The Nonlinear Library: LessWrong LW - My disagreements with "AGI ruin: A List of Lethalities" by Noosphere89
Sep 16, 2024
31:11
Link to original article
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: My disagreements with "AGI ruin: A List of Lethalities", published by Noosphere89 on September 16, 2024 on LessWrong.
This is going to probably be a long post, so do try to get a drink and a snack while reading this post.
This is an edited version of my own comment on the post below, and I formatted and edited the quotes and content in line with what @MondSemmel recommended:
My comment: https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/?commentId=Gcigdmuje4EacwirD
MondSemmel's comment: https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/?commentId=WcKi4RcjRstoFFvbf
The post I'm responding to: https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/
To start out my disagreement, I have this to talk about:
Response to Lethality 3
We need to get alignment right on the 'first critical try' at operating at a 'dangerous' level of intelligence, where unaligned operation at a dangerous level of intelligence kills everybody on Earth and then we don't get to try again.
I think this is actually wrong, because of synthetic data letting us control what the AI learns and what they value, and in particular we can place honeypots that are practically indistinguishable from the real world, such that if we detected an AI trying to deceive or gain power, the AI almost certainly doesn't know whether we tested it or whether it's in the the real world:
Because of this, given our theoretical complete control over the data input of these models, it will be possible to train them inside what is effectively a simulated world slightly different from our own. This will allow us to maximize their capabilities along axes that are safe and useful to us, while minimizing the risks of deception and take-over and being able to setup traps to detect when these are likely.
If the model is not trained on human data, then we can make its understanding of our world subtly different from the reality. If done carefully and cleverly, this will make it very easy to fool and be able to discover e.g. deceptiveness, or attempted takeover in a 'realistic' simulated world that the model is put inside during red-teaming.
While deceiving an adversary with direct access to your cognition is hard enough, doing so when you have been brought up in a world entirely controlled by that adversary, and which is feeding you subtly incorrect and misleading information about the adversary and their capabilities is likely orders of magnitude more difficult.
Jacob Cannell has argued along similar lines of training potential AGI systems in simulations of fantasy worlds, where takeover can be simulated but is far enough away that skills do not transfer. Fully synthetic datasets are necessary for this and indeed, a fantasy world is likely far from what we need.
Instead, we only need to simulate a world very close to our own but with slightly different affordances - maybe specific software vulnerabilities are simulated to exist but are actually patched in our world, maybe certain peptide-making companies accept all orders in the simulated world but have more checks in our world, maybe certain interpretability techniques are claimed not to work in the simulation, but do in fact work in our world - to cause any attempted takeover behaviour or adversarial
deception to appear extremely plausible to the AI but in fact be detectable and defendable in our reality.
In fact, we should design simulated worlds with 'honeytraps', which appear to be loopholes subtly planted in the AI's input data to encourage them to attempt to pursue their misaligned behaviour, but which we know of ahead of time and can monitor and catch.
It's the same reason for why we can't break out of the simulation IRL, except we don't have to face adversarial cognition, so the AI's task is even harder than our task.
See also this link:
https://www.beren.io/2024-05-11-Alignment-in-...
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: My disagreements with "AGI ruin: A List of Lethalities", published by Noosphere89 on September 16, 2024 on LessWrong.
This is going to probably be a long post, so do try to get a drink and a snack while reading this post.
This is an edited version of my own comment on the post below, and I formatted and edited the quotes and content in line with what @MondSemmel recommended:
My comment: https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/?commentId=Gcigdmuje4EacwirD
MondSemmel's comment: https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/?commentId=WcKi4RcjRstoFFvbf
The post I'm responding to: https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/
To start out my disagreement, I have this to talk about:
Response to Lethality 3
We need to get alignment right on the 'first critical try' at operating at a 'dangerous' level of intelligence, where unaligned operation at a dangerous level of intelligence kills everybody on Earth and then we don't get to try again.
I think this is actually wrong, because of synthetic data letting us control what the AI learns and what they value, and in particular we can place honeypots that are practically indistinguishable from the real world, such that if we detected an AI trying to deceive or gain power, the AI almost certainly doesn't know whether we tested it or whether it's in the the real world:
Because of this, given our theoretical complete control over the data input of these models, it will be possible to train them inside what is effectively a simulated world slightly different from our own. This will allow us to maximize their capabilities along axes that are safe and useful to us, while minimizing the risks of deception and take-over and being able to setup traps to detect when these are likely.
If the model is not trained on human data, then we can make its understanding of our world subtly different from the reality. If done carefully and cleverly, this will make it very easy to fool and be able to discover e.g. deceptiveness, or attempted takeover in a 'realistic' simulated world that the model is put inside during red-teaming.
While deceiving an adversary with direct access to your cognition is hard enough, doing so when you have been brought up in a world entirely controlled by that adversary, and which is feeding you subtly incorrect and misleading information about the adversary and their capabilities is likely orders of magnitude more difficult.
Jacob Cannell has argued along similar lines of training potential AGI systems in simulations of fantasy worlds, where takeover can be simulated but is far enough away that skills do not transfer. Fully synthetic datasets are necessary for this and indeed, a fantasy world is likely far from what we need.
Instead, we only need to simulate a world very close to our own but with slightly different affordances - maybe specific software vulnerabilities are simulated to exist but are actually patched in our world, maybe certain peptide-making companies accept all orders in the simulated world but have more checks in our world, maybe certain interpretability techniques are claimed not to work in the simulation, but do in fact work in our world - to cause any attempted takeover behaviour or adversarial
deception to appear extremely plausible to the AI but in fact be detectable and defendable in our reality.
In fact, we should design simulated worlds with 'honeytraps', which appear to be loopholes subtly planted in the AI's input data to encourage them to attempt to pursue their misaligned behaviour, but which we know of ahead of time and can monitor and catch.
It's the same reason for why we can't break out of the simulation IRL, except we don't have to face adversarial cognition, so the AI's task is even harder than our task.
See also this link:
https://www.beren.io/2024-05-11-Alignment-in-...