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This is: Developmental Stages of GPTs , published by orthonormal on the AI Alignment Forum.
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.
Epistemic Status: I only know as much as anyone else in my reference class (I build ML models, I can grok the GPT papers, and I don't work for OpenAI or a similar lab). But I think my thesis is original.
Related: Gwern on GPT-3
For the last several years, I've gone around saying that I'm worried about transformative AI, an AI capable of making an Industrial Revolution sized impact (the concept is agnostic on whether it has to be AGI or self-improving), because I think we might be one or two cognitive breakthroughs away from building one.
GPT-3 has made me move up my timelines, because it makes me think we might need zero more cognitive breakthroughs, just more refinement / efficiency / computing power: basically, GPT-6 or GPT-7 might do it. My reason for thinking this is comparing GPT-3 to GPT-2, and reflecting on what the differences say about the "missing pieces" for transformative AI.
My Thesis:
The difference between GPT-2 and GPT-3 has made me suspect that there's a legitimate comparison to be made between the scale of a network architecture like the GPTs, and some analogue of "developmental stages" of the resulting network. Furthermore, it's plausible to me that the functions needed to be a transformative AI are covered by a moderate number of such developmental stages, without requiring additional structure. Thus GPT-N would be a transformative AI, for some not-too-large N, and we need to redouble our efforts on ways to align such AIs.
The thesis doesn't strongly imply that we'll reach transformative AI via GPT-N especially soon; I have wide uncertainty, even given the thesis, about how large we should expect N to be, and whether the scaling of training and of computation slows down progress before then. But it's also plausible to me now that the timeline is only a few years, and that no fundamentally different approach will succeed before then. And that scares me.
Architecture and Scaling
GPT, GPT-2, and GPT-3 use nearly the same architecture; each paper says as much, with a sentence or two about minor improvements to the individual transformers. Model size (and the amount of training computation) is really the only difference.
GPT took 1 petaflop/s-day to train 117M parameters, GPT-2 took 10 petaflop/s-days to train 1.5B parameters, and the largest version of GPT-3 took 3,000 petaflop/s-days to train 175B parameters. By contrast, AlphaStar seems to have taken about 30,000 petaflop/s-days of training in mid-2019, so the pace of AI research computing power projects that there should be about 10x that today. The upshot is that OpenAI may not be able to afford it, but if Google really wanted to make GPT-4 this year, they could afford to do so.
Analogues to Developmental Stages
There are all sorts of (more or less well-defined) developmental stages for human beings: image tracking, object permanence, vocabulary and grammar, theory of mind, size and volume, emotional awareness, executive functioning, et cetera.
I was first reminded of developmental stages a few years ago, when I saw the layers of abstraction generated in this feature visualization tool for GoogLeNet.
We don't have feature visualization for language models, but we do have generative outputs. And as you scale up an architecture like GPT, you see higher levels of abstraction. Grammar gets mastered, then content (removing absurd but grammatical responses), then tone (first rough genre, then spookily accurate authorial voice). Topic coherence is mastered first on the phrase level, then the sentence level, then the paragraph level. So too with narrative flow.
Gwern's poetry experiments (GPT-2, GPT-3) are good examples. GPT-2 could more ...
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