-
1 week ago | 
80000hours.org | Robert Wiblin |Lukas Finnveden
Once we get to a world where it is technologically possible to replace those researchers with AI systems — which could just be fully obedient, instruction-following AI systems — then you could feasibly have a situation where there’s just one person at the top of the organisation that gives a command: “This is how I want the next AI system to be developed.” And then this army of loyal, obedient AIs will then do all of the technical work.
-
Aug 23, 2024 | 
lesswrong.com | Lukas Finnveden
This post gives my personal take on “AI for epistemics” and how important it might be to work on. Some background context:AI capabilities are advancing rapidly and I think it’s important to think ahead and prepare for the possible development of AI that could automate almost all economically relevant tasks that humans can do. That kind of AI would have a huge impact on key epistemic processes in our society.
-
Jan 24, 2024 | 
lesswrong.com | Feeping Creature |Seth Herd |Lukas Finnveden |Rohin Shah
I agree it helps to run experiments at small scales first, but I'd be pretty surprised if that helped to the point of enabling a 30x speedup -- that means that the AI labor allows you get 30x improvement in compute needed beyond what would be done by default by humans (though the 30x can include e.g. improving utilization, it's not limited just to making individual experiments take less time).
-
Jan 4, 2024 | 
lesswrong.com | Lukas Finnveden |Roger Dearnaley
This is a series of posts with lists of projects that it could be valuable for someone to work on. The unifying theme is that they are projects that:Would be especially valuable if transformative AI is coming in the next 10 years or so. Are not primarily about controlling AI or aligning AI to human intentions. Most of the projects would be valuable even if we were guaranteed to get aligned AI. Some of the projects would be especially valuable if we were inevitably going to get misaligned AI.
-
Oct 25, 2023 | 
lesswrong.com | Steven Byrnes |Thomas Kwa |Lukas Finnveden |Rob Bensinger
AI used to be a science. In the old days (back when AI didn't work very well), people were attempting to develop a working theory of cognition. Those scientists didn’t succeed, and those days are behind us. For most people working in AI today and dividing up their work hours between tasks, gone is the ambition to understand minds.
-
Oct 24, 2023 | 
lesswrong.com | Matt Goldenberg |Daniel Kokotajlo |Ben Pace |Lukas Finnveden
Previously: Sadly, FTXI doubted whether it would be a good use of time to read Michael Lewis’s new book Going Infinite about Sam Bankman-Fried (hereafter SBF or Sam). What would I learn that I did not already know? Was Michael Lewis so far in the tank of SBF that the book was filled with nonsense and not to be trusted? I set up a prediction market, which somehow attracted over a hundred traders. Opinions were mixed.
-
Aug 23, 2023 | 
lesswrong.com | Lukas Finnveden
I've written several posts about the plausible implications of "evidential cooperation in large worlds" (ECL), on my newly-revived blog. This is a cross-post of the first. If you want to see the rest of the posts, you can either go to the blog or click through the links in this one. All of the content on my blog, including this post, only represent my own views — not those of my employer. (Currently OpenPhilanthropy.)“ECL” is short for “evidential cooperation in large worlds”.
-
Jul 26, 2023 | 
lesswrong.com | Lukas Finnveden |Sammy Martin |Thomas Kwa |Seth Herd
Crossposted from my blog. One of my professors in college taught a class about effective altruism—a social movement about doing good effectively. Whenever he was talking about the scale of animal suffering, all the statistics he talked about were about the suffering of non-fish.
-
May 26, 2023 | 
lesswrong.com | Lukas Finnveden
Summary:In the current paradigm, training is much more expensive than inference. So whenever we finish end-to-end training a language model, we can run a lot of them in parallel. If a language model was trained with Chinchilla scaling laws on the FLOP-equivalent of a large fraction of the world’s current GPU and TPUs: I estimate that the training budget could produce at least ~20 million tokens per second. Larger models trained on more data would support more tokens per second.
-
May 26, 2023 | 
lesswrong.com | Lukas Finnveden |Alexander Gietelink Oldenziel
As AI systems get more capable, they may at some point be able to help us with alignment research. This increases the chance that things turn out ok. Right now, we don’t have any particularly scalable or competitive alignment solutions. But the methods we do have might let us use AI to vastly increase the amount of labor spent on the problem before AI has the capability and motivation to take over.
