There is a lot of hype around deep learning. especially at business-oriented AI events. I suggest some questions to think about for companies who are considering using DL.
Ehud’s guidelines for evaluating AI systems: keep it simple, keep it ethical, be careful, do proper stats, and be skeptical
Managing research projects is not much fun, but it is important. I have seen many research projects which did not reach their potential or even failed completely, because of poor management.
The NLP/AI community needs to do a better job of dealing with multiple hypotheses, otherwise a lot of our results will be garbage.
I think we should use rules to make simple high-value decisions, and learning to make complex low-value decisions, within an architecture where ML decision makers are embedded in a rules-based framework.
Imformation graphics work well for domain experts. but they are not nearly as useful for junior professionals. And the “man in the street” may struggle to understand anything more complex than a simple bar chart.
My father died recently, and I spoke at his funeral about a trip we took together to Baja California (Mexico) when I was 11 years old.