A couple less common but arguably tougher questions to consider in prep for your next interview. These are basically “trick” questions so in order to give the “right” answer it helps to know the technically correct answer. No matter what do NOT use this answer.

Can machines learn?

Correct answer: No, By their very definitions the two words that make up the term machine learning when combined in that order result in a logical contradiction, and a thing which is logically impossible, a learning machine. To put it more simply, if a machine could learn it would no longer be a machine.

Suggested interview answer: Duh. Of course, why else would I be interviewing for this prestigious position as a machine learning engineer? If machines couldn’t learn it would mean I wasted my entire education and career on a lie. I don’t know about you, but I would be pissed.

How many artificial intelligences and/or artificially intelligent systems exist in the world today?

Correct answer: Zero. AI does not currently exist anywhere on, above, or below the planet, and it may never.

Suggested interview answer: It’s impossible to say. There are so darn many. You’ve got city traffic light systems around the globe, smart cars, smart phones, basically anything with the word ‘smart’ in front of it. Literally hundreds of thousands. I mean you can’t walk down the street these days without some damn AI bugging you to wax philosophic about the philosophy of Wittgenstein or some dumb shit like that.

Is data science a science?

Correct answer: No. It is not science or ‘a’ science, or a ‘type’ of science, if such a thing as a ‘type’ or ‘kind’ of science is even possible. It is not a field of inquiry or topic area of research in science. All science is data science in the sense that all science uses parts of some of the tools that fall under the rubric of “data science” to analyze data that is generated by the experiments that are conducted in that particular field or, in the case of purely theoretical fields, to analyze data sets that are relevant to the theory. So called data science is a tool of science, nothing more. A useful and powerful tool to be sure but science could and did get along just fine before the advent of the age of “data science.” Statistics and mathematics have always been useful tools for the scientist and they always will be, calling them data science does not make them science. More recently, powerful computers, and clever algorithms have greatly expanded the insights to be gained from even the most mundane of data sets. That said, using computers running algorithms to analyze data is not science, computers are still only tools of science and thus not science. A good, though imperfect analogy is a calculator in mathematics. It is a very useful tool for doing math, yet there is no field of “calculator mathematics.” Yes the analogy fails in many respects but the point of the example is clear, a tool of a thing is not the thing itself and never can be.

Suggested interview Answer: Who can say what is and isn’t “science” these days, and who really cares? I just want this job and you can call it whatever the heck you want as far as I’m concerned. How about this, deep data science, that has a nice ring to it, doesn’t it?

Remember do not use the correct answer…ever.

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