Thanks a lot for all the effort you put into this post! I don’t agree with anything, but reading and commenting it was very stimulating, and probably useful for my own research.
Likewise, thanks for taking the time to write such a long comment! And hoping that’s a typo in the second sentence :)
I’m quite curious about why you wrote this post. If it’s for convincing researchers in AI Safety that transparency is useful and important for AI Alignment, my impression is that many researchers do agree, and those who don’t tend to have thought about it for quite some time (Paul Christiano comes to mind, as someone who is less interested in transparency while knowing a decent amount about it). So if the goal was to convince people to care about transparency, I’m not sure this post was necessary.
Fair enough! Since I’m pretty new to thinking about this stuff, my main goal was to convince myself and organize my own thoughts around this topic. I find that writing a review is often a good way to get up to speed on something. Then once I’d written it, it seemed like I might as well post it somewhere.
Wrt the community though, I’d be especially curious to get more feedback on Motivation #2. Do people not agree that transparency is *necessary* for AI Safety? And if they do agree, then why aren’t more people working on it?
I agree with the idea, with maybe the caveat that it doesn’t apply to Ems à la Hanson. A similar argument could hold about neuroscience facts we would need to know to scan and simulate brains, though.
Yeah, I’d add that if even we had a similar hardware-based forecast for mapping the human connectome, there would still be a lot that we don’t know about dynamics there too. I have the impression that basically all ways to forecast things in this space have to make somenon-obvious (to me) assumption that business as usual will scale up to strong AI without a need for qualitative breakthroughs.
My take on why verification might scale is that we will move towards specification of properties of the program instead of it’s input/output relation. So verifying whether the code satisfy some formal property that indicates myopia or low goal-directedness. Note that transparency is still really important here, because even with completely formal definitions of things like myopia and goal-directedness, I think transparency will be necessary to translate them into properties of the specific class of models studied (neural networks for example).
I agree, but think that transparency is doing most of the work there (i.e. what you say sounds more to me like an application of transparency than scaling up the way that verification is used in current models.) But this is just semantics.
I think this misses a very big part of what makes a paperclip-maximizer dangerous—the fact that it can come up with catastrophic plans after it’s been deployed. So it doesn’t have to be explicitly deceptive and bidding it’s time; it might just be really competent and focused on maximizing paperclips, which requires more than exact transparency to catch. It requires being able to check properties that ensures the catastrophic outcomes won’t happen.
Hm, I want to disagree, but this may just come down to a difference in what we mean by deployment. In the paragraph that you quoted, I was imagining the usual train/deploy split from ML where deployment means that we’ve frozen the weights of our AI and prohibit further learning from taking place. In that case, I’d like to emphasize that there’s a difference between intelligence as a meta-ability to acquire new capabilities and a system’s actual capabilities at a given time. Even if an AI is superintelligent, i.e. able to write new information into its weights extremely efficiently, once those weights are fixed, it can only reason and plan using whatever object-level knowledge was encoded in them up to that point. So if there was nothing about bio weapons in the weights when we froze them, then we wouldn’t expect the paperclip-maximizer to spontaneously make plans involving bio weapons when deployed.
On the other hand, none of this would apply to the “alien in a box” model that would basically be continuously training by my definition (though in that case, we could still patch the solution by monitoring the AI in real time). So maybe it was a poor choice of words.
An AI who does AI Safety research is properly terrifying. I’m really stunned by this choice, as I think this is probably one of the most dangerous case of oracle AI that I can think of. I see two big problems with it:
It looks like exactly the kind of tasks where, if we haven’t solve AI alignment in advance, Goodhart is upon us. What’s the measure? What’s the proxy? Best scenario: the AI is clearly optimizing something stupid, and nobody cares. Worst case scenario, more probably because the AI is actually supposed to outperform humans: it pushes for something that looks like it makes sense but doesn’t actually work, and we might use these insight to build more advanced AGIs and be fucked.
It’s quite simple to imagine a Predict-o-matic type scenario: pushing simpler and easier models that appear to work but don’t, so that its task becomes easier.
