Thanks for the comment. I hope you think this is interesting content.
I’m not sure I understand what the argument is.
The most important points we want to argue with this post are that (1) if a system itself is made to be safe, but it’s copycatted and open-sourced, then the safety measures were not effective (2) it is bad when developers like OpenAI publish incomplete/overly-convenient analysis of the risks of what they develop that, for example, ignore copycatting, and (3) the points from “What do we want”? and “What should we do?”
″...we are not convinced of any fundamental social benefits that film provides aside from, admittedly, being entertaining...”
Yes entertainment has value, but I don’t think that entertainment from text-to-image models is/will be commensurate with film. I could also very easily list a lot of non-entertainment uses of film involving stuff like education, communication, etc. And I think someone from 1910 could easily think of these as well. What stuff like this would you predict from text-to-image diffusion models?
So, what justifies this casual dismissal of the entertainment value of text-to-image models?
We don’t. We argue that it’s unlikely to outweigh harms.
Your primary conclusion is that “the AI research community should curtail work on risky capabilities”.
I wouldn’t say this is our primary conclusion. See my first response above. Also, I don’t think this is obvious. Sam Altman, Demis Hassabis, and many others strongly disagree with this.
The problem is coordinating our behavior. If OpenAI decides not to work on it, someone else will
We disagree that the counterfactual to OpenAI not working on some projects like DALLE2 or GPT4 would be similar to the status quo. We discussed this in the paragraph that says ”...On one hand, AI generators for offensive content were probably always inevitable. However...”
...Government bans? Do you realize how hard it would be to prevent text-to-image models from being created and shared...
Yes. We do not advocate for government bans. My answer to this is essentially what we wrote in the “The role of AI governance.” I don’t have much to add beyond what we already wrote. I recommend rereading that section. In short, there are regulatory tools that can be used. For example, the FTC may have a considerable amount of power in some cases.
Are we talking about 100 people a year being victimized? That would indeed be sad, but compared to potential human extinction from AI, probably not as big of a deal.
Where did the number 100 come from? In the post, we cite one article about a study from 2019 that found ~15,000 deepfakes online. That was in 2019 when image and video generation were much less developed than today. And in the future, things may be much more widespread because of open-source tools based on SD that are easy to use.
Another really important point, I think, is that we argue in the post that trying to avoid dynamics involving racing toward TAI, copycatting, and open-sourcing of models will LESSEN X-risk. You wrote your comment as if we are trying to argue that preventing sex crimes are more important than X-risk. We don’t say this. I recommend rereading the “But even if one does not view the risks specific to text-to-image models as a major concern...” paragraph and the “The role of AI researchers” section.
Finally, and I want to put a star on this point—we all should care a lot about sex crime. And I’m sure you do. Writing off problems like this by comparing them to X-risk (1) isn’t valid in this case because we argue for improving the dev ecosystem to address both of these problems, (2) should be approached with great care and good data if it needs to be done, and (3) is one type of thing that leads to a lot of negativity and bad press about EA.
I think this is probably even more true for your comments on entertainment value and whether that might outweigh the harms of deepfake sex crimes. First, I’m highly skeptical that we will find uses for text-to-image models that are so widely usable and entertaining that it would be commensurate to the harms of diffusion-deepfake sex crime. But even if we could be confident that entertainment would hypothetically outweigh sex crimes on pure utilitarian grounds, in the real world with real politics and EA critics, I do not think this position would be tenable. It could serve to undermine support for EA and end up being very negative if widespread.
But even if we could be confident that entertainment would hypothetically outweigh sex crimes on pure utilitarian grounds, in the real world with real politics and EA critics, I do not think this position would be tenable.
Isn’t this basically society’s revealed position on, say, cameras? People can and do use cameras for sex crimes (e.g. voyeurism) but we don’t regulate cameras in order to reduce sex crimes.
I agree that PR-wise it’s not a great look to say that benefits outweigh risks when the risks are sex crimes but that’s because PR diverges wildly from reality. (And if cameras were invented today, I’d expect we’d have the same PR arguments about them.)
None of this is to imply a position on deepfakes—I don’t know nearly enough about them. My position is just that it should in fact come down to a cost/benefit calculation.
