LLMs
sari and
LLMs
I've learned so much from @hubermanlab, but podcasts make recall and discovery challenging.
Huberman AI uses the latest models from @openai to search every episode with Google-level accuracy and summarize the results.
https://t.co/1nctiAIuGO https://t.co/VpjnY2NcpM
riley tomasektwitter.com"A key challenge of (LLMs) is that they do not come with a manual! They come with a “Twitter influencer manual” instead, where lots of people online loudly boast about the things they can do with a very low accuracy rate, which is really frustrating..."
Simon Willison, attempting to explain LLM
Language models can take a big chunk of text and smush it down like a foot crushing a can of Coke. Except it doesn’t come out crushed—it comes out as a perfectly packaged and proportional mini-Coke. And it’s even drinkable! This is a Willy Wonka-esque magic trick, without the Oompa Loompas.
People need to be more thoughtful building products on top of LLMs. The fact that they generate text is not the point.
thesephist.comstream.thesephist.com
Meta AI released LLaMA ... and they included a paper which described exactly what it was trained on. It was 5TB of data.
2/3 of it was from Common Crawl. It had content from GitHub, Wikipedia, ArXiv, StackExchange and something called “Books”.
What’s Books? 4.5% of the training data was books. Part of this was Project Gutenberg, which is public