I don’t think any of the intuitions given work, for a simple reason: even if the research agenda doesn’t require in itself any real knowledge of humans, the outputs still have to be humanly understandable. I want the AI to write blog posts that I can understand. So it will have to master clear writing, which seems from experience to require a lot of modeling of the other (and as a human, I get a bunch of things for free unconsciously, that an AI wouldn’t have, like a model of emotions).
These two comments seem related so let me reply to them together. I think what you’re asking here is “how can we be sure that a “research accelerator” AI, trained to help with a self-contained AI safety agenda such as transparency, will produce solutions that we can understand before we implement them [so as to avoid getting tricked into implementing something that turns out to be bad, as in your first quote]?” And I would answer that I’ve made an assumption that knowledge is universal and new ideas are discovered by incrementally building on existing ones. This is why basically any student today knows more about science than the smartest people from a century ago, and on the flip side, I think would constrain how far beyond us the insights from early AGIs trained on our work could be. Suppose an AI system was trained on a dataset of existing transparency papers to come up with new project ideas in transparency. Then its first outputs would probably use words like neurons and weights instead of some totally incomprehensible concepts, since those would be the very same concepts that would let it efficiently make sense of its training set. And new ideas about neurons and weights would then be things that we could independently reason about even if they’re very clever ideas that we didn’t think of ourselves, just like you and I can have a conversation about circuits even if we didn’t come up with it.
Another issue with this proposal is that you’re saying on one side that the AI is superhuman at technical AI safety, and on the other hand that it can only do these specific proposals that don’t use anything about humans. That’s like saying that you have an AI that wins at any game, but in fact it only works for chess. Either the AI can do research on everything in AI Safety, and it will probably have to understand humans; or it is specifically for one research proposal, but then I don’t see why not create other AIs for other research proposals. The technology is available, and the incentives would be here (if only to be as productive as the other researchers who have an AI to help them).
Agree that there’s a (strong!) assumption being made that “research accelerators for narrow agendas” will come before potentially dangerous AI systems. I think this might actually be a weak point of my story. Rohin asked something similar in the second bullet-point of his comment so I’ll try to answer there...
Likewise, thanks for taking the time to write such a long comment! And hoping that’s a typo in the second sentence :)
You’re welcome. And yes, this was as typo that I corrected. ^^
Wrt the community though, I’d be especially curious to get more feedback on Motivation #2. Do people not agree that transparency is *necessary* for AI Safety? And if they do agree, then why aren’t more people working on it?
My take is that a lot of people around here agree that transparency is at least useful, and maybe necessary. And the main reason why people are not working on it is a mix of personal fit, and the fact that without research in AI Alignment proper, transparency doesn’t seem that useful (if we don’t know what to look for).
I agree, but think that transparency is doing most of the work there (i.e. what you say sounds more to me like an application of transparency than scaling up the way that verification is used in current models.) But this is just semantics.
Well, transparency is doing some work, but it’s totally unable to prove anything. That’s a big part of the approach I’m proposing. That being said, I agree that this doesn’t look like scaling the current way.
Hm, I want to disagree, but this may just come down to a difference in what we mean by deployment. In the paragraph that you quoted, I was imagining the usual train/deploy split from ML where deployment means that we’ve frozen the weights of our AI and prohibit further learning from taking place. In that case, I’d like to emphasize that there’s a difference between intelligence as a meta-ability to acquire new capabilities and a system’s actual capabilities at a given time. Even if an AI is superintelligent, i.e. able to write new information into its weights extremely efficiently, once those weights are fixed, it can only reason and plan using whatever object-level knowledge was encoded in them up to that point. So if there was nothing about bio weapons in the weights when we froze them, then we wouldn’t expect the paperclip-maximizer to spontaneously make plans involving bio weapons when deployed.
You’re right that I was thinking of a more online system that could update it’s weights during deployment. Yet even with frozen weights, I definitely expect the model to make plans involving things that were not involved. For example, it might not have a bio-weapon feature, but the relevant subfeature to build some by quite local rules that don’t look like a plan to build a bio-weapon.
