This is a model-independent way of proving if an AI system is honest.
I don’t see how this is a proof, it seems more like a heuristic. Perhaps you could spell out this argument more clearly?
Also, it is not clear to me how to use a timing attack in the context of a neural network, because in a standard feedforward network, all parameter settings will use the same amount of computation in a forward pass and hence run in the same amount of time. Do you have a specific architecture in mind, or are you just reasoning about arbitrary AGI systems? I think in the linked article above there are a couple ideas of how to vary the amount of time neural networks take :).
Thank you for the suggested link. That does seem like a good expansion on the same.
Yes, you are right, this argument clearly doesn’t hold for neural networks as they exist now. But this entire article was to come to this exact line of reasoning, hence the emphasis on model-independent approaches. A standard feedforward network with fixed parameters and weights without a mesa-optimizer cannot lie variably owing to different situations as exploited in timing attacks, nor does it have the abilities of an AGI. Rather than any arbitrary AGI system, I would say any model that has inner and outer objectives to align to will take different computation times. I am not talking about pseudo alignment here where the system performs well on fixed test cases, not on others, but in deceptive alignment where the system actually only pursues mesa-optimizers when not under threat, this leads to a change in computation time. Does that make things clearer now?
I thought of this as proof rather than a heuristic because the argument seemed rigorous to me unless you would like to emphasize why not. This stems from the logic explained above, for deceptive alignment, the mechanism of deception cannot be the same as for truth, if it is, the problem essentially reduces to pseudo alignment, and since both are not the same, there is a contradiction.
This seems interesting and connected to the idea of using a speed prior to combat deceptive alignment.
I don’t see how this is a proof, it seems more like a heuristic. Perhaps you could spell out this argument more clearly?
Also, it is not clear to me how to use a timing attack in the context of a neural network, because in a standard feedforward network, all parameter settings will use the same amount of computation in a forward pass and hence run in the same amount of time. Do you have a specific architecture in mind, or are you just reasoning about arbitrary AGI systems? I think in the linked article above there are a couple ideas of how to vary the amount of time neural networks take :).
Thank you for the suggested link. That does seem like a good expansion on the same.
Yes, you are right, this argument clearly doesn’t hold for neural networks as they exist now. But this entire article was to come to this exact line of reasoning, hence the emphasis on model-independent approaches. A standard feedforward network with fixed parameters and weights without a mesa-optimizer cannot lie variably owing to different situations as exploited in timing attacks, nor does it have the abilities of an AGI. Rather than any arbitrary AGI system, I would say any model that has inner and outer objectives to align to will take different computation times. I am not talking about pseudo alignment here where the system performs well on fixed test cases, not on others, but in deceptive alignment where the system actually only pursues mesa-optimizers when not under threat, this leads to a change in computation time. Does that make things clearer now?
I thought of this as proof rather than a heuristic because the argument seemed rigorous to me unless you would like to emphasize why not. This stems from the logic explained above, for deceptive alignment, the mechanism of deception cannot be the same as for truth, if it is, the problem essentially reduces to pseudo alignment, and since both are not the same, there is a contradiction.