The problem with being a safety focused AI lab, is you're also a danger focused AI lab. I don't think being danger focused leads you to build inspiring things.
We really need some open source project to manage all the shims across the world. Like in Seattle the WSDOT API has live ferry locations but they never made a GTFS feed for, so i made my own shim for it. I feel like there needs to be some way to make the data access easier for developers.
Surprised Amazon managed to fumble mechanical turk at the same time Mercor/Scale and all these other companies started hiring humans to do data labeling tasks.
I spent multiple hours every week in various risk management meetings one semester for our drone building student team. There was no risk management done, they just made it clear they would try and expell and sue us if we tested our drone.
A single 3090 will train qwen 0.8B just fine. While it’s not a very capable model any training technique you would want to master can be used to make real progress. And all the skills you need to learn how to do this can be learned watching Andrej Karpathy’s zero to hero series (shame he quit educational content and went to anthropic)
Depends on your pcie connection. If they're both x16 then it's pretty low overhead, x8 is ok, but x4 is too slow. Also it's a bit tricky getting an optimal setups with mismatched vram, I think you could probably still make use of the full vram if you're clever but it's trickier.
for layer parallelism (e.g. to get more vram) the bandwidth between layers is essentially nothing (like 16kb per token I think), so I don't think x4 would even be a problem!
