Sorry, that's patently untrue. Perhaps it's anecdotal, but I know a host of undergrads who got head hunted into quite elite tech positions either directly from Uni where I studied, or due to private projects they were in. And I even know a few that doesn't even have any uni edu that got hired to very high technical positions. Usually they were nerdy types who had worked with or had exposure to large systems for whatever reason, or who showed some promise due to previous work, demos or programs they'd made. But sure, most people have to go the edu route. It's the safest way into tech, as you are - at least in principle - fully vetted before you apply. Thinking that you can get a data science or hacker job just by installing Kali is ofc also very untrue.
I think my post is more representative of the truth than yours. I am sure you are telling the truth, but these unique talents you are talking about are not representative of the bulk of people working in research.
The demand for AI/ML will fast outstrip available talent. We'll be pulling students right out of undergrad if they can pass an interview.
I'm hiring folks off Reddit and 4chan that show an ability to futz with PyTorch and read papers.
Also, from your sibling comment:
> Maybe it is also a matter of location. I am in Germany.
Huge factor. US cares about getting work done and little else. Titles are honestly more trouble than they're worth and you sometimes see negative selection for them in software engineering. I suspect this will bleed over into AI/ML in ten years.
Work and getting it done is what matters. If someone has an aptitude for doing a task, it doesn't matter where it came from. If they can get along with your team, do the work, learn on the job and grow, bring them on.
I recommend taking all the introductory courses you can find on both AI and ML. If you like the introductory courses, and you feel compelled to move on, then chances are you'll do well in a job regarding AI or ML. There are also several ways into it, either through pure mathematics, statistical modelling, data science, particularly through learning about various algorithms and reading papers, or even through practical application within data warehousing or day-to-day programming. I'd say it helps to have an academic background in either IT, statistics or mathematics, though, but depending on what you're aiming for it doesn't need be a firm prerequisite. Btw. linguists or anyone interested in natural language ought also apply!
