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It's interesting that part of human value might be having our actions matter. But if you build an AI that can give you all the things, or even if you could've built such an AI but chose not to, then objectively your actions no longer matter much after that. I've no idea how even CEV could approach this problem.

Edit: I think I've figured it out. The AI shouldn't try to build the best world according to CEV, it should take the best action for an AI to take according to CEV. So if the AI notices that humans strongly prefer to be left alone with their problems, it'll just shut down. Or find some other way to ensure that humans can't rely on AIs for everything.

Yeah. It also worries me that a lot of value change has happened in the past, and much of it has been caused by selfish powerful actors for their ends rather than ours. The most obvious example is jingoism, which is often fanned up by power-hungry leaders. A more subtle example is valuing career or professional success. The part of it that isn't explained by money or the joy of the work itself, seems to be an irrational desire installed into us by employers.

Maybe one example is the idea of Dutch book. It comes originally from real world situations (sport betting and so on) and then we apply it to rationality in the abstract.

Or another example, much older, is how Socrates used analogy. It was one of his favorite tools I think. When talking about some confusing thing, he'd draw an analogy with something closer to experience. For example, "Is the nature of virtue different for men and for women?" - "Well, the nature of strength isn't that much different between men and women, likewise the nature of health, so maybe virtue works the same way." Obviously this way of reasoning can easily go wrong, but I think it's also pretty indicative of how people do philosophy.

I don't say it's not risky. The question is more, what's the difference between doing philosophy and other intellectual tasks.

Here's one way to look at it that just occurred to me. In domains with feedback, like science or just doing real world stuff in general, we learn some heuristics. Then we try to apply these heuristics to the stuff of our mind, and sometimes it works but more often it fails. And then doing good philosophy means having a good set of heuristics from outside of philosophy, and good instincts when to apply them or not. And some luck, in that some heuristics will happen to generalize to the stuff of our mind, but others won't.

If this is a true picture, then running far ahead with philosophy is just inherently risky. The further you step away from heuristics that have been tested in reality, and their area of applicability, the bigger your error will be.

Does this make sense?

I'm pretty much with you on this. But it's hard to find a workable attack on the problem.

One question though, do you think philosophical reasoning is very different from other intelligence tasks? If we keep stumbling into LLM type things which are competent at a surprisingly wide range of tasks, do you expect that they'll be worse at philosophy than at other tasks?

If the AI can rewrite its own code, it can replace itself with a no-op program, right? Or even if it can't, maybe it can choose/commit to do nothing. So this approach hinges on what counts as "shutdown" to the AI.

I think if AIs talk to each other using human language, they'll start encoding stuff into it that isn't apparent to a human reader, and this problem will get worse with more training.

It seems as a result of this post, many people are saying that LLMs simulate people and so on. But I'm not sure that's quite the right frame. It's natural if you experience LLMs through chat-like interfaces, but from playing with them in a more raw form, like the RWKV playground, I get a different impression. For example, if I write something that sounds like the start of a quote, it'll continue with what looks like a list of quotes from different people. Or if I write a short magazine article, it'll happily tack on a publication date and "All rights reserved". In other words it's less like a simulation of some reality or set of realities, and more like a really fuzzy and hallucinatory search engine over the space of texts.

It is of course surprising that a search engine over the space of texts is able to write poems, take derivatives, and play chess. And it's plausible that a stronger version of the same could outsmart us in more dangerous ways. I'm not trying to downplay the risk here. Just saying that, well, thinking in terms of the space of texts (and capabilities latent in it) feels to me more right than thinking about simulation.

Thinking further along this path, it may be that we don't need to think much about AI architecture or training methods. What matters is the space of texts - the training dataset - and any additional structure on it that we provide to the AI (valuation, metric, etc). Maybe the solution to alignment, if it exists, could be described in terms of dataset alone, without reference to the AI's architecture at all.

As far as I can tell, the answer is: don’t reward your AIs for taking bad actions.

I think there's a mistake here which kind of invalidates the whole post. If we don't reward our AI for taking bad actions within the training distribution, it's still very possible that in the future world, looking quite unlike the training distribution, the AI will be able to find such an action. Same as ice cream wasn't in evolution's training distribution for us, but then we found it anyway.

I really like how you've laid out a spectrum of AIs, from input-imitators to world-optimizers. At some point I had a hope that world-optimizer AIs would be too slow to train for the real world, and we'd live for awhile with input-imitator AIs that get more and more capable but still stay docile.

But the trouble is, I can think of plausible paths from input-imitator to world-optimizer. For example if you can make AI imitate a conversation between humans, then maybe you can make an AI that makes real world plans as fast as a committee of 10 smart humans conversing at 1000x speed. For extra fun, allow the imitated committee to send network packets and read responses; for extra extra fun, give them access to a workbench improving their own AI. I'd say this gets awfully close to a world-optimizer that could plausibly defeat the rest of humanity, if the imitator it's running on is good enough (GPT-6 or something). And there's of course no law saying it'll be friendly: you could prompt the inner humans with "you want to destroy real humanity" and watch the fireworks.

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