That’s a question that many people ask themselves and professionals in the data analytics field. However, they get different answers depending on who they ask. Naturally, the A.I. professional will tell you that of course, since A.I. methods are much better than conventional machine learning ones, while the field is booming lately. The data scientist may have a more retrained approach, as she is more likely to look at the matter scientifically, expressing some cautiousness about how influential A.I. professionals will be in the data science field. As someone who is both in A.I. and Data Science, perhaps I could offer a more balanced perspective.
First of all, an A.I. professional is a specialist in A.I. methods and if we are thinking about how this person can do a data scientist’s job, we are looking at someone who focuses on data analytics, rather than some other part of A.I. (e.g. robotics, theoretical A.I., etc.). Also, when we are examining a data science professional, we are looking at someone who is not in A.I. and who uses mostly conventional data science methods for the data analytics problems he tackles.
In my latest book, I outlined the importance of A.I. and how it is very influential in the data science field and the role of the data scientist. I even encouraged people to be kept up-to-date about the developments of A.I. as I predicted it will have an important role to play in the years to come. However, I did not urge anyone to drop what they are doing and focus on A.I. methods alone. If someone is already in the field, that’s great, since they already have developed the mindset of the data scientist and have mastered some of the tools, so by studying A.I. methods for data analytics, they are expanding their skill-set. That’s different from becoming A.I. specialists though. The A.I. specialists may be great at tackling Kaggle competitions, where the data is in a pretty clean and structured form (or at least mostly structured). However, this doesn't automatically make them adept at handling all kinds of data, like a data scientist does.
It’s really hard to make predictions about things involving people and their work, as the market is a chaoit system. However, I can attempt to venture an educated guess about what is most likely to happen, if things continue evolving the way they do. So, as A.I. becomes more and more versatile and more robust in tackling data analytics problems, it is bound to dominate over other data science techniques. So, if you are happy using SVMs or random forests, for example, you may want to rethink your toolkit! Yet, it is unlikely that A.I. will fully automate the data science process, much like statistics have not become fully obsolete just because there are several statistical programming environments out there (e.g. Statistica, R, SAS, etc.). Statistics is and is bound to remain useful because it is much more than its techniques. The same goes for data science. Even if all the conventional methods used by a data scientist become obsolete, giving way to A.I. ones, people will continue asking questions about the data, forming hypotheses, analyzing problems so that they can be modeled as data science ones, etc.
Of course, people will still communicate with the stakeholders of the projects, create visuals, do presentations, etc. So, even if the A.I. professional is bound to be an asset to an organization, he is most likely going to be part of a data science team, working side-by-side with a data scientist. As for the latter, she will be more knowledgeable about A.I. methods and will spend more time on other parts of her job, rather than doing feature selecting and building a series of models, since that’s something that will be automated by an A.I. system.
Therefore, unless a major breakthrough happens in the next few years, I’d recommend you are a bit skeptical about the A.I. paradigm shift that many evangelists talk about, as if it’s the coming of a new Messiah. It would be nice if everything was suddenly easy and smooth, due to A.I., but I wouldn’t uninstall my data science software just yet...
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.