AI ALIGNMENT FORUM
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AI doom from an LLM-plateau-ist perspective
watermark30

it does seem to be part of the situation we’re in

Maybe - I can see it being spun in two ways:

  1. The AI safety/alignment crowd was irrationally terrified of chatbots/current AI, forced everyone to pause, and then, unsurprisingly, didn't find anything scary
  2. The AI safety/alignment crowd need time to catch up their alignment techniques to keep up with the current models before things get dangerous in the future, and they did that

To point (1): alignment researchers aren't terrified of GPT-4 taking over the world, wouldn't agree to this characterization, and are no... (read more)

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AI doom from an LLM-plateau-ist perspective
watermark30

For me, the balance of considerations is that pause in scaling up LLMs will probably lead to more algorithmic progress

I'd consider this to be one of the more convincing reasons to be hesitant about a pause (as opposed to the 'crying wolf' argument, which seems to me like a dangerous way to think about coordinating on AI safety?). 

I don't have a good model for how much serious effort is currently going into algorithmic progress, so I can't say anything confidently there - but I would guess there's plenty and it's just not talked about? 

It might be... (read more)

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2Steve Byrnes
Can you elaborate on this? I think it’s incredibly stupid that people consider it to be super-blameworthy to overprepare for something that turned out not to be a huge deal—even if the expected value of the preparation was super-positive given what was known at the time. But, stupid as it may be, it does seem to be part of the situation we’re in. (What politician wants an article like this to be about them?) (Another example.) I’m in favor of interventions to try to change that aspect of our situation (e.g. widespread use and normalization of prediction markets??), but in the meantime, it seems to me that we should keep that dynamic in mind (among other considerations). Do you disagree with that in principle? Or think it’s overridden by other considerations? Or something else?
AI doom from an LLM-plateau-ist perspective
watermark21

I had a potential disagreement with your claim that a pause is probably counterproductive if there's a paradigm change required to reach AGI: even if the algorithms of the current paradigm aren't directly a part of the algorithm behind existentially dangerous AGI, advances in these algorithms will massively speed up research and progress towards this goal.

My take is: a “pause” in training unprecedentedly large ML models is probably good if TAI will look like (A-B), maybe good if TAI will look like (C), and probably counterproductive if TAI w

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3Steve Byrnes
I think that’s one consideration, but I think there are a bunch of considerations pointing in both directions. For example: Pause in scaling up LLMs → less algorithmic progress: * The LLM code-assistants or research-assistants will be worse * Maybe you can only make algorithmic progress via doing lots of GPT-4-sized training runs or bigger and seeing what happens * Maybe pause reduces AI profit which would otherwise be reinvested in R&D Pause in scaling up LLMs → more algorithmic progress: * Maybe doing lots of GPT-4-sized training runs or bigger is a distraction from algorithmic progress * In pause-world, it’s cheaper to get to the cutting edge, so more diverse researchers & companies are there, and they’re competing more narrowly on algorithmic progress (e.g. the best algorithms will get the highest scores on benchmarks or whatever, as opposed to whatever algorithms got scaled the most getting the highest scores) Other things: * Pro-pause: It’s “practice for later”, “policy wins beget policy wins”, etc., so it will be easier next time (related) * Anti-pause: People will learn to associate “AI pause” = “overreaction to a big nothing”, so it will be harder next time (related) * Pro-pause: Needless to say, maybe I’m wrong and LLMs won’t plateau! There are probably other things too. For me, the balance of considerations is that pause in scaling up LLMs will probably lead to more algorithmic progress. But I don’t have great confidence. (We might differ in how much of a difference we’re expecting LLM code-assistants and research-assistants to make. I put them in the same category as PyTorch and TensorFlow and IDEs and stackoverflow and other such productivity-enhancers that we’re already living with, as opposed to something wildly more impactful than that.)