It's like asking whether a carpenter is a scientist because they developed a cabinet...

Well, Wikipedia might be wrong, but this is how they describe Computer Science: "Computer science is the study of computation, information, and automation. Computer science spans theoretical disciplines (such as algorithms, theory of computation, and information theory) to applied disciplines (including the design and implementation of hardware and software). Though more often considered an academic discipline, computer science is closely related to computer programming."

You might argue, though, that Computer Science is not "real" science.

I honestly feel like that, as a "Software Engineer". As the digital world is new, and full of similar concepts to the old analogue world, it's natural that we borrow names along with the concepts. Similar to how a lot of things in the sea are named after land things, with a marine prefix, like the sea horse, sea star, sea cucumber, and so on. And now in IT we have engineers, architects, rockstars, tribes, and science, even though, very often, they have no relation to the original profession or concept, and especially doesn't have the responsibility or impact of that.

Computer science is the study, that is looking at how computers and the use of computers can benefit mankind (in my short lay version). Software engineering is a subset of computer science where you build a real world application of the studies.

The algorithms behind the Facebook feed and your favorite AI/ML product is the science. The code that powers the feed and chatgpt is the engineering.

Wikipedia is not wrong IMHO, but then I studied computer science 20 years ago so maybe I’m out of touch

I agree with you. There's a lot of engineering, and science going into IT. But I'm a Software Engineer too, and while I'm clever and do clever things, I can assure you that there's no rigor or reason to call it engineering. This version of engineering is at most the re-use of the word, like how the word art is not just covering artistic expressions, but skillfulness too, even though when you apply a skill artfully, you don't create art. Similarly, I do a lot that takes ideas of engineering, but really I'm just writing code and kinda design a smallish system maybe.

Engineering is at all levels, I’ve designed systems that span global infrastructure and systems that run in embedded systems. I still think that design process is a key part of engineering. Figuring out how to put things together is part of design. I agree the more jr engineer you are, the less “engineering” you see.. but you can’t run until you learn to walk.

I talk to my friends who sent projects into space and friends who design deep sea drilling rigs. The engineering process is very similar.

I think that you could genuinely make that argument though.

I hate to break it to you but that is part of science. Perhaps the major part of it too. Hypothesis: I can build a cabinet with these materials which will bear some load range. Experiment: I built it and it obviously works.

Now change cabinet to particle accelerator that a giant team of other theorists and engineers designed. Am I not doing science by participating in building it? So experimental scientists arent scientists?

To me, you're describing the differences between a cook and a chef. Just because you've built something with well known methods doesn't make you a scientist. You didn't come up with anything new. In fact, we're starting to sound a lot like Apple. Apple is (in)famous for taking ideas that someone else did all of the hard work of developing and proving to work, and then take various ideas like that to combine into an actual useful working something. (They also do a lot of pure deep research as well, so don't think I'm going too far with the analogy.)

This image is burned into my retina https://en.meming.world/images/en/a/aa/Something_of_a_Scient...

semantics, I say. cooks are "chiefs" of certain things otherwise they'd have no value on a team. unless you're saying that chefs setting the menu means theyre more of a scientist. at worst you're saying e.g. scientists are like chefs in that their used their creativity to make up the standard model. at best you're saying chefs are theorists and cooks are experimental scientists which is , again, to my own point.

i'm saying that chefs are more likely to be aware of what is happening when doing the whipping, baking, cooling, etc and why that's important vs just doing it as a step. so when they experiment with the menus, there's a bit more than basic understanding in why they think the experiments might work. cooks are just the kids in chemistry class following the directions, and depending on how well they follow the directions (and to a point how well the instructions are written), they might not blow up the lab (or burn the m.f. soufflé)

says who? cooks at a fine restaurant better know everything about their part. but they chose to try to work under a greater chef because their eventual upward mobility as a practitioner is higher. literally chef means leader or the boss. in the rest of the world that usually means less specifically qualified. not likely here that a chef isnt a good cook but a good cook certainly has the ability to set a menu if they wanted. you're not making a strong argument imo.

Yeah to my newb eyes, the core of machine learning can (and probably is) as scientific as any mathematics goes (granted they try novel ideas, and not applying old solutions). Now openai sits more on the scaling/engineering to make his business.

There is actually an old argument about whether Computer Science is math or science. The lack of hypotheses and physicality drives the "it's math" camp. I tend to fall into that camp. Most computer science is either math or applied math.

That said, most of machine learning is not just engineering. If anything, there is little engineering done at all.

The note is interesting and in a way a time taking puzzle,

but do not forget the apparent point in the original poster's is the lacking approach below the implementations of above said techniques.

The techniques you listed in a substantial way expanded our knowledge ("oh, we can do this ... and solve more problems") while raising little more questions.

The said implementations on topic give results but raise many more unanswered questions.

(This post is unfortunately an "immature" reply - just a provisional tentative point. The matter of "ML algorithms ∩ science" requires more time and concentration and consultation of the texts of the Great Ones.)

>> but do not forget the apparent point in the original poster's is the lacking approach below the implementations of above said techniques.

That's right. I think the approach that is lacking is best explained in the submission guidelines for the Journal of AI Research:

>> Papers describing systems should clearly describe the contributions or the principles underlying the system. Papers describing theoretical results should also discuss their practical utility. In general, it should be clear how the work advances the current state of understanding and why the advance matters. Papers should report on what was learned in doing the work, rather than merely on what was done.

https://www.jair.org/index.php/jair/about/submissions

Doing stuff is not science, even if it's doing stuff that's never been done before. In science, we experiment to understand why stuff can be done, and how, not just that it can be done. And of course it matters what is being done, so for example experimenting to find the best combination of spices for a dish is not an instance of science.