I could also very easily list a lot of non-entertainment uses of film involving stuff like education, communication, etc.
Random nitpick, but text-to-image models seem plausibly very useful for education and communication. I would love for people’s slide decks with pages and pages of text to be replaced by images that convey the same points better. Maybe imagine Distill-like graphics / papers, except that it no longer takes 5x as long to produce them relative to a normal paper.
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.
Thanks for the comment. I hope you think this is interesting content.
The most important points we want to argue with this post are that (1) if a system itself is made to be safe, but it’s copycatted and open-sourced, then the safety measures were not effective (2) it is bad when developers like OpenAI publish incomplete/overly-convenient analysis of the risks of what they develop that, for example, ignore copycatting, and (3) the points from “What do we want”? and “What should we do?”
Yes entertainment has value, but I don’t think that entertainment from text-to-image models is/will be commensurate with film. I could also very easily list a lot of non-entertainment uses of film involving stuff like education, communication, etc. And I think someone from 1910 could easily think of these as well. What stuff like this would you predict from text-to-image diffusion models?
We don’t. We argue that it’s unlikely to outweigh harms.
I wouldn’t say this is our primary conclusion. See my first response above. Also, I don’t think this is obvious. Sam Altman, Demis Hassabis, and many others strongly disagree with this.
We disagree that the counterfactual to OpenAI not working on some projects like DALLE2 or GPT4 would be similar to the status quo. We discussed this in the paragraph that says ”...On one hand, AI generators for offensive content were probably always inevitable. However...”
Yes. We do not advocate for government bans. My answer to this is essentially what we wrote in the “The role of AI governance.” I don’t have much to add beyond what we already wrote. I recommend rereading that section. In short, there are regulatory tools that can be used. For example, the FTC may have a considerable amount of power in some cases.
Where did the number 100 come from? In the post, we cite one article about a study from 2019 that found ~15,000 deepfakes online. That was in 2019 when image and video generation were much less developed than today. And in the future, things may be much more widespread because of open-source tools based on SD that are easy to use.
Another really important point, I think, is that we argue in the post that trying to avoid dynamics involving racing toward TAI, copycatting, and open-sourcing of models will LESSEN X-risk. You wrote your comment as if we are trying to argue that preventing sex crimes are more important than X-risk. We don’t say this. I recommend rereading the “But even if one does not view the risks specific to text-to-image models as a major concern...” paragraph and the “The role of AI researchers” section.
Finally, and I want to put a star on this point—we all should care a lot about sex crime. And I’m sure you do. Writing off problems like this by comparing them to X-risk (1) isn’t valid in this case because we argue for improving the dev ecosystem to address both of these problems, (2) should be approached with great care and good data if it needs to be done, and (3) is one type of thing that leads to a lot of negativity and bad press about EA.
I think this is probably even more true for your comments on entertainment value and whether that might outweigh the harms of deepfake sex crimes. First, I’m highly skeptical that we will find uses for text-to-image models that are so widely usable and entertaining that it would be commensurate to the harms of diffusion-deepfake sex crime. But even if we could be confident that entertainment would hypothetically outweigh sex crimes on pure utilitarian grounds, in the real world with real politics and EA critics, I do not think this position would be tenable. It could serve to undermine support for EA and end up being very negative if widespread.
Isn’t this basically society’s revealed position on, say, cameras? People can and do use cameras for sex crimes (e.g. voyeurism) but we don’t regulate cameras in order to reduce sex crimes.
I agree that PR-wise it’s not a great look to say that benefits outweigh risks when the risks are sex crimes but that’s because PR diverges wildly from reality. (And if cameras were invented today, I’d expect we’d have the same PR arguments about them.)
None of this is to imply a position on deepfakes—I don’t know nearly enough about them. My position is just that it should in fact come down to a cost/benefit calculation.
Random nitpick, but text-to-image models seem plausibly very useful for education and communication. I would love for people’s slide decks with pages and pages of text to be replaced by images that convey the same points better. Maybe imagine Distill-like graphics / papers, except that it no longer takes 5x as long to produce them relative to a normal paper.
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.