LessWrong 2.0 Reader

I would be genuinely surprised if training a transformer on the pre2014 human Go data over and over would lead it to spontaneously develop alphaZero capacity. I would expect it to do what it is trained to: emulate / predict as best as possible the distribution of human play. To some degree I would anticipate the transformer might develop some emergent ability that might make it slightly better than Go-Magnus - as we've seen in other cases - but I'd be surprised if this would be unbounded. This is simply not what the training signal is.

Man, I wish that was my experience. I feel like I’m constantly asking GPT4o a question, getting a weird or bad response. Then switching to 4 to finish the job.

The best method of improving sample efficiency might be more like AlphaZero. The simplest method that's more likely to be discovered might be more like training on the same data over and over with diminishing returns. Since we are talking low-hanging fruit, I think it's reasonable that first forays into significantly improved sample efficiency with respect to real data are not yet much better than simply using more unique real data.

a reduction in training data will not necessarily reduce the amount of computation needed. But once again, that’s the way to bet

I'm ambivalent on this. If the analogy between improvement of sample efficiency and generation of synthetic data holds, synthetic data seems reasonably likely to be less valuable than real data (per token). In that case we'd be using all the real data we have anyway, which with repetition is sufficient for up to about $100 billion training runs (we are at $100 million right now). Without autonomous agency (not necessarily at researcher level) before that point, there won't be investment to go over that scale until much later, when hardware improves and the cost goes down.

Thanks!

I largely disagree (even now I think having tried to play the inside game at labs looks pretty good, although I have sometimes disagreed with particular decisions in that direction because of opportunity costs). I'd be happy to debate if you'd find it productive (although I'm not sure whether I'm disagreeable enough to be a good choice).

At the top of this document.

I can't see a link to any LW dialog at the top.

And I doubt that Microsoft or Google have a program dedicated to "trying everything that look promising", even though it is true that they have manpower and hardware to do just that. But would they choose to do that?

Actually I'm under the impression a lot of what they do is just sharing papers in a company slack and reproducing stuff at scale. Now of course they might intuitively block out certain approaches that they think are dead-ends but turn out to be promising, but I wouldn't underestimate their agility at adapting new approaches if something unexpected is found.^[1]  My mental model is entirely informed from seeing Dwarkesh's interview with DeepMind researchers. They talk about ruthless efficiency in trying out new ideas and seeing what works. They also talk about how having more compute would make them X times better researchers. 

Yes, of course, we are seeing a rich stream of promising new things, ranging from evolutionary schemas (many of which tend towards open-endedness and therefore might be particularly unsafe, while very promising) to various derivatives of Mamba to potentially more interpretable architectures (like Kolmogorov-Arnold networks or like recent Memory Mosaics, which is an academic collaboration with Meta, but which has not been a consumer of significant compute yet) to GFlowNet motifs from Bengio group, and so on.

I think these are all at best marginal improvements and will be dwarfed by more compute^[2] or at least will only beat SOTA after being given more compute. I think the space for algo improvement for a given amount of compute is saturated quickly.  Also if anything, the average smaller place will over-index on techniques that crank out a little extra performance on smaller models but fail at scale. 

Of course, if a place with more hardware decides to adopt a non-standard scheme invented by a relatively hardware-poor place, the place with more hardware would win. 

My mental model of the hardware poor is they want to publicize their results as fast as they can so they get more clout, VC funding, or just getting essentially acquired by big tech. Academic recognition in the form of citations drive researchers. Getting rich drives the founders. 

The key here is that when people discuss "foom", they usually tend to focus on a (rather strong) argument that AGI is likely to be sufficient for "foom". But AGI is not necessary for "foom", one can have "foom" fully in progress before full AGI is achieved ("the road to superintelligence goes not via human equivalence, but around it").

Yes I agree there is a small possibility, but I find this is almost "pascal mugging". I think there is a stickiness of the AlphaGo model of things that's informing some choices which are objectively bad in a world where the AlphaGo model doesn't hold. The fear response to the low odds world is not appropriate for the high odds world.  

1. ^{^}

   I think the time it takes to deploy a model after training is making people think these labs are slower than they actually are. 

2. ^{^}

   As an example most improvements from Llama-3 came from just training the models on more data (with more compute). Sora looks worse than SOTA approaches until you throw more compute at it.

Agreed that it reflects on badly on the people involved, although less on Paul since he was only a "technical advisor" and arguably less responsible for thinking through / due diligence on the social aspects. It's frustrating to see the EA community (on EAF and Twitter at least) and those directly involved all ignoring this.

("shouldn’t be allowed anywhere near AI Safety decision making in the future" may be going too far though.)