This may come as a shock to the layperson, but there's more to artificial intelligence than neural nets, and many non-NN AI approaches could arguably be said to "learn", and are thus "machine learning" too, depending on your definition.
I'm thinking of evolutionary algorithms, various other biologically inspired computation techniques (of which NN's are but one example), more traditional AI techniques such as expert systems, and a whole host of stochastic, non-biologically inspired algorithms.
I'm not super familiar with NN's myself, so I can't say whether the gigantically disproportionate attention from the media and the research community is deserved based on actual superiority in effectiveness of NN's compared to other techniques, or whether they're used and talked about mostly because that's what most people know.
It would be interesting to hear the thoughts of a non-NN AI researcher regarding this.
I'm thinking of evolutionary algorithms, various other biologically inspired computation techniques (of which NN's are but one example), more traditional AI techniques such as expert systems, and a whole host of stochastic, non-biologically inspired algorithms.
I'm not super familiar with NN's myself, so I can't say whether the gigantically disproportionate attention from the media and the research community is deserved based on actual superiority in effectiveness of NN's compared to other techniques, or whether they're used and talked about mostly because that's what most people know.
It would be interesting to hear the thoughts of a non-NN AI researcher regarding this.