@fchollet a colleague of mine came up with this using GPT-4 and it gets the final answer right... and the reasoning is quite convincing... until you see the flaw: https://t.co/SCGjUDEK2Q...
A recent work from @iddo claimed GPT4 can score 100% on MIT's EECS curriculum with the right prompting.
My friends and I were excited to read the analysis behind such a feat, but after digging deeper, what we found left us surprised and disappointed.
https://t.co/mpDqlenk04
🧵
Why AI Will Save The World
By Marc Andreessen
The era of Artificial Intelligence is here, and boy are people freaking out.
Fortunately, I am here to bring the good news: AI will not destroy the world, and in fact may save it.
🧵
A new Ahead of AI issue is out, where I am covering the latest research highlights concerning LLM tuning and dataset efficiency: https://t.co/3kOdk14rzo
Everyone should learn to fine-tune LLMs.
You can try (and fail) to force GPT-4 on to every solution, or you can reduce your costs and improve your accuracy by fine-tuning a task-specific LLM.
Here's why, and some tips for getting started:
We're launching ten $100,000 grants for building prototypes of a democratic process for steering AI. Our goal is to fund experimentation with methods for gathering nuanced feedback from everyone on how AI should behave. Apply by June 24, 2023: https://t.co/kJG2bNnons
If you are Canadian and speak French and you have a PhD related to data science *or* networks/security, you can become my colleague. https://t.co/Kw91SjIMsz https://t.co/BS4mG7YpwQ
@rasbt I don't have a good understanding of RL so I was explaining it to myself as follow: annotation work for SL is slow & expensive = difficult to cover tasks and domains horizontally. RL is 'cheap' in comparison: thumbs up/down or answer ranking = allows covering general language.