Suppose an AI system was trained on a dataset of existing transparency papers to come up with new project ideas in transparency. Then its first outputs would probably use words like neurons and weights instead of some totally incomprehensible concepts, since those would be the very same concepts that would let it efficiently make sense of its training set. And new ideas about neurons and weights would then be things that we could independently reason about even if they’re very clever ideas that we didn’t think of ourselves, just like you and I can have a conversation about circuits even if we didn’t come up with it.
Likewise, thanks for taking the time to write such a long comment! And hoping that’s a typo in the second sentence :)
Fair enough! Since I’m pretty new to thinking about this stuff, my main goal was to convince myself and organize my own thoughts around this topic. I find that writing a review is often a good way to get up to speed on something. Then once I’d written it, it seemed like I might as well post it somewhere.
Wrt the community though, I’d be especially curious to get more feedback on Motivation #2. Do people not agree that transparency is *necessary* for AI Safety? And if they do agree, then why aren’t more people working on it?
Yeah, I’d add that if even we had a similar hardware-based forecast for mapping the human connectome, there would still be a lot that we don’t know about dynamics there too. I have the impression that basically all ways to forecast things in this space have to make some non-obvious (to me) assumption that business as usual will scale up to strong AI without a need for qualitative breakthroughs.
I agree, but think that transparency is doing most of the work there (i.e. what you say sounds more to me like an application of transparency than scaling up the way that verification is used in current models.) But this is just semantics.
Hm, I want to disagree, but this may just come down to a difference in what we mean by deployment. In the paragraph that you quoted, I was imagining the usual train/deploy split from ML where deployment means that we’ve frozen the weights of our AI and prohibit further learning from taking place. In that case, I’d like to emphasize that there’s a difference between intelligence as a meta-ability to acquire new capabilities and a system’s actual capabilities at a given time. Even if an AI is superintelligent, i.e. able to write new information into its weights extremely efficiently, once those weights are fixed, it can only reason and plan using whatever object-level knowledge was encoded in them up to that point. So if there was nothing about bio weapons in the weights when we froze them, then we wouldn’t expect the paperclip-maximizer to spontaneously make plans involving bio weapons when deployed.
On the other hand, none of this would apply to the “alien in a box” model that would basically be continuously training by my definition (though in that case, we could still patch the solution by monitoring the AI in real time). So maybe it was a poor choice of words.
These two comments seem related so let me reply to them together. I think what you’re asking here is “how can we be sure that a “research accelerator” AI, trained to help with a self-contained AI safety agenda such as transparency, will produce solutions that we can understand before we implement them [so as to avoid getting tricked into implementing something that turns out to be bad, as in your first quote]?” And I would answer that I’ve made an assumption that knowledge is universal and new ideas are discovered by incrementally building on existing ones. This is why basically any student today knows more about science than the smartest people from a century ago, and on the flip side, I think would constrain how far beyond us the insights from early AGIs trained on our work could be. Suppose an AI system was trained on a dataset of existing transparency papers to come up with new project ideas in transparency. Then its first outputs would probably use words like neurons and weights instead of some totally incomprehensible concepts, since those would be the very same concepts that would let it efficiently make sense of its training set. And new ideas about neurons and weights would then be things that we could independently reason about even if they’re very clever ideas that we didn’t think of ourselves, just like you and I can have a conversation about circuits even if we didn’t come up with it.
Agree that there’s a (strong!) assumption being made that “research accelerators for narrow agendas” will come before potentially dangerous AI systems. I think this might actually be a weak point of my story. Rohin asked something similar in the second bullet-point of his comment so I’ll try to answer there...
You’re welcome. And yes, this was as typo that I corrected. ^^
My take is that a lot of people around here agree that transparency is at least useful, and maybe necessary. And the main reason why people are not working on it is a mix of personal fit, and the fact that without research in AI Alignment proper, transparency doesn’t seem that useful (if we don’t know what to look for).
Well, transparency is doing some work, but it’s totally unable to prove anything. That’s a big part of the approach I’m proposing. That being said, I agree that this doesn’t look like scaling the current way.
You’re right that I was thinking of a more online system that could update it’s weights during deployment. Yet even with frozen weights, I definitely expect the model to make plans involving things that were not involved. For example, it might not have a bio-weapon feature, but the relevant subfeature to build some by quite local rules that don’t look like a plan to build a bio-weapon.
That seems reasonable.