This trend is in most companies business-driven, in others it is technical-driven. Few companies have technical leadership that can manage true AI resources. If you remember the ML courses from Uni and experts in that field, you can imagine why. In many universities AI departments are assigned to schools of psychology and philosophy. Only companies with a deep engineering culture as those mentioned here can build up true AI departments.
The other driver is business-driven. And this is where management demands 'AI experts', when what they really want is data-miners. And in many cases management prides themselves on 'AI algorithms', but we know that this is a term for anything that gets the results that management wants and may be far from intelligent and in most corporate cases a bunch of SQL scripts.
What's the potential path forward (say projecting 10 years ahead) from the current growth in demand for data mining centric people?
I mean people go and study in response to demand. They learn data mining and AI at Universities. I think it's often people with backgrounds or aptitude in maths. What will the 22 year old with an aptitude for maths that is learning R, SQL, AI-for-business and such be doing in 10 years?
I don't know if the starting point matters much. "Results Driven," even if its optimising inventory or making ad purchasing decisions or data mining old DBs is not a bad place to "search" for advancements. Not everything needs to be fundamental research.
I doubt the 22 year old you are referring too will run out of problems to solve. I also feel at some point it will be like a lot of software engineering is today. Working for companies implementing solutions similar to what already exists but tailored to the context of that companies specific needs. As of now I feel that this field is so young that a lot of the solutions are almost completely custom built to the problem at hand and that a lot of work is needed to abstract away those solutions into higher level reusable pieces.
> The other driver is business-driven. And this is where management demands 'AI experts', when what they really want is data-miners. And in many cases management prides themselves on 'AI algorithms', but we know that this is a term for anything that gets the results that management wants and may be far from intelligent and in most corporate cases a bunch of SQL scripts.
I wonder if this is a case where managers end up believing their own bullshit. "AI driven" is basically the marketing-speak for "a bunch of SQL scripts".
The other driver is business-driven. And this is where management demands 'AI experts', when what they really want is data-miners. And in many cases management prides themselves on 'AI algorithms', but we know that this is a term for anything that gets the results that management wants and may be far from intelligent and in most corporate cases a bunch of SQL scripts.