In a survey on "scientific discovery", I would have expected more examples than face and image recognition and natural language processing, which are so stale at this point.
Healthcare? Physics? Chemistry? Biology? Sociology?
Healthcare? Physics? Chemistry? Biology? Sociology?
Deep learning does not do this.
Data with less noises are what most deep learning and non statistical models does well. Meaning that image, nlp, etc.. deep learning does well. But data with lots noises/uncertainty/variance or even data that isn't large enough, such as time series, currently statistical models are still king (https://en.wikipedia.org/wiki/Makridakis_Competitions).
Even with healthcare you're answering a question/ hypothesis. This is where statistical models strength lies because all statistical models are hypothesis tests and vice versa. There are very little opportunity in healthcare where you would use deep learning compare to statistic. I've seen NLP can be of use but the majority of work in healthcare are inference/casuality base (this is why they use propensity model so much). I'm in this space public healthcare.
More generally, it seems that time series forecasting so far has mostly attracted statisticians with little DL experience [1]. Now that there is $50k prize, this will be a good test of whether statistical methods are "still king". If I were to enter this field, I'd probably look into latest transformer based models, especially the ones used to model raw audio data, e.g. [2].
There's also a real possibility that whenever any strong forecasting method is developed (DL based or otherwise) it's not published as the developers simply use it to make money (betting, stock market, etc).
[1] https://journals.plos.org/plosone/article?id=10.1371/journal...
This is just one of the two competitions for m5. The other one is uncertainty.