I think the more capable AI systems are, the more we’ll see patterns like “Every time you ask an AI to do something, it does it well; the less you put yourself in the loop and the fewer constraints you impose, the better and/or faster it goes; and you ~never see downsides.” (You never SEE them, which doesn’t mean they don’t happen.)
This, again, seems unlikely to me.
For most things that people seem likely to use AI for in the foreseeable future, I expect downsides and failure modes will be easy to notice. If self-driving cars are crashing or going to the wrong destination, or if AI-generated code is causing the company’s website to crash or apps to malfunction, people would notice those.
Even if someone has an AI that he or she just hooks it up to the internet and give it the task “make money for me”, it should be easy to build in some automatic record-keeping module that keeps track of what actions the AI took and where the money came from. And even if the user does not care if the money is stolen, I would expect the person or bank that was robbed to notice and ask law enforcement to investigate where the money went.
Can you give an example of some type of task for which you would expect people to frequently use AI, and where there would reliably be downside to the AI performing the task that everyone would simply fail to notice for months or years?
I think this kind of thing is common among humans. Employees might appear to be accomplishing the objectives they were given, with distortions hard to notice (and sometimes noticed, sometimes not) - e.g., programmers cutting corners and leaving a company with problems in the code that don’t get discovered until later (if ever). People in government may appear to be loyal to the person in power, while plotting a coup, with the plot not noticed until it’s too late. I think the key question here is whether AIs might get situational awareness and other abilities comparable to those of humans.
Those 2 types of downsides, creating code with a bug versus plotting a takeover, seem importantly different.
I can easily see how an LLM-based app fine-tuned with RLHF might generate the first type of problem. For example, let’s say some GPT-based app is trained using this method to generate the code for websites in response to prompts describing how the website should look and what features it should have. And lets suppose during training it generates many examples that have some unnoticed error—maybe it does not render properly on certain size screens, but the evaluators all have normal-sized screens where that problem does not show up.
If the evaluators rated many websites with this bug favorably, then I would not be surprised if the trained model continued to generate code with the same bug after it was deployed.
But I would not expect the model to internally distinguish between “the humans rated those examples favorably because they did not notice the rendering problem” versus “the humans liked the entire code including the weird rendering on larger screens”. I would not expect it to internally represent concepts like “if some users with large screens notice and complain about the rendering problem after deployment, Open AI might train a new model and rate those websites negatively instead” or to care about whether this would eventually happen or to take any precautions against the rendering issue being discovered.
By contrast, the coup-plotting problem is more similar to the classic AI takeover scenario. And that does seem to require the type of foresight and situational awareness to distinguish between “the leadership lets me continue working in the government because they don’t know I am planning a coup” versus “the leadership likes the fact that I am planning to overthrow them”, and to take precautions against your plans being discovered while you can still be shut down.
I don’t think n AI system gets the later type of ability just as an accidental side effect of reinforcement learning with human feedback (at least not for the AI systems we have now). The development team would need to do a lot of extra work to give an AI that foresightful planning ability, and ability to understand the decision system of a potential adversary enough to predict which information it needs to keep secret for its plans to succeed. And if a development team is giving its AI those abilities (and exercising any reasonable degree of caution) then I would expect them to build in safeguards: have hard constraints on what it is able to do, ensure its plans are inspectable, etc.
Is the disagreement here about whether AIs are likely to develop things like situational awareness, foresightful planning ability, and understanding of adversaries’ decisions as they are used for more and more challenging tasks?
I think this piece represents my POV on this pretty well, especially the bits starting around here.
Is the disagreement here about whether AIs are likely to develop things like situational awareness, foresightful planning ability, and understanding of adversaries’ decisions as they are used for more and more challenging tasks?
My thought on this is, if a baseline AI system does not have situational awareness before the AI researchers started fine-tuning it, I would not expect it to obtain situational awareness through reinforcement learning with human feedback.
I am not sure I can answer this for the hypothetical “Alex” system in the linked post, since I don’t think I have a good mental model of how such a system would work or what kind of training data or training protocol you would need to have to create such a thing.
If I saw something that, from the outside, appeared to exhibit the full range of abilities Alex is described as having (including advancing R&D in multiple disparate domains in ways that are not simple extrapolations of its training data) I would assign a significantly higher probability to that system having situational awareness than I do to current systems. If someone had a system that was empirically that powerful, which had been trained largely by reinforcement learning, I would say the responsible thing to do would be:
Keep it air-gapped rather than unleashing large numbers of copies of it onto the internet
Carefully vet any machine blueprints, drugs or other medical interventions, or other plans or technologies the system comes up with (perhaps first building a prototype to gather data on it in an isolated controlled setting where it can be quickly destroyed) to ensure safety before deploying them out into the world.
The 2nd of those would have the downside that beneficial ideas and inventions produced by the system take longer to get rolled out and have a positive effect. But it would be worth it in that context to reduce the risk of some large unforeseen downside.
I think that as people push AIs to do more and more ambitious things, it will become more and more likely that situational awareness comes along with this, for reasons broadly along the lines of those I linked to (it will be useful to train the AI to have situational awareness and/or other properties tightly linked to it).
I think this could happen via RL fine-tuning, but I also think it’s a mistake to fixate too much on today’s dominant methods—if today’s methods can’t produce situational awareness, they probably can’t produce as much value as possible, and people will probably move beyond them.
The “responsible things to do” you list seem reasonable, but expensive, and perhaps skipped over in an environment where there’s intense competition, things are moving quickly, and the risks aren’t obvious (because situationally aware AIs are deliberately hiding a lot of the evidence of risk).
