LLMs
sari and
LLMs
Using LLM products today feels a lot like using early cars in the 1800s: clearly magical, clearly going to change the world, and really hard to drive.
The first cars didn’t have steering wheels (they hadn’t been invented yet), so you’d steer them with a big lever called a tiller. The problem with tillers is that they are imprecise, which made... See more
The first cars didn’t have steering wheels (they hadn’t been invented yet), so you’d steer them with a big lever called a tiller. The problem with tillers is that they are imprecise, which made... See more
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.comPeople need to be more thoughtful building products on top of LLMs. The fact that they generate text is not the point.
thesephist.comstream.thesephist.comLanguage 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.
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