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This is nothing new actually, the same thing happend during the crypto boom.
There's slop users (autoclankers) and then there's researchers or developers actually doing the same stuff they've been doing for 5+ years.
I think it just seems that way because there's always a clash on practically every post.
Some people don't see the inherent flaw in outsourcing their physical thoughts to a cloud model, or the massive economic bubble they are helping to create.
But some people are doing some genuinely interesting things that would have otherwise been impossible several years ago just because AI and model training research got a huge boost for everyone the past few years.
My personal favorite is a drone that rapidly identifies and counts produce plant quality, output, issues, etc for large farms with some brand spanking new image models, and it costs about as much as maybe a new toolbox. No one wants to manually weed through hundreds of acres to count buds and try to catch problems before its too late. It's a great upgrade from doing random samples that misses a lot of data.
On the other hand, those opposed to AI also have a subgroup that wants anything and everything with AI in the name dead, without any regard to what it is or what it does.
It's like when you throw world and ml users into one post. They both think the other is louder, and also the big dumb lol.
This might be a bit of a hot take, but I don't really see anything inherently wrong with this. The scientists and engineers will continue doing their regardless of public opinion, and while some of them may have tangentially benefited from from increased interest and funding in the field, most of it is going to these corporate LLM models which are taking up all the oxygen in the room.
That's a bubble that needs to burst. I think it's more important to keep public sentiment rightfully focused in that direction. Let's face it, you're really not going to be able to educate the general public on these nuances. The field at large will persist regardless.
If you don't differentiate and keep the two in the same pot you won't be able to fund research into the useful stuff. It's true that consumer hype and research funding decisions are not the same, but they may be indirectly linked. A public fund may fear public outrage if it continues funding X millions of AI projects even if they're not LLM related.
So the reputation damage may affects viable, net positive applications.