I realize I don't understand anything about either subject and the oreilly book is like $80
I want stuff that is fairly technical if possible. Ebook recs would be good as well
I realize I don't understand anything about either subject and the oreilly book is like $80
I want stuff that is fairly technical if possible. Ebook recs would be good as well
tech/science swag.
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The issue about informational content about anything AI related is the gap between technical depth and accessibility.
Do you have a background in this field? Do you know why only attention matters? Can you tell the difference between a CNN/RNN? Does it even matter? - Or do you just wanna keep up with latest developments? If that's the case I'd just recommend reading HN whenever a new 'groundbreaking' breaktheough is achieved.
In case you already have some knowledge, Edan Meyer does good videos on selected topics in AI. He doesn't go into any actual technical detail but without a very solid math background one would be lost either way.
I quite like his video on Stable Diffusion which includes an explanation and some actual hands-on examples.
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I want more than a fascile understanding of AI and ML and even though I wont understand most of it I want to know what goes on in an LLM. Like how even though I'm a brainlet ai still kind of understand how a computer works down to the silicone level.
I got into some of it, how the model processes nodes, and stuff about weighting and it's all v interesting.
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Then I'd recommend the following.
I love this book: https://www.deeplearningbook.org
It uses the following architecture. Feel free to skip a chapter if you think it isn't worth the time:
Besides that, Microsoft offers good courses for ML Beginners and AI Beginners. Chiobu linked that below.
Good luck.
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What's the likelihood that this branch of AI hits another brick wall?
I guess the math is advanced enough you can totally apply to other fields even if this peters out?
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In all honesty after some cursory research I think this version of ML will change computing. I think within a decade optimized LLMs will be doing most of the high level computing inside everyday devices. The applications are endless, and in lots of cases programs that now exceed millions of lines of code could ostensibly be replaced with an LLM
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I think people who say it's
are r-slurred for sure, but I am curious what the limitations looks like w.r.t. "hallucinations". Not from an "ethics" BS perspective but from a "if X is supposed to do Y but doesn't do it reliably, that's really fricking annoying" from a user perspective..
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Near future: they design very controlled datasets and scrutinize the output. End user LLMs will be restricted wrt outputs and inputs.
Long term: they figure out how the neural network actually works and stop it from hallucinating
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Awesome, thanks
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