Comment by OtherShrezzing

Comment by OtherShrezzing 2 days ago

Humans don't grow exponentially indefinitely. But there's only something in the order of 100k AI researchers employed in the big labs right now. Meanwhile, there's around 20mn software engineers globally, and around 200k math graduates per year.

The number of humans who could feasibly work on this problem is pretty high, and the labs could grow an order of magnitude, and still only be tapping into the top 1-2% of engineers & mathematicians. They could grow two orders of magnitude before they've absorbed all of the above-average engineers & mathematicians in the world.

sigmoid10 2 days ago

I'd actually say the market is stretched pretty thin by now. I've been an AI researcher for a decade and what passes as AI researcher or engineer these days is borderline worthless. You can get a lot of people who can use scripts and middleware like frontend lego sets to build things, but I'd say there are less than 1k people in the world right now who can actually meaningfully improve algorithmic design. There are a lot more people out there who do systems design and cloud ops, so only when you choose to go for scaling, you'll find a plentiful set of human brainpower.

Reply View 4 replies

llm_trw 2 days ago

Do you know what places people who are interested in research congregate at? Every forum, meet up or journal gets overwhelmed by bullshit with a year of being good.

Reply View | 3 replies
- sigmoid10 a day ago
  
  Universities (at least certain ones) and startups (more in absolute terms than universities, but there's also a much bigger fraction of swindlers). Most blogs and forums are garbage. If you're not inside these ecosystems, try to find out who the smart/talented people are by reading influential papers. Then you can start following them on X, linkedin etc. and often you'll see what they're up to next. For example, there's a pretty clear research paper and hiring trail of certain people that eventually led to GPT-4, even though OpenAI never published anything on the architecture.
  
  Reply View | 2 replies
  
  llm_trw a day ago
  
  I am in correspondence with a number of worth while authors, it's just that there isn't any place where they congregate in the (semi) open and without the weirdos who do stuff with the models you're missing out on a lot.
  My favorite example I can never share in polite company is that the (still sota) best image segmentation algorithm I ever saw was done by a guy labeling parts of the vagina for his stable diffusion fine tune pipeline. I used what he'd done as the basis for a (also sota 2 years later) document segmentation model.
  Found him on a subreddit about stable diffusion that's now completely overrun by shitesters and he's been banned (of course).
  
  Reply View | 1 reply
  
  sigmoid10 21 hours ago
  
  It's pretty easy nowadays to come up with a narrow domain SOTA in image tasks. All you need to do is label some pictures and do a bit of hyperparameter search. This can literally be done by high schoolers on a laptop. And that's exactly what they do in those subreddits where everyone primarily cares about creating explicit content. The real frontier for algorithmic development is large domains (which need a lot more data by default as well). But there actually are some big-game explicit content platforms engaged in research in this area and they have shown somewhat interesting results.
  
  Reply View | 0 replies