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 agree with you everything you said regarding EAs focusing on the cause areas that are going to do the most good, and for those organizations to carry the burden of evidence / proof so that we are enabled to reduce the most suffering per dollar.
I'm not trying to convince others that this is a top priority cause area. It's definitely not and wouldn't encourage people to donate if their singular goal is to do the most good in the world.
I have more than one goal here, however. My goal is to find out how I can do the most good in this cause area. I don't want people to stop donating to x-risk, global health, or farm animal welfare. I'm still donating to GiveWell charities and those take up a majority of my donations still.
As GiveWell points out, that's difficult to accomplish, even with their own top charities. I agree that we should continue donating to places that have shown the evidence. Yet, we can't expect the same GiveWell-like evidence in other cause areas, whether it's an EA cause or not. That's why they started Open Philanthropy.
I'm not the type of person to be a perfect utilitarian robot, nor do I want to be. If I were, then I wouldn't have donated money to my best friend's father's funeral which they couldn't afford.
Peter Singer says in his famous TED talk that EA combines both the head and the heart.