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Joined 3 years ago
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Cake day: July 3rd, 2023

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  • Unfortunately, the field is pretty brutal about just assuming you are fully caught up to everything in the field, or that you will read the references for a given statement. You often don’t need to know all the math, but at least have a general idea of what some concepts are. I’ve been part of ‘journal clubs’ for the past 5 years where the only really goal is to stay abreast of the changes going on so that a new paper doesn’t send me down a rabbit hole of reading a referenced paper, which also has unknown terms, and read those references, etcetcetera.

    I’m not in a great position to give useful advice due to that. it might be a good approach to find a youtuber who gives a 20-30 minute review/summary of a single paper that looks interesting, and of the video makes sense, then try to read the paper, and if you get stuck, see if the YouTuber has a video on the concept that got you stuck.


  • I work in AI/ML, and it’s such a hilariously broad and meaningless field I don’t even do remotely the same thing as my coworker in the same team hired on the same day. They work in agentic llm swarm research I work in CV/RL. Here’s three papers I had to read recently, and not fully understand but extract usable insight from (why did they do what they did and can I use that for myself)

    Upsample anything

    Dinov3

    LTGNN line transformer graph neural networks for robust line feature matching just kidding this one isn’t on arxiv

    It takes me a couple hours to deeply read a paper, I’m not like, getting 100% from it till like the 4th read through and a couple pages of notes, or reading a couple background papers (such as the JBU paper for UpAnything). Try reading one or two and if it seems interesting then maybe that’s an area in AI ML that might be interesting to you.

    Remember the people doing the most math are the people doing ai ML academically in a masters or PhD, us commercial users tend to do less math and more “this new model came out can we apply it to our problem”





  • It does seem like the open-weight models don’t have easily monetizable adjacent money streams like support and services, best they have is inference cost. But the whole point of the open weights is that anyone else can come and take that part from them. It’s a tough place to be in if the goal is only profitability. But since AI was declared a national security thing here in the US, the strategic value of ensuring we don’t end up dominating global AI is probably worth it. And a steal at the 500mil per year loss they claim.

    I do wonder what sustainable future looks like for an open weights lab, once we’re past this wackiness. A very well tuned harness? That’s kind of its own product. On-prem seployment support?