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Cake day: June 7th, 2023

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  • v_krishna@lemmy.mltoOpen Source@lemmy.mlProton's biased article on Deepseek
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    2 days ago

    In deep learning generally open source doesn’t include actual training or inference code. Rather it means they publish the model weights and parameters (necessary to run it locally/on your own hardware) and publish academic papers explaining how the model was trained. I’m sure Stallman disagrees but from the standpoint of deep learning research DeepSeek definitely qualifies as an “open source model”






  • v_krishna@lemmy.mltoAsklemmy@lemmy.ml*Permanently Deleted*
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    11 months ago

    In the early 2000s plucking and waxing your brows to really really thin was fashionable, esp in certain places. Then people realized it is dumb but if you are around 40 now it was too late. So microblading helps you not look like a MadTV sketch. Source: my wife turns 40 this year, grew up in a particularly hood area of the sf bay area, and from 30 onwards really regretted plucking her eyebrows to almost nothing.


  • Dr bronners for skin and hair (I have very thick indian hair in jata style dreadlocks down to my knees). For a long time I used a charcoal based face wash (lush until they changed their formula for coal face, then some similar brand I found on amazon) but for whatever odd reason after a few years my skin stopped tolerating it and kept breaking out, so I switched to Kate Somerville’s sulfur face wash which works wonders (but does have a bit of a smell to it unfortunately).











  • Many (14?) years back I attended a conference (now I can’t remember what it was for, I think a complex systems department at some DC area university) and saw a lady give a talk about using agent based modeling to do computational sociology planning around federal (mostly navy/army) development in Hawaii. Essentially a sim city type of thing but purpose built to help aid in public planning decisions. Now imagine that but the agents aren’t just sets of weighted heuristics but instead weighted heuristic/prompt driven LLMs with higher level executive prompts to bring them together.



  • A lot of semantic NLP tried this and it kind of worked but meanwhile statistical correlation won out. It turns out while humans consider semantic understanding to be really important it actually isn’t required for an overwhelming majority of industry use cases. As a Kantian at heart (and an ML engineer by trade) it sucks to recognize this, but it seems like semantic conceptualization as an epiphenomenon emerging from statistical concurrence really might be the way that (at least artificial) intelligence works