I think that's one of the things that irks me most about this LLM bubble. I think ML has really cool applications in research, approximate tasks with margins for error like image classification, upscaling etc. It was one of my favorite topics during my CS degree. But now it's tainted by the gen "AI" slop machines, so all the interesting breakthroughs of real practical ML get chalked up to "AI" innovation and people just assume chatGPT is responsible when it's actually a team of researchers building small efficient programs. Ofc ML is less efficient than traditional computational approaches that can work deterministically so those should always be prioritized, but it has its uses for sure.

[–] GrouchyGrouse@hexbear.net 12 points 2 days ago (1 children)

This right here. The chat bot shit is shit. The tech analyzing X Rays to detect lung cancers is great. The problem is they can’t just sell “machine augmented professionals” they need to sell “A.I.” which is a marketing department fever dream

[–] robot_dog_with_gun@hexbear.net 6 points 2 days ago (2 children)

The tech analyzing X Rays to detect lung cancers is great

well, it's great if it's actually doing something novel and not just noticing that there's a strong correlation with older medical equipment, certain kinds of poverty, and smoking, so it's just assuming a shitty xray is cancer.

[–] bobs_guns@lemmygrad.ml 6 points 2 days ago

Also your radiologists who use it can become deskilled because they rely too much on the AI, which can cause problems over time

[–] KuroXppi@hexbear.net 5 points 2 days ago

My favourite go-to example is the DARPA gun that learnt overcast day = hidden tank

https://gwern.net/tank

(Though I'm arguing against myself because the above link says it's an unsourced, possibly overblown urban legend, idrc)

[–] lilypad@hexbear.net 5 points 2 days ago

There's a few problems (like image recognition) where deterministic approaches are either nonfunctional or far more resource and time intensive. But like, thats not what we get. Capital declares we shall feed it so we feed it. We will hit an ai winter again. Third time and its gonna hurt more than the others. We had one in the 70s, one in the 90s (though that was also affected by the dismantling of the soviet onion and drying up of DOD funding), and now its wormed its way into everything and is gonna be painful when it hits.

[–] userse31@hexbear.net 5 points 2 days ago* (last edited 2 days ago)

The various projects I say pre-2020 involving machine learning were genuinely interesting.

You were seeing a computer think in a way analogous to biological brains.

And then the LLMs came.