If you are looking to improve your skill set or steer business/research strategy in 2021, you most certainly have come across articles decrying a skills shortage in deep learning. Not that long ago, you would have read the same about a shortage of professionals with machine learning skills. In the earlier years of the 2010s, the emphasis would have been on a shortage of data scientists skilled in “big data.”

Andrew Ng telling us for years that “AI is the new electricity”, and the advent of AI in business and society is constantly suggested to have an impact similar to that of the industrial revolution. While warnings of skills shortages are arguably overblown, why do we seem to change our ideas about what skills are needed?

While job listings are notoriously whimsical, they can, in aggregate, tell a story.

If deep learning is a part of AI, why are there ~20% fewer jobs open for the latter? The answer is that the terms we use for these fields often have as much to do with trends and marketability as they do with any substantive differences. That’s not to say that we can’t differentiate the different categories based on technical characteristics, we’ll do that too!

Why do the “hot buzzords” change every few years and how do they relate to one another?

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