If you don’t like AI systems doing tasks that humans can’t evaluate, I think you should be concerned about the fact that people keep building larger models and fine-tuning them in ways that elicit intelligent behavior.
Indeed, I think accelerating progress in scaling up language models is likely net negative (given our current level of preparedness) and will become more clearly net negative over time as risks grow. I’m very excited about efforts to monitor and build consensus about these risks, or to convince or pressure AI labs to slow down development as further scaling becomes more risky.
But I think something has gone quite wrong if our collective strategy ends up being “We keep training smarter systems, but fortunately we are only able to fine-tune them to do tasks with external feedback signals from the world.” You’re going to get increasingly smart systems that can predict and manipulate observable features of the world; this is just asking for a catastrophic failure from reward hacking or deceptive alignment.
Even the world where you keep building larger and larger language models, but avoid ever training them to act in the world, seems like a recipe for trouble. It creates an unstable situation where anyone who fine-tunes a model can cause a catastrophe, or where a tiny behavioral quirk of a model could lead it to take over the world.
If we want to avoid AI systems taking over the world then I think we should strongly prefer to do it by stopping the creation of systems smart enough to do so. That seems like a much better place for norms.
I think this entire picture becomes even more compelling if you are mostly worried about deceptive alignment. I think training bigger models seems like the overwhelmingly dominant risk factor, and making models of a fixed size more useful seems like an extremely plausible intervention for reducing the risk of deceptive alignment.
Perhaps the big difference is that I think it matters a lot whether AI systems are smart enough to be an AI CEO; and not so much whether people are actually trying to employ their AI as a CEO. The risk comes from a deceptive aligned AI (or reward-hacking AI) having that level of competence. If you are in that unfortunate world, then you probably do unfortunately want a bunch of aligned AIs doing complex stuff to help you survive.
If there is an attempt to build consensus in the ML community around “Hey you can train language models just try not to have them do complicated stuff” then I wouldn’t push back on it. But I think saying “Don’t try to align AI systems that do complex tasks because it interferes with norms against using AIs to do complex tasks” is actively counterproductive. And I think this is a less reasonable ask than “just stop building such big language models,” and so I’ll argue against it being the main ask the safety community focuses on.
I understand your point of view and think it is reasonable.
However, I don’t think “don’t build bigger models” and “don’t train models to do complicated things” need to be at odds with each other. I see the argument you are making, but I think success on these asks are likely highly correlated via the underlying causal factor of humanity being concerned enough about AI x-risk and coordinated enough to ensure responsible AI development.
I also think the training procedure matters a lot (and you seem to be suggesting otherwise?), since if you don’t do RL or other training schemes that seem designed to induce agentyness and you don’t do tasks that use an agentic supervision signal, then you probably don’t get agents for a long time (if ever).
if you don’t do RL or other training schemes that seem designed to induce agentyness and you don’t do tasks that use an agentic supervision signal, then you probably don’t get agents for a long time
Is this really the case? If you imagine a perfect Oracle AI, which is certainly not agenty, it seems to me that with some simple scaffolding, one could construct a highly agentic system. It would go something along the lines of
Setup API access to ‘things’ which can interact with the real world.
Ask the oracle ‘What would be the optimal action if you want to do <insert-goal> via <insert-api-functions>?’
Do the actions that are outputted.
Some kind of looping mechanism to gain feedback from the world and account for it.
This is my line of reasoning why AIS matters for language models in general.
I meant “other training schemes” to encompass things like scaffolding that deliberately engineers agents using LLMs as components, although I acknowledge they are not literally “training” and more like “engineering”.
The thing that we care about is how long it takes to get to agents. If we put lots of effort making powerful Oracle systems or other non-agentic systems, we must assume that agentic systems will follow shortly. Someone will make them, even if you do not.
If you don’t like AI systems doing tasks that humans can’t evaluate, I think you should be concerned about the fact that people keep building larger models and fine-tuning them in ways that elicit intelligent behavior.
Indeed, I think accelerating progress in scaling up language models is likely net negative (given our current level of preparedness) and will become more clearly net negative over time as risks grow. I’m very excited about efforts to monitor and build consensus about these risks, or to convince or pressure AI labs to slow down development as further scaling becomes more risky.
But I think something has gone quite wrong if our collective strategy ends up being “We keep training smarter systems, but fortunately we are only able to fine-tune them to do tasks with external feedback signals from the world.” You’re going to get increasingly smart systems that can predict and manipulate observable features of the world; this is just asking for a catastrophic failure from reward hacking or deceptive alignment.
Even the world where you keep building larger and larger language models, but avoid ever training them to act in the world, seems like a recipe for trouble. It creates an unstable situation where anyone who fine-tunes a model can cause a catastrophe, or where a tiny behavioral quirk of a model could lead it to take over the world.
If we want to avoid AI systems taking over the world then I think we should strongly prefer to do it by stopping the creation of systems smart enough to do so. That seems like a much better place for norms.
I think this entire picture becomes even more compelling if you are mostly worried about deceptive alignment. I think training bigger models seems like the overwhelmingly dominant risk factor, and making models of a fixed size more useful seems like an extremely plausible intervention for reducing the risk of deceptive alignment.
Perhaps the big difference is that I think it matters a lot whether AI systems are smart enough to be an AI CEO; and not so much whether people are actually trying to employ their AI as a CEO. The risk comes from a deceptive aligned AI (or reward-hacking AI) having that level of competence. If you are in that unfortunate world, then you probably do unfortunately want a bunch of aligned AIs doing complex stuff to help you survive.
If there is an attempt to build consensus in the ML community around “Hey you can train language models just try not to have them do complicated stuff” then I wouldn’t push back on it. But I think saying “Don’t try to align AI systems that do complex tasks because it interferes with norms against using AIs to do complex tasks” is actively counterproductive. And I think this is a less reasonable ask than “just stop building such big language models,” and so I’ll argue against it being the main ask the safety community focuses on.
I understand your point of view and think it is reasonable.
However, I don’t think “don’t build bigger models” and “don’t train models to do complicated things” need to be at odds with each other. I see the argument you are making, but I think success on these asks are likely highly correlated via the underlying causal factor of humanity being concerned enough about AI x-risk and coordinated enough to ensure responsible AI development.
I also think the training procedure matters a lot (and you seem to be suggesting otherwise?), since if you don’t do RL or other training schemes that seem designed to induce agentyness and you don’t do tasks that use an agentic supervision signal, then you probably don’t get agents for a long time (if ever).
Is this really the case? If you imagine a perfect Oracle AI, which is certainly not agenty, it seems to me that with some simple scaffolding, one could construct a highly agentic system. It would go something along the lines of
Setup API access to ‘things’ which can interact with the real world.
Ask the oracle ‘What would be the optimal action if you want to do <insert-goal> via <insert-api-functions>?’
Do the actions that are outputted.
Some kind of looping mechanism to gain feedback from the world and account for it.
This is my line of reasoning why AIS matters for language models in general.
I meant “other training schemes” to encompass things like scaffolding that deliberately engineers agents using LLMs as components, although I acknowledge they are not literally “training” and more like “engineering”.
The thing that we care about is how long it takes to get to agents. If we put lots of effort making powerful Oracle systems or other non-agentic systems, we must assume that agentic systems will follow shortly. Someone will make them, even if you do not.
I don’t disagree… in this case you don’t get agents for a long time; someone else does though.