The "hype cycle" for machine learning is broken in the sense that advanced machine learning is stuck in the "trough of disillusionment" yet many beginners have just leaped off the "technology trigger" That is, people's experience is not well modeled by the "hype cycle".
If there is a single example to consider it is that almost every day we see another re-implementation of the NIST digits case. Have you ever seen anyone do a similar but different problem?
Probably not. Because the vast bulk of work in a machine learning project is getting the training set, data prep, wrangling with details in the algorithms etc. It is high risk.
Because so many people are new to it they blame themselves instead of the technology so it is not so clear we are in the "trough of disillusionment"; machine learning works some of the time to create miracle products from large companies, but it is not unusual to hear that "we'll never do that again".
Future advances will depend not on algorithms so much as (i) large investments to produce training data (could we get it to work with letters instead of digits? do you take that for granted?) and (ii) finding clever ways to get the training data at less cost.
