We are starting to see a bit of a pushback although it's hard to discern amongst all the hype. There's some recognition that deep learning is just one technique that happened to pop (to use Rodney Brooks' term) for a variety of reasons but that we haven't made huge progress in cognitive science and other fields.
Deep learning is the current shiny toy but I suspect we'll find it isn't actually sufficient for a lot of things we want to do and we'll run into a wall a lot of people aren't expecting.
One of the most popular problems to be solved in modern IT systems is that of keeping the state of your application distributed across a number of machines.
Paxos is one of the algorithms that solves this problem by giving you a protocol that allows the sets of machines to agree upon a set of operations that would all be applied to their states thus giving you a set of machines in the same state.
A simple example, is if you had a set of 3 machines starting with state of “0” and wanted to add “1” to their state. Paxos would define how they should communicate so that in the end, even if one of the machines failed during execution, would all end up with a state of “1”.
Solid answer. There is no point to worry about classes. All I have to know is what I like and find interesting. Then find out what I need to know to do that.
/Cognitive scientist