We hear a lot about deep learning (DL) lately, mainly through the social media. All kinds of professionals, especially those involved in data science, never get tired of praising it, with claims ranging from “it’s greatly enhancing the way we perform predictive analytics” to “it’s the next best thing since sliced bread or baked bread for that matter!” What few people tell us is that most of these guys (they are mainly male) have vested interests in DL, so we may want to take these claims with a pinch of salt!
Don’t get me wrong though; I do value DL and other A.I. methods for machine learning (ML). However, we need to be able to distinguish between the marketing spiel and the facts. The former is for people poised to promote DL at all costs (for their own interests), while the latter is for engineers and other down-to-earth people who prefer to form their own opinions on the matter, rather than get all infatuated with this tech like some mindless technically inept fanboy.
Deep Learning involves the training and application of large ANNs to predictive analytics problems. It requires a lot of data and it promises to provide a more robust generalization based on that data, definitely better than the already obsolete statistical models, whose performance in most big data problems leaves a lot to be desired. Still, it is not clear whether DL can tackle all kinds of problems. For example, it is quite challenging to acquire the amount of data that is needed in order to solve fraud detection or other anomaly detection problems. When it comes to classifying images, however, the data available is more than adequate to train a DL network and let it do its magic. In addition, if we are interested in finding out why data point X is predicted to be of value Y (i.e. which features of X contribute the most for this prediction), we may find that DL isn’t that helpful because of the black box problem that it inherently has, just like all other ANN-based models. If however all we care about it getting this prediction and getting it fast, a DL network is sufficient, especially if we train it offline before we deploy it on the cloud (or on a physical computer cluster, if you are more old-fashioned).
Deep Learning can be of benefit to data science as it is a powerful tool. However, it’s not the tool that is going to make all other tools obsolete. As long as there are other parts in the pipeline beyond the data engineering and data modeling ones (e.g. data visualization, communicating the results, understanding the business questions, formulating hypotheses, among others), getting a DL system to replace data scientists is a viable option only in sci-fi movies. People who fantasize about the potential of DL in data science, imagining it to be the panacea that will enable companies to replace data scientists probably don’t understand how data science works and/or how the business world works. For example, someone has to be held accountable for the predictions involved and that person will have to explain them, in comprehensive terms, to both her manager and the other stakeholders of the data science project. Clearly, no matter how sophisticated DL systems are, they are unable to undertake these tasks. As for hiring some technically brilliant idiot to operate these systems and be a make-believe data scientist, with the salary of an average IT professional, well that’s definitely an option, but not one that any sane person would be likely to recommend to an organization, given that she wants to keep that organization as a client. If such a decision is to be made, it is most likely going to come from some person who cares more about pleasing his supervisor by telling her what she wants to hear, than about saying something that is bound to stand the test of time.
All in all, DL is a great tool, but we need to be realistic about its benefits. Just like any other innovative technology, it has a lot of potential, but it’s not going to solve all our problems and it’s definitely not going to replace data scientists in the foreseeable future. It can make existing data scientists more productive though, especially if they are familiar with A.I. and have some experience with using ANNs in predictive analytics. If we keep all that in mind and manage our expectations accordingly, we are bound to benefit from this promising technology and use it in tandem with other ML methods, making data science not only more efficient but also richer and even more interesting than it already is.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.