Sorry if this isn’t the right place for this question but I couldn’t think of anywhere better to put it.
So I finished my degree in computer science a couple years ago right when the tech crash just started hitting, and the job market has been an enormous clusterfuck. Instead of trying to get a job where everyone seems to be going all-in on LLMs, machine learning, and crypto bullshit, I’d really like to be able to put my programming skills to good use helping out scientific research in some way, but I have no clue where to start. While in college I did help out my university’s biology research department by writing small programs here and there to help undergrad/grad students who weren’t very knowledgeable about technical solutions, but because of the recent funding cuts to scientific research and education, everyone there is struggling harder than I am.
Ideally I’d love to help contribute to causes that help improve people’s lives (or astronomy just because space is cool). Does anyone know of resources I could look into to start down this path?
One interesting science field is “discrete AI” (probably has a few other names) which basically technically means “based on integers instead of floating point numbers”. It has a few more implications on the models being more mathematically clean, but that’s a long paragraph if I get into it.
The expecations are AI that is not based on absurd computing resources and black boxes, but getting the same benefits from low-power low-cost hardware and with outputs that can be more realistically queried to explain why the output became what it was.
E.g. if AI is used to make decisions on when to feed fish, and it feeds slightly too much, you’d want to be able to ask “why” and get a useful answer instead of today’s “yeah idunno magic computer said so i guess training data lol”
Kinda sounds like you’re talking about Explainable AI too. Very interesting set of fields, but I’m pretty sure they’re all having funding problems too.
Yeah, funding is kinda not. I assumed the question was ignoring that, but I may have been mistaken.
Tsetlin machines are the ones I found most interesting. Strict yes/no logic stuff in the actual decision model, while the deeper complexity is in the training.
Sounds interesting. Glad those topics are still being investigated. So important to remember that even those neural methods labored for decades in the shadows before they finally found the answers they needed.