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
Here's Will MacAskill at EAG 2020:
Command-f for the full context on this.
The climate change scenarios that EAs are most worried about are tail-risks of extreme warming, in comparison to GiveDirectly's effects which seem slightly positive in most worlds. And while the best climate change interventions might be robustly not-bad, that's not true for the entire space. Given the relatively modest damage in the median forecasts (e.g. 10% counterfactual GDP, greatly outweighed by economic growth) many proposals, like banning all air travel, or anti-natalism, would do far more harm than good. Will suggests that climate change policies are robustly good for the very long term growth rate (not just level), but I don't understand why - virtually all very long-term growth will not take place on this planet.