What does an average AI prof know that a physics graduate who can code doesn’t know? I’m struggling to name even one thing. If you set the two of them to code AI for some competition like controlling a robot, I doubt that there would be much advantage to the AI guy.
So people with no experience programming robots but who know the equations governing them would just be able to, on the spot, come up with comparable code to AI profs? What do they teach in AI courses, if not the kind of thing that would make you better at this?
How to code, and rookie Bayesian stats/ML, plus some other applied stuff, like statistical Natural Language Processing (this being an application of the ML/stats stuff, but there are some domain tricks and tweaks you need).
So people with no experience programming robots but who know the equations governing them would just be able to, on the spot, come up with comparable code to AI profs?
The point is that there would only be experience, not theory, separating someone who knew Bayesian stats, coding and how to do science from an AI “specialist”. Yes, there are little shortcuts and details that a PhD in AI would know, but really there’s no massive intellectual gulf there.
So people with no experience programming robots but who know the equations governing them would just be able to, on the spot, come up with comparable code to AI profs? What do they teach in AI courses, if not the kind of thing that would make you better at this?
How to code, and rookie Bayesian stats/ML, plus some other applied stuff, like statistical Natural Language Processing (this being an application of the ML/stats stuff, but there are some domain tricks and tweaks you need).
The point is that there would only be experience, not theory, separating someone who knew Bayesian stats, coding and how to do science from an AI “specialist”. Yes, there are little shortcuts and details that a PhD in AI would know, but really there’s no massive intellectual gulf there.