Artificial-Intelligence Experts Are in High Demand

Considering being an employee such as in the OP, I have two reactions:

(1) Take statistics, machine learning, neural nets, artificial intelligence (AI), big data, Python, R, SPSS, SAS, SQL Server, Hadoop, etc., set them aside, and ask the organization looking to hire: "What is the real world problem or collection of problems you want solved or progress on?"

Or, look at the desired ends, not just the means.

(2) Does the hiring organization really know what they want done that is at all doable with current technical tools or only modest extensions of them?

Or, since artificial intelligence is such a broad field, really, so far of mostly unanswered research questions, and the list of topics I mentioned is still more broad, I question if many organizations know in useful terms just what those topics would do for their organization.

So, for anyone with a lot of technical knowledge in, say, the AI, etc., topics, it is important for them to be able to evaluate the career opportunity. I.e., is there a real career opportunity there, say, one good to put on a resume and worth moving across country, buying a house, supporting a family, getting kids through college, meeting unusual expenses, e.g., special schooling for an ADHD child, providing for retirement, making technical and financial progress in the career, etc.?

So, some concerns:

(A) If an organization is to pay the big bucks for very long, e.g., for longer than some fashion fad, then they will likely need some valuable results on their real problems for their real bottom line. So, to evaluate the opportunity, should hear about the real problems and not just a list of technical topics.

(B) For the opportunity for the big bucks to be realistic, really should know where the money is coming from and why. That is, to evaluate the opportunity, need to know more about the money aspects than a $10/hour fast food guy.

(C) As just an employee, can get replaced, laid off, fired, etc. So, to evaluate the opportunity, need to evaluate how stable the job will be, and for that need to know about the real business and not just a list of technical topics.

(D) For success in projects, problem selection and description and tool selection are part of what is crucial. Is the hiring organization really able to do such work for AI, etc. topics?

Or, mostly organizations are still stuck in the model of a factory 100+ years ago where the supervisor knew more and the subordinate was there to add muscle to the work of the supervisor. But in the case of AI, etc., what supervisors really know more or much of anything; what hiring managers know enough to do good problem and tool selection?

Or, if the supervisors don't know much about the technical topics, then usually the subordinate is in a very bad career position. This is an old problem: One of the more effective solutions is some high, well respected professionalism. E.g., generally a working lawyer is supposed to report only to a lawyer, not a generalist manager. Or there might be professional licensing, peer review, legal liability, etc. Or, being just an AI technical expert working for a generalist business manager promises in a year or so to smell like week old dead fish.

(E) If some of the AI, etc., topics do have a lot of business value, then maybe someone with such expertise really should be a founder of a company, harvest most of the value, and not be an employee. So, what are the real problems to be solved. That is, is there a startup opportunity there?

Really, my take is that the OP is, net, talking about a short term fad in some topics long surrounded with a lot of hype. Not good, not a good direction for a career.

AI and hype? Just why might someone see a connection there?

Just because you can't see unicorns doesn't mean they aren't there, but at some point you have to give up the search. It's fine to talk about how, broadly speaking, rats are more intelligent than ants, plants or microbes (which are basically I/O rules with a body), chimpanzees more so than rats, humans more so than than chimps etc. But in general there's a ton of overlap and the qualities we associate with intelligence – memory, planning, self-awareness, tool use, whatever – are only loosely correlated continua.

There are a few more binary measures in intelligence research, such as the mirror test, but at best they're only a small piece of the puzzle. There's no sudden point where everything clicks into place.

Of course, if you have such a good definition of intelligence, feel free to enlighten me.

Population I of course meant to say !

Just because you're using statistics it doesn't mean that you are a statistician. For example, most of particle physics is based on statistics, but this is not enough to motivate them to rename the field. Whole fields of engineering use statistics every day, machine learning is just another.

You are repeating the same mistake as before. The difference is significant between admitting we don't know what those lines are and claiming that they don't exist.