Did everyone actually fail to notice, for months, that social media algorithms would sometimes recommend extremist content/disinformation/conspiracy theories/etc (assuming that this is the downside you are referring to)?
It seems to me that some people must have realized this as soon as they starting seeing Alex Jones videos showing up in their YouTube recommendations.
This, again, seems unlikely to me.
For most things that people seem likely to use AI for in the foreseeable future, I expect downsides and failure modes will be easy to notice. If self-driving cars are crashing or going to the wrong destination, or if AI-generated code is causing the company’s website to crash or apps to malfunction, people would notice those.
Even if someone has an AI that he or she just hooks it up to the internet and give it the task “make money for me”, it should be easy to build in some automatic record-keeping module that keeps track of what actions the AI took and where the money came from. And even if the user does not care if the money is stolen, I would expect the person or bank that was robbed to notice and ask law enforcement to investigate where the money went.
Can you give an example of some type of task for which you would expect people to frequently use AI, and where there would reliably be downside to the AI performing the task that everyone would simply fail to notice for months or years?
I think this kind of thing is common among humans. Employees might appear to be accomplishing the objectives they were given, with distortions hard to notice (and sometimes noticed, sometimes not) - e.g., programmers cutting corners and leaving a company with problems in the code that don’t get discovered until later (if ever). People in government may appear to be loyal to the person in power, while plotting a coup, with the plot not noticed until it’s too late. I think the key question here is whether AIs might get situational awareness and other abilities comparable to those of humans.
Those 2 types of downsides, creating code with a bug versus plotting a takeover, seem importantly different.
I can easily see how an LLM-based app fine-tuned with RLHF might generate the first type of problem. For example, let’s say some GPT-based app is trained using this method to generate the code for websites in response to prompts describing how the website should look and what features it should have. And lets suppose during training it generates many examples that have some unnoticed error—maybe it does not render properly on certain size screens, but the evaluators all have normal-sized screens where that problem does not show up.
If the evaluators rated many websites with this bug favorably, then I would not be surprised if the trained model continued to generate code with the same bug after it was deployed.
But I would not expect the model to internally distinguish between “the humans rated those examples favorably because they did not notice the rendering problem” versus “the humans liked the entire code including the weird rendering on larger screens”. I would not expect it to internally represent concepts like “if some users with large screens notice and complain about the rendering problem after deployment, Open AI might train a new model and rate those websites negatively instead” or to care about whether this would eventually happen or to take any precautions against the rendering issue being discovered.
By contrast, the coup-plotting problem is more similar to the classic AI takeover scenario. And that does seem to require the type of foresight and situational awareness to distinguish between “the leadership lets me continue working in the government because they don’t know I am planning a coup” versus “the leadership likes the fact that I am planning to overthrow them”, and to take precautions against your plans being discovered while you can still be shut down.
I don’t think n AI system gets the later type of ability just as an accidental side effect of reinforcement learning with human feedback (at least not for the AI systems we have now). The development team would need to do a lot of extra work to give an AI that foresightful planning ability, and ability to understand the decision system of a potential adversary enough to predict which information it needs to keep secret for its plans to succeed. And if a development team is giving its AI those abilities (and exercising any reasonable degree of caution) then I would expect them to build in safeguards: have hard constraints on what it is able to do, ensure its plans are inspectable, etc.
Is the disagreement here about whether AIs are likely to develop things like situational awareness, foresightful planning ability, and understanding of adversaries’ decisions as they are used for more and more challenging tasks?
I think this piece represents my POV on this pretty well, especially the bits starting around here.
My thought on this is, if a baseline AI system does not have situational awareness before the AI researchers started fine-tuning it, I would not expect it to obtain situational awareness through reinforcement learning with human feedback.
I am not sure I can answer this for the hypothetical “Alex” system in the linked post, since I don’t think I have a good mental model of how such a system would work or what kind of training data or training protocol you would need to have to create such a thing.
If I saw something that, from the outside, appeared to exhibit the full range of abilities Alex is described as having (including advancing R&D in multiple disparate domains in ways that are not simple extrapolations of its training data) I would assign a significantly higher probability to that system having situational awareness than I do to current systems. If someone had a system that was empirically that powerful, which had been trained largely by reinforcement learning, I would say the responsible thing to do would be:
Keep it air-gapped rather than unleashing large numbers of copies of it onto the internet
Carefully vet any machine blueprints, drugs or other medical interventions, or other plans or technologies the system comes up with (perhaps first building a prototype to gather data on it in an isolated controlled setting where it can be quickly destroyed) to ensure safety before deploying them out into the world.
The 2nd of those would have the downside that beneficial ideas and inventions produced by the system take longer to get rolled out and have a positive effect. But it would be worth it in that context to reduce the risk of some large unforeseen downside.
I think that as people push AIs to do more and more ambitious things, it will become more and more likely that situational awareness comes along with this, for reasons broadly along the lines of those I linked to (it will be useful to train the AI to have situational awareness and/or other properties tightly linked to it).
I think this could happen via RL fine-tuning, but I also think it’s a mistake to fixate too much on today’s dominant methods—if today’s methods can’t produce situational awareness, they probably can’t produce as much value as possible, and people will probably move beyond them.
The “responsible things to do” you list seem reasonable, but expensive, and perhaps skipped over in an environment where there’s intense competition, things are moving quickly, and the risks aren’t obvious (because situationally aware AIs are deliberately hiding a lot of the evidence of risk).
Social media algorithms.
Did everyone actually fail to notice, for months, that social media algorithms would sometimes recommend extremist content/disinformation/conspiracy theories/etc (assuming that this is the downside you are referring to)?
It seems to me that some people must have realized this as soon as they starting seeing Alex Jones videos showing up in their YouTube recommendations.