before they are developed radically faster by AI they will be developed slightly faster.
I see a couple reasons why this wouldn’t be true:
First, consider LLM progress: overall perplexity increases relatively smoothly, particular capabilities emerge abruptly. As such the ability to construct a coherent Arxiv paper interpolating between two papers from different disciplines seems likely to emerge abruptly. I.e. currently asking a LLM to do this would generate a paper with zero useful ideas, and we have no reason to expect that the first GPT-N to be able to do this will only generate half, or one idea. It is just as likely to generate five+ very useful ideas.
There are a couple ways one might expect continuity via acceleration in AI-driven research in the run up to GPT-N (both of which I disagree with): Quoc Le-style AI-based NAS is likely to have continued apace in the run up to GPT-N, but for this to provide continuity you have to claim that the year GPT-N starts moving AI research forwards, AI NAS built up to just the rightrate of progress needed to allow GPT-N to fit the trend. Otherwise there might be a sequence of research-relevant, intermediate tasks which GPT-(N-i) will develop competency on—thereby accelerating research. I don’t see what those tasks would be[1].
I don’t think interdisciplinarity is a silver bullet for making faster progress on deep learning.
Second, I agree that interdisciplinarity, when building upon a track record of within-discipline progress, would be continuous. However, we should expect Arxiv and/or Github-trained LLMs to skip the mono-disciplinary research acceleration phase. In effect, I expect there to be no time in between when we can get useful answers to “Modify transformer code so that gradients are more stable during training”, and “Modify transformer code so that gradients are more stable during training, but change the transformer architecture to make use of spiking”.
If you disagree, how do you imagine continuous progress leading up to the above scenario? An important case is if Codex/Github Copilot improves continuously along the way taking a larger and larger role in ML repo authorship. If we assume that AGI arrives without depending onLLMs achieving understanding of recent Arxiv papers, then I agree that this scenario is much more likely to feature continuity in AI-driven AI research. I’m highly uncertain about how this assumption will play out. Off the top of my head, 40% of codex-driven research reaches AGI before Arxiv understanding.
I see a couple reasons why this wouldn’t be true:
First, consider LLM progress: overall perplexity increases relatively smoothly, particular capabilities emerge abruptly. As such the ability to construct a coherent Arxiv paper interpolating between two papers from different disciplines seems likely to emerge abruptly. I.e. currently asking a LLM to do this would generate a paper with zero useful ideas, and we have no reason to expect that the first GPT-N to be able to do this will only generate half, or one idea. It is just as likely to generate five+ very useful ideas.
There are a couple ways one might expect continuity via acceleration in AI-driven research in the run up to GPT-N (both of which I disagree with): Quoc Le-style AI-based NAS is likely to have continued apace in the run up to GPT-N, but for this to provide continuity you have to claim that the year GPT-N starts moving AI research forwards, AI NAS built up to just the right rate of progress needed to allow GPT-N to fit the trend. Otherwise there might be a sequence of research-relevant, intermediate tasks which GPT-(N-i) will develop competency on—thereby accelerating research. I don’t see what those tasks would be[1].
Second, I agree that interdisciplinarity, when building upon a track record of within-discipline progress, would be continuous. However, we should expect Arxiv and/or Github-trained LLMs to skip the mono-disciplinary research acceleration phase. In effect, I expect there to be no time in between when we can get useful answers to “Modify transformer code so that gradients are more stable during training”, and “Modify transformer code so that gradients are more stable during training, but change the transformer architecture to make use of spiking”.
If you disagree, how do you imagine continuous progress leading up to the above scenario? An important case is if Codex/Github Copilot improves continuously along the way taking a larger and larger role in ML repo authorship. If we assume that AGI arrives without depending on LLMs achieving understanding of recent Arxiv papers, then I agree that this scenario is much more likely to feature continuity in AI-driven AI research. I’m highly uncertain about how this assumption will play out. Off the top of my head, 40% of codex-driven research reaches AGI before Arxiv understanding.
Perhaps better and better versions of Ought’s work. I doubt this work will scale to the levels of research utility relevant here.