Can we quantify the value of theoretical alignment research before and after ChatGPT?
For example, mech interp research seems much more practical now. If alignment proves to be more of an engineering problem than a theoretical one, then I don’t see how you can meaningfully make progress without precursor models.
Furthermore, given how nearly everyone with a lot of GPUs is getting similar results to OAI, where similar means within 1 OOM, it’s likely that in the future someone would have stumbled upon AGI with the compute of the 2030s.
Let’s say their secret sauce gives them the equivalent of 1 extra hardware generation (even this is pretty generous). That’s only ~2-3 years. Meta built a $10B data center to match TikTok’s content algorithm. This datacenter meant to decide which videos to show to users happened to catch up to GPT-4!
I suspect the “ease” of making GPT-3/4 informed OAI’s choice to publicize their results.
I wonder if you’re getting disagreement strictly over that last line. I think that all makes sense, but I strongly suspect that the ease of making ChatGPT had nothing to do with their decision to publicize and commercialize.
There’s little reason to think that alignment is an engineering problem to the exclusion of theory. But making good theory is also partly dependent on knowing about the system you’re addressing, so I think there’s a strong argument that that progress accelerated alignment work as strongly as capabilities.
I think the argument is that it would be way better to do all the work we could on alignment before advancing capabilities at all. Which it would be. If we were not only a wise species, but a universally utilitarian one (see my top level response on that if you care). Which we are decidedly not.
Can we quantify the value of theoretical alignment research before and after ChatGPT?
For example, mech interp research seems much more practical now. If alignment proves to be more of an engineering problem than a theoretical one, then I don’t see how you can meaningfully make progress without precursor models.
Furthermore, given how nearly everyone with a lot of GPUs is getting similar results to OAI, where similar means within 1 OOM, it’s likely that in the future someone would have stumbled upon AGI with the compute of the 2030s.
Let’s say their secret sauce gives them the equivalent of 1 extra hardware generation (even this is pretty generous). That’s only ~2-3 years. Meta built a $10B data center to match TikTok’s content algorithm. This datacenter meant to decide which videos to show to users happened to catch up to GPT-4!
I suspect the “ease” of making GPT-3/4 informed OAI’s choice to publicize their results.
I wonder if you’re getting disagreement strictly over that last line. I think that all makes sense, but I strongly suspect that the ease of making ChatGPT had nothing to do with their decision to publicize and commercialize.
There’s little reason to think that alignment is an engineering problem to the exclusion of theory. But making good theory is also partly dependent on knowing about the system you’re addressing, so I think there’s a strong argument that that progress accelerated alignment work as strongly as capabilities.
I think the argument is that it would be way better to do all the work we could on alignment before advancing capabilities at all. Which it would be. If we were not only a wise species, but a universally utilitarian one (see my top level response on that if you care). Which we are decidedly not.