There are 4 points of disagreement I have about this post.
First, I think it’s fundamentally based on a strawperson.
my fundamental objection is that their specific strategy for delaying AI is not well targeted.
This post provides an argument for not adopting the “neo-luddite” agenda or not directly empowering neo-luddites. This is not an argument against allying with neo-luddites for specific purposes. I don’t know of anyone who has actually advocated for the former. This is not how I would characterize Katija’s post.
Second, I think there is an inner strawperson with the example about text-to-image models. From a bird’s eye view, I agree with caring very little about these models mimicking humans artistic styles. But this is not where the vast majority of tangible harm may be coming from with text-to-image models. I think that this most likely comes from non-consensual deepfakes being easy to use for targeted harassment, humiliation, and blackmail. I know you’ve seen the EA forum post about this because you commented on it. But I’d be interested in seeing a reply to my reply to your comment on the post.
Third, I think that this post fails to consider how certain (most?) regulations that neo-luddites would support could meaningfully slow risky things down. In general, any type of regulation that makes research and dev for risky AI technologies harder or less incentivized will in fact slow risky AI progress down. I think that the one example you bring up—text-to-image models—is a counterexample to your point. Suppose we pass a bunch of restrictive IP laws that make it more painful to research, develop, and deploy text-to-image models. That would suddenly slow down this branch of research which could concievably be useful for making riskier AI in the future (e.g. multimodal media generators), hinder revenue opportunities for companies who are speeding up risky AI progress, close off this revenue option to possible future companies who may do the same, and establish law/case law/precedent around generative models that could be set precedent or be repurposed for other types of AI later.
Fourth, I also am not convinced by the specific argument about how indiscriminate regulation could make alignment harder.
Suppose the neo-luddites succeed, and the US congress overhauls copyright law. A plausible consequence is that commercial AI models will only be allowed to be trained on data that was licensed very permissively, such as data that’s in the public domain...Right now, if an AI org needs some data that they think will help with alignment, they can generally obtain it, unless that data is private.
This is a nitpick, but I don’t actually predict this scenario would pan out. I don’t think we’d realistically overhaul copyright law and have the kind of regime with datasets that you describe. But this is probably a question for policy people. There are also neo-luddite solutions that your argument would not apply to—like having legal requirements for companies to make their models “forget” certain content upon request. This would only be a hindrance to the deployer.
Ultimately though, what matters is not whether something makes certain alignment research harder. It matters how much something makes alignment research harder relative to how much it makes risky research harder. Alignment researchers are definitely the ones that are differentially data-hungry. What’s a concrete, concievable story in which something like the hypothetical law you described makes things differentially harder for alignment researchers compared to capabilities researchers?
There are 4 points of disagreement I have about this post.
First, I think it’s fundamentally based on a strawperson.
This post provides an argument for not adopting the “neo-luddite” agenda or not directly empowering neo-luddites. This is not an argument against allying with neo-luddites for specific purposes. I don’t know of anyone who has actually advocated for the former. This is not how I would characterize Katija’s post.
Second, I think there is an inner strawperson with the example about text-to-image models. From a bird’s eye view, I agree with caring very little about these models mimicking humans artistic styles. But this is not where the vast majority of tangible harm may be coming from with text-to-image models. I think that this most likely comes from non-consensual deepfakes being easy to use for targeted harassment, humiliation, and blackmail. I know you’ve seen the EA forum post about this because you commented on it. But I’d be interested in seeing a reply to my reply to your comment on the post.
Third, I think that this post fails to consider how certain (most?) regulations that neo-luddites would support could meaningfully slow risky things down. In general, any type of regulation that makes research and dev for risky AI technologies harder or less incentivized will in fact slow risky AI progress down. I think that the one example you bring up—text-to-image models—is a counterexample to your point. Suppose we pass a bunch of restrictive IP laws that make it more painful to research, develop, and deploy text-to-image models. That would suddenly slow down this branch of research which could concievably be useful for making riskier AI in the future (e.g. multimodal media generators), hinder revenue opportunities for companies who are speeding up risky AI progress, close off this revenue option to possible future companies who may do the same, and establish law/case law/precedent around generative models that could be set precedent or be repurposed for other types of AI later.
Fourth, I also am not convinced by the specific argument about how indiscriminate regulation could make alignment harder.
This is a nitpick, but I don’t actually predict this scenario would pan out. I don’t think we’d realistically overhaul copyright law and have the kind of regime with datasets that you describe. But this is probably a question for policy people. There are also neo-luddite solutions that your argument would not apply to—like having legal requirements for companies to make their models “forget” certain content upon request. This would only be a hindrance to the deployer.
Ultimately though, what matters is not whether something makes certain alignment research harder. It matters how much something makes alignment research harder relative to how much it makes risky research harder. Alignment researchers are definitely the ones that are differentially data-hungry. What’s a concrete, concievable story in which something like the hypothetical law you described makes things differentially harder for alignment researchers compared to capabilities researchers?