When it comes to gene editing, our society decides to regulate its application but is very open that developing the underlying technology is valuable.
Here, I would refer to the third principle proposed in the “What Do We Want” section as well (on Cost-Benefit evaluation): I think that there should be at least more work done to try and anticipate / mitigate harms done by these general technologies. Like what is the rough likelihood of an extremely good outcome vs. extremely bad outcome for model X being deployed? If I add modification Y to it, does this change?
I don’t think our views are actually inconsistent here: if society scopes down the allowed usage of a general technology to comply with a set of regulatory standards that are deemed safe, that would work for me.
My personal view on the danger here really is really that there isn’t enough technical work here to mitigate the misusage of models, or even to enforce compliance in a good way. We really need technical work on that, and only then can we start effectively asking the regulation question. Until then, we might want to just delay release of super-powerful successors for this kind of technologies, until we can give better performance guarantees for systems like this, deployed this publicly.
We agree for sure that cost/benefit ought be better articulated when deploying these models (see the What Do We Want section on Cost-Benefit Analysis). The problem here really is the culture of blindly releasing and open-sourcing models like this, using a Go Fast And Break Things mentality, without at least making a case for what the benefits are, what the harms are, and not appealing to any existing standard when making these decisions.
Again, it’s possible (but not our position) that the specifics of DALLE-2 don’t bother you as much, but certainly the current culture we have around such models and their deployment seems an unambiguously alarming development.
The text-to-image models for education + communication here seems like a great idea! Moreover, I think it’s definitely consistent with what we’ve put forth here too, since you could probably fine-tune on graphics contained in papers related to your task at hand. The issue here really is that people are incurring unnecessary amounts of risk by making, say, an automatic Distill-er by using all images on the internet or something like that, when training on a smaller corpora would probably suffice, and vastly reduce the amount of possible risk of a model intended originally for Distill-ing papers. The fundamental position we advance that better protocols are needed before we start mass-deploying these models, and not that NO version of these models / technologies could be beneficial, ever.