In recent months, the CEOs of leading AI companies have grown increasingly confident about rapid progress:
* OpenAI's Sam Altman: Shifted from saying in November "the rate of progress continues" to declaring in January "we are now confident we know how to build AGI"
* Anthropic's Dario Amodei: Stated in January "I'm more confident than I've ever been that we're close to powerful capabilities... in the next 2-3 years"
* Google DeepMind's Demis Hassabis: Changed from "as soon as 10 years" in autumn to "probably three to five years away" by January.
What explains the shift? Is it just hype? Or could we really have Artificial General Intelligence (AGI) by 2028?[1]
In this article, I look at what's driven recent progress, estimate how far those drivers can continue, and explain why they're likely to continue for at least four more years.
In particular, while in 2024 progress in LLM chatbots seemed to slow, a new approach started to work: teaching the models to reason using reinforcement learning.
In just a year, this let them surpass human PhDs at answering difficult scientific reasoning questions, and achieve expert-level performance on one-hour coding tasks.
We don't know how capable AGI will become, but extrapolating the recent rate of progress suggests that, by 2028, we could reach AI models with beyond-human reasoning abilities, expert-level knowledge in every domain, and that can autonomously complete multi-week projects, and progress would likely continue from there.
On this set of software engineering & computer use tasks, in 2020 AI was only able to do tasks that would typically take a human expert a couple of seconds. By 2024, that had risen to almost an hour. If the trend continues, by 2028 it'll reach several weeks.
No longer mere chatbots, these 'agent' models might soon satisfy many people's definitions of AGI — roughly, AI systems that match human performance at most knowledge work (see definition in footnote).[1]
This means that, while the co
I'm torn on this, because on the one hand I love the accessibility and the de-biasing that comes with this kind of blinding. On the other hand, I think the quality of the talks would go down, if due to nothing else then a sort of regression to the mean scenario. I may be able to write a good proposal for a talk, but that doesn't mean that I am an engaging and charismatic public speaker.
I think I'd be happier with blinding if it is for a journal submission or something in writing, but it is REALLY hard to judge how good a presentation/talk/workshop will be based off of a piece of writing.
If I am very experienced in running workshops, then I'd want to refer to that in my proposal, but mentioning the previous workshops I've done would de-blind the process.
But I do think that there are decent options that the CEA events team could explore for adding more un-conference aspects to EAGs and EAGxs, such as a certain number of spaces and time slots set aside as "open," and then a whiteboard set up for anyone to sign up for a time slot and a space to offer a workshop.
EDIT: I just read other comments on this post and I realized that I am basically just repeating what Nick Laing has already written. I guess I should have just upvoted that comment rather than writing out my own. Haha.