>This really does sound like Computer Science since it's very beginnings.
Except in actual computer science you can prove that your strategies, discovered by trial and error, are actually good. Even though Dijkstra invented his eponymous algorithm by writing on a napkin, it's phrased in the language of mathematics and one can analyze quantitatively its effectiveness and trade-offs, and one can prove if it's optimal (as was done recently).
> >This really does sound like Computer Science since it's very beginnings.
> Except in actual computer science you can prove that your strategies, discovered by trial and error, are actually good.
Maybe it's true for computer science - but most people on here aren't doing computer science. They're doing software engineering. And it sure as heck isn't true for software engineering. If it were, I wouldn't be hearing arguments about programming languages for years, or static vs dynamic typing, or functional vs OOP...
So what you're arguing about AI isn't exactly anything new to software development.
Yes, in theory. But it's testing against highly complex, ever-changing systems, where small changes can have big impact on the outcome. So it's more akin to "weak" science like psychology. And weak here means that most finding have a weak standing, because of each variable having little individual contribution in the complex setup researched, making it harder to reproduce results.
Even more problematic is that too many "researchers" are just laymen, lacking a proper scientific background, and they are often just playing around with third-party-services, while delivering too much noise to the community.
So in general, AI has also something like the replication crisis in its own way. But on the other side, the latest wave of AI is just some years old (3 years now?), which is not much in real scientific progress-rates.
the move from "software engineering" to "AI engineering" is basically a switch from a hard science to a soft science.
rather than being chemists and physicists making very precise theory-driven predictions that are verified by experiment, we're sociologists and psychologists randomly changing variables and then doing a t-test afterward and asking "did that change anything?"
the difference is between having a "model" and a "theory". A theory tries to explain the "why" based on some givens, and a model tell you the "how". For engineering, we want why and not how. ie, for bugs, we want to root-cause, and fix - not fix by trial-and-error.
the hard sciences have theories. and soft sciences have models.
computer science is built on theory (turing machine/lambda calc/logic).
AI models are well "models" - we dont know why it works but it seems to - thats how models are.
Except in actual computer science you can prove that your strategies, discovered by trial and error, are actually good. Even though Dijkstra invented his eponymous algorithm by writing on a napkin, it's phrased in the language of mathematics and one can analyze quantitatively its effectiveness and trade-offs, and one can prove if it's optimal (as was done recently).