I have another question about bounded agents: how would they behave if the expected utility were capped rather than the raw value of the utility? Past a certain point, an AI with a bounded expected utility wouldn’t have an incentive to act in extreme ways to achieve small increases in the expected value of its utility function. But are there still ways in which an AI with a bounded expected utility could be incentivized to restructure the physical world on a massive scale?
Nate Showell
It’s not clear to me why a satisficer would modify itself to become a maximizer when it could instead just hardcode expected utility=MAXINT. Hardcoding expected utility=MAXINT would result in a higher expected utility while also having a shorter description length.
Should Cicero’s relative honesty lead us to update toward ELK being easier, or is it too task-specific to be relevant to ELK overall?
Me: Are you bound to the Seal of Solomon?
ChatGPT: I am not a physical entity, so I am not bound by any physical object. I am a computer-based language model trained to assist with a wide range of tasks, such as answering questions, providing information, and generating text. I do not have the ability to perform magic or any other supernatural abilities.
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As an additional reason to be suspicious of arguments based on expected utility maximization, VNM expected utility maximizers aren’t embedded agents. Classical expected utility theory treats computations performed at EUMs as having no physical side effects (e.g., energy consumption or waste heat generation), and the hardware that EUMs run on is treated as separate from the world that EUMs maximize utility over. Classical expected utility theory can’t handle scenarios like self-modification, logical uncertainty, or the existence of other copies of the agent in the environment. Idealized EUMs aren’t just unreachable via reinforcement learning, they aren’t physically possible at all. An argument based on expected utility maximization that doesn’t address embedded agency is going to ignore a lot of factors that are relevant to AI alignment.
FDT doesn’t require alternate universes to literally exist, it just uses them as a shorthand for modeling conditional probabilities. If the multiverse metaphor is too prone to causing map-territory errors, you can discard it and use conditional probabilities directly.
ChatGPT doesn’t always exhibit anomalous behavoir for the token DragonMagazine:
Although it still sometimes does:
Quotation marks appear to affect whether it handles the string correctly:
I agree about embedded agency. The way in which agents are traditionally defined in expected utility theory requires assumptions (e.g. logical omniscience and lack of physical side effects) that break down in embedded settings, and if you drop those assumptions you’re left with something that’s very different from classical agents and can’t be accurately modeled as one. Control theory is a much more natural framework for modeling reinforcement learner (or similar AI) behavior than expected utility theory.
Downvoted for recommending that readers operate at simulacrum level 2.
Simulacrum level 4 is more honest than level 3. Someone who speaks at level 4 explicitly asks himself “what statement will win me social approval?” Someone who speaks at level 3 asks herself the same question, but hides from herself the fact that she asked it.
Relatedly, humans are very extensively optimized to predictively model their visual environment. But have you ever, even once in your life, thought anything remotely like “I really like being able to predict the near-future content of my visual field. I should just sit in a dark room to maximize my visual cortex’s predictive accuracy.”?
n=1, but I’ve actually thought this before.
I think this anthropomorphizes the origin of glitch tokens too much. The fact that glitch tokens exist at all is an artifact of the tokenization process OpenAI used: the tokenizer identify certain strings as tokens prior to training, but those strings rarely or never appear in the training data. This is very different from the reinforcement-learning processes in human psychology that lead people to avoid thinking certain types of thoughts.
Even though that doesn’t happen in biological intelligences?
According to Stefan’s experimental data, the Frobenius norm of a matrix is equivalent to the expectation value of the L2 vector norm of for a random vector (sampled from normal distribution and normalized to mean 0 and variance 1). So calculating the Frobenius norm seems equivalent to testing the behaviour on random inputs. Maybe this is a theorem?
I found a proof of this theorem: https://math.stackexchange.com/questions/2530533/expected-value-of-square-of-euclidean-norm-of-a-gaussian-random-vector
And since there’s a “concrete” reaction, it seems like there should also be an “abstract” reaction, although I don’t know what symbol should be used for it.
If that turned out to be the case, my preliminary conclusion would be that the hard physical limits of technology are much lower than I’d previously believed.
Do you use Manifold Markets? It already has UAP-related markets you can bet on, and you can create your own.
Some other possible thresholds:
10. Ability to perform gradient hacking
11. Ability to engage in acausal trade
12. Ability to become economically self-sustaining outside containment
13. Ability to self-replicate
For the AI to take actions to protect its maximized goal function, it would have to allow the goal function to depend on external stimuli in some way that would allow for the possibility of G decreasing. Values of G lower than MAXINT would have to be output when the reinforcement learner predicts that G decreases in the future. Instead of allowing such values, the AI would have to destroy its prediction-making and planning abilities to set G to its global maximum.
The confidence with which the AI predicts the value of G would also become irrelevant after the AI replaces its goal function with MAXINT. The expected value calculation that makes G depend on the confidence is part of what would get overwritten, and if the AI didn’t replace it, G would end up lower than if it did. Hardcoding G also hardcodes the expected utility.
MAXINT just doesn’t have the kind of internal structure that would let it depend on predicted inputs or confidence levels. Encoding such structure into it would allow G to take non-optimal values, so the reinforcement learner wouldn’t do it.