major fields of AI: (1) Symbolists have inverse deduction (finding a general rule for a set of observations), (2) Connectionists have backprop and RNNs (see above) (3) Evolutionists have genetic programming (improvement of programs via selection, mutation and cross -over), (4) Bayesianists have incremental integration of evidence using Bayes' theorem, e.g. dynamic Bayes networks, and finally (5) Analogizers have kernel machines and algorithms to compare things and create new concepts.
Domingo's hypotheses is basically that only a combination of various of these approaches will lead to AI.