My response here would be similar to this one. I think there’s a kind of “bitter lesson” here: for particularly complex fields, it’s often easier to solve the general problem of which that field is an instance of, rather than attempting to solve the field directly. For example:
If you’re trying to solve mechanistic interpretability, studying a specific LLM in detail isn’t the way; you’d be better off trying to find methods that generalize across many LLMs.
If you’re trying to solve natural-language processing, turns out tailor-made methods are dramatically out-performed by general-purpose generative models (LLMs) trained by a general-purpose search method (SGD).
If you’re trying to advance bioscience, you can try building models of biology directly, or you can take the aforementioned off-the-shelf general-purpose generative model, dump biology data into it, and get a tool significantly ahead of your manual efforts.
Broadly, LLMs/DL have “solved” or outperformed a whole bunch of fields at once, without even deliberately trying, simply as the result of looking for something general and scalable.
Like, yeah, after you’ve sketched out your general-purpose method and you’re looking for where to apply it, you’d need to study the specific details of the application domain and tinker with your method’s implementation. But the load-bearing, difficult step is deriving the general-purpose method itself; the last-step fine-tuning is comparatively easy.
In addition, I’m not optimistic about solving e. g. interpretability directly, simply because there’s already a whole field of people trying to do that, to fairly leisurely progress. On intelligence-enhancement front, there would be mountains of regulatory red tape to go through, and the experimental loops would be rate-limited by the slow human biology. Etc., etc.
My response here would be similar to this one. I think there’s a kind of “bitter lesson” here: for particularly complex fields, it’s often easier to solve the general problem of which that field is an instance of, rather than attempting to solve the field directly. For example:
If you’re trying to solve mechanistic interpretability, studying a specific LLM in detail isn’t the way; you’d be better off trying to find methods that generalize across many LLMs.
If you’re trying to solve natural-language processing, turns out tailor-made methods are dramatically out-performed by general-purpose generative models (LLMs) trained by a general-purpose search method (SGD).
If you’re trying to advance bioscience, you can try building models of biology directly, or you can take the aforementioned off-the-shelf general-purpose generative model, dump biology data into it, and get a tool significantly ahead of your manual efforts.
Broadly, LLMs/DL have “solved” or outperformed a whole bunch of fields at once, without even deliberately trying, simply as the result of looking for something general and scalable.
Like, yeah, after you’ve sketched out your general-purpose method and you’re looking for where to apply it, you’d need to study the specific details of the application domain and tinker with your method’s implementation. But the load-bearing, difficult step is deriving the general-purpose method itself; the last-step fine-tuning is comparatively easy.
In addition, I’m not optimistic about solving e. g. interpretability directly, simply because there’s already a whole field of people trying to do that, to fairly leisurely progress. On intelligence-enhancement front, there would be mountains of regulatory red tape to go through, and the experimental loops would be rate-limited by the slow human biology. Etc., etc.