jj4211

It's not a subjective thing. Cold medicines treat symptoms, not the disease. Cold and allergies have common symptoms.

If your concern is that cold medicines don't work for your allergies, thwn those tend not to work for colds either.

If the medicine is trying to use phenylephrine in a pill, that doesn't do anything. You might also want to skip the acetaminophen usually included and you have zero need for that, but not every co of d medicine has that.

A month from now:

I'm working on this horrible Venezuela situation that BIDEN left me with. Such a waste of American resources but I'm going to pull that back to make America great again.

Ok, so you weren't countering that ML wasn't AI, just contending that ML wasn't "the" AI in the AI field, that's fine. Keep in mind this thread kicked off from an assertion that ML wasn't AI. Fine, ML wasn't all of AI, but definitely was AI and in the popular understanding, it was pretty much "the" AI in the same way LLM is "the" AI now.

As I said. It's an extrapolation of the rules from once upon a time to a totally different approach. It's marketing and increasingly subjective. Any number can "make sense" in that context. The number isn't based on anything you could actually measure for a long time now, it's already a fiction, so it can go wherever.

To be fair, the industry spent decades measuring a distance, so when they started doing features that had equivalent effects, the easiest way for people to understand was to say something akin to equivalent size.

Of course, then we have things like Intel releasing their "10 nm* process, then after TSMC's 7nm process was doing well and Intel fab hit some bumps, they declared their 10 to be more like a 7 after all.. it's firmly all marketing number..

Problem being no one is suggesting a more objective measure.

For a while now the "nm" has been a bit of a marketing description aiming for what the size would be if you extrapolated the way things used to be to today. The industry spent so long measuring that when the measurement broke down they just kind of had to fudge it to keep the basis of comparison going, for lack of a better idea . If we had some fully volumetric approach building these things equally up in three dimensions, we'd probably have less than "100 pm" process easily, despite it being absurd.

I would say more the opposite, in pop culture, LLM == AI. In the technical world, both in university and industry, AI has covered a lot of areas, and machine vision based on ML was absolutely under the category of AI. If you said you used pytorch to train an AI model no one in the industry or academia would have batted an eye.

Yes. And I took an "AI" class as part of computer science curriculum back around 2000, including implementing "AI" stuff in lisp. We've been talking about AI for decades and ML for machine vision was always under that umbrella from the time out started becoming viable.

LLM is the recent popular subset of AI, not all of AI.

Yeah, this is right up machine vision alley, and is a pretty straightforward and good thing in general. It lets computers look through haystacks that humans just would take forever to get through. A thoroughly attentative human may do as well, if not better, given enough time, but it takes a lot of time and a human will burn out before reviewing that much imaging.

I mean, prior to the whole LLM craze ML was pretty much 'the' AI thing.

Note that AI got so messy they started saying the thing people imagined is 'AGI', so I don't know if you are thinking that, but in that picky scenario there is no such thing as 'AI' on the market with that definition.

ML is also AI. Obviously would be one of those instead of the LLM stuff, and generally the LLM stuff is the more controversial.

Note this isn't an LLM.