I just joined last week, and between my being cautious about acceleration, busy with onboarding, and most of the content being written already, I didn’t contribute much to this post. But I’m happy to see this level of transparency from OpenAI. I hope it becomes standard in the future at OpenAI and at other companies (and AI safety shops, so we can see whether capabilities or safety is being accelerated more), and that our civilization will ultimately turn transparency into competent governance of the increasingly scary AI systems being developed.
I’m curious to hear which graphs people think are net positive vs net negative for OpenAI to have made and published, and what additional metrics would be useful. Hopefully this is just the start!
Today we’re releasing data on models accelerating research at OpenAI. Recursive self-improvement could be the most important contributor to AI capabilities over the next few years, but by default it will only be seen inside a few frontier AI labs. Being transparent is more urgent than ever, so we can inform the public discussion on whether and how to pace model development. I ask other AI companies to do the same.
Chris Ong (in this thread he goes through every plot in detail!)
Today we are releasing new data on how models have accelerated AI research at OpenAI since the start of this year. Recursive self-improvement will be one of the most consequential AI developments of the next few years, and a shared public understanding for how models enter into the AI research process will be a critical part of democratic governance of AI and pacing the model frontier.
At the current trajectory of capabilities growth, RSI could soon be one of the most consequential developments of our time. The prospect of rapid, unprecedented advances in machine intelligence call for us to have the most informed, transparent discussion we can have in service of the best interests of society. Our new post shares an early snapshot of how models are contributing to research inside OpenAI, along with the methods behind our measurements—data that we hope will establish a norm of public disclosure & ground engaged discussions about what the pace & direction of research acceleration should look like.
I don’t really understand why, in all the graphs, the numbers are not the same each time. I mean, compare this graph to this graph. Why are the numbers not the same (even rounded)? Why are the time horizon bins different?
That first sentence is such an obvious applause light I closed the article, reopened it, closed it again afyer the first paragraph, and only then went back and read the rest.
It contained some info I’m glad to have, but I’m really not sure who it’s for? It seemed written at a level where I know of audiences who’d find the gist obvious and the details unsurprising, and audiences umable to follow, but few in the goldilocks zone between those?
I just joined last week, and between my being cautious about acceleration, busy with onboarding, and most of the content being written already, I didn’t contribute much to this post. But I’m happy to see this level of transparency from OpenAI. I hope it becomes standard in the future at OpenAI and at other companies (and AI safety shops, so we can see whether capabilities or safety is being accelerated more), and that our civilization will ultimately turn transparency into competent governance of the increasingly scary AI systems being developed.
I’m curious to hear which graphs people think are net positive vs net negative for OpenAI to have made and published, and what additional metrics would be useful. Hopefully this is just the start!
Tweet threads by others on the team:
Kevin Liu:
Chris Ong (in this thread he goes through every plot in detail!)
Michael Zhao:
This looks like a broken draft post, because I see zero links or any words, and I wonder what happened to the post.
Is there any information on what happened?
Fixed
I don’t really understand why, in all the graphs, the numbers are not the same each time. I mean, compare this graph to this graph. Why are the numbers not the same (even rounded)? Why are the time horizon bins different?
The second graph averages over the period from Jan to July. So the numbers are not the same as for any individual month.
That first sentence is such an obvious applause light I closed the article, reopened it, closed it again afyer the first paragraph, and only then went back and read the rest.
It contained some info I’m glad to have, but I’m really not sure who it’s for? It seemed written at a level where I know of audiences who’d find the gist obvious and the details unsurprising, and audiences umable to follow, but few in the goldilocks zone between those?
Did you click the ‘view methods’ buttons next to the various graphs? That’s where the most interesting information lived in my read through.
No, I hadn’t, but I will now, thanks