...Then Cortana will be about porn... * not kidding * ...

A brief intro using regular expressions, giving you the general idea:

https://www.nzini.com/lessons/NLP2+-+Template+Matching.html

Also, classically, look for the "Message Understanding Conferences" and "Information Extraction" & "Named Entity Recognition" as a task.

It gets really tricky if the information is "implied": John bought flowers for Lucy —> "Does John like Lucy?": evidently yes, but nobody SAYS that. Good luck! 😊

Classically, you would use some form of "template", in the simplest form a sort of "anchor word" whereabout in a certain radius other (pre-defined) words are sought. If a "match" is found — a goal is recognized. The difficulty, evidently, is how to get down false positives & false negatives and how to "estimate" good templates — the advantage is, however, full explainability: "WHY was that goal suggested" would be exactly traceable. The templates can get arbitrarily involved, e.g. with probabilities, conditional probabilities, dependencies of words and goals, etc.

"With machine learning" you could give it a set of "labelled texts", as in, "this text is about this, that text is about that", and you could have the system reduce the matching words (in the simplest form: simply as a set of words in no particular order) progressively until the ability to recognize a goal given a small "bag of words" has been optimized. You can e.g. use for that random forests, or whatever else you like. Disadvantage: EXPLAINING the goals will be way harder. — EDIT: for this approach, you do need an annotated data set, for the above one — not, there instead you need the templates'

Let's say, this is an issue of degree, rather than a black-and-white issue. For instance, if you have human DNA, rather than elephant or horse DNA, you can assume a human will come out.

If you can get an idea, will it be Asian, Caucasian, African,... — you can get therefrom a general idea, too.

But that is as far as it gets, sort of: humans look vastly different based on minor details. Think of actors in different roles. The closest you could get is a sort of "cohort" of possibilities for the looks.

Behavior, I trust, will be even less reliable.

An AI could "hallucinate" a "solution", sure, the way they "sharpen" images. But that would be A solution, not THE solution.

No. And even if you look at "identical" twins — who were developed not only from the same DNA, but also in the same nutritional, hormonal and biochemical environment — they are highly similar, but not "identical".