Shallow review of technical AI safety, 2024

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 Shallow review of technical AI safety, 2024 

 by technicalities , Stag , Stephen McAleese , jordine , Dr. David Mathers 29th Dec 2024 AI Alignment Forum 49 min read 35 202

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 from  aisafety.world 

  

 The following is a list of live agendas in technical AI safety, updating  our post from last year. It is “shallow” in the sense that 1) we are not specialists in almost any of it and that 2) we only spent about an hour on each entry. We also only use public information, so we are bound to be off by some additional factor. 

 The point is to help anyone look up some of what is happening, or that thing you vaguely remember reading about; to help new researchers orient and know (some of) their options and the standing critiques; to help policy people know who to talk to for the actual information; and ideally to help funders see quickly what has already been funded and how much (but this proves to be hard).

 “AI safety” means many things. We’re targeting work that intends to prevent very competent cognitive systems from having large unintended effects on the world. 

 This time we also made an effort to identify work that doesn’t show up on LW/AF by trawling conferences and arXiv. Our list is not exhaustive, particularly for academic work which is unindexed and hard to discover.  If we missed you or got something wrong, please comment, we will edit. 

 The  method section is important but we put it down the bottom anyway.

 Here’s a spreadsheet version also. 

  

 Editorial

 One commenter said we shouldn’t do this review, because the sheer length of it fools people into thinking that there’s been lots of progress. Obviously we disagree that it’s not worth doing, but be warned: the following is an exercise in  quantity ; activity is not the same as progress; you have to consider whether it actually helps.
 
 A smell of ozone . In the last month there has been a  flurry of hopeful or despairing  pieces claiming that the next base models are not a big advance, or that we hit a  data wall . These often ground out in gossip, but it’s true that the next-gen base models are held up by something, maybe just inference cost. The pretraining runs are bottlenecked on electrical power too. Amazon is at present not getting its  nuclear datacentre .
 
 
 But overall it’s been a big year for capabilities despite no pretraining scaling. I had forgotten long contexts were so recent; million-token windows only arrived in February. Multimodality was February. “Reasoning” was September. “Agency” was  October .
 I don’t trust benchmarks much, but on  GPQA (hard science) they leapt all the way from chance to PhD-level just with post-training.
 FrontierMath 

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