LLMs
sari and
LLMs
Thoughts on AI
linkedin.com
LLMs are mid. It does a good job at getting the average and this is useful in many areas where this is tough. When you want "good", LLMs let you down. We underestimate the impact doing things to a "mid" level will have on the world.
1/ AI exploded on the scene at the end of 2022, with applications like @OpenAI's ChatGPT taking the world by storm.
2023 is poised to be the most exciting year of AI yet! 🎉
Here's mine and @vivek7ue's look at the lay of the land + what might happen next...
👀🧵... See more
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.comMeta 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
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.comLeaked Google document: “We Have No Moat, And Neither Does OpenAI”
Simon Willisonsimonwillison.net