Comment on 'It's Literally the Gulag': Furious Meta Employees Speak Out on 'Soul-Crushing' AI Jobs
gravitas_deficiency@sh.itjust.works 4 days agoOncological genomic sequencing cross-correlated with clinical patient outcome data to improve cancer treatment targeting based on real world patient outcome data. Personally, I work on data pipeline stuff.
flying_sheep@lemmy.ml 4 days ago
I hope pipeline stuff is better than when I started. I (not so) accidentally played a big part in revolutionizing my field by building anndata and scanpy. Then I tried doing it again by writing sequencing stuff in Rust, but was too early / too bad, but now that’s happening too!
gravitas_deficiency@sh.itjust.works 4 days ago
I’m personally playing with the idea of putting together a POC to transition our “foundation” data warehouse from PSQL to a graphDB, because the extensibility and maintainability of our current system is fucking awful. Like, some upstream entity gets a version bump and there’s like 5 systems we have to go through and add columns to various tables and occasionally fuck around with joins and so on every single time there’s a new piece of data we want to integrate. And we have no capability to scan back historically and evaluate our holistic state at some particular time index, which can be really helpful for some applications.
Anyways, I’m fucking swamped at work so haven’t touched that at all, but I’ve wanted to explore that idea for well over a year and a half at this point.
flying_sheep@lemmy.ml 4 days ago
I have little experience with graphdb, but a lot of experience with the pain you’re describing. Maintaining schemas is a pain, maybe if you don’t need the performance, you can go that route!
gravitas_deficiency@sh.itjust.works 4 days ago
The thing that interests me about it is that it will be a lot more trivially interrogable by ML stuff (bespoke ML specifically, not LLM), which could glean an absolute shitload of interesting insights for us.
I am an enormous fucking Luddite for a whole swath of reasons when it comes to LLMs, but ML outside of that context can be immensity powerful when employed correctly.