Hm. I’m not sure if Scott Aaronson has any weird views on AI in particular, but if he’s basically mainstream-oriented we could potentially ask him to briefly skim the Tiling Agents paper and say if it’s roughly the sort of paper that it’s reasonable for an organization like MIRI to be working on if they want to get some work started on FAI.
Yes, I would welcome his perspective on this.
I feel again like you’re trying to interpret the paper according to a different purpose from what it has. Like, I suspect that if you described what you thought a promising AGI research agenda was supposed to deliver on what sort of timescale, I’d say, “This paper isn’t supposed to do that.”
I think I’ve understood your past comments on this point. My questions are about the implicit assumptions upon which the value of the research rests, rather than about what the research does or doesn’t succeed in arguing.
This part is clearer and I think I may have a better idea of where you’re coming from, i.e., you really do think the entire field of AI hasn’t come any closer to AGI, in which case it’s much less surprising that you don’t think the Tiling Agents paper is the very first paper ever to come closer to AGI. But this sounds like a conversation that someone else could have with you, because it’s not MIRI-specific or FAI-specific.
As I said in earlier comments, the case for the value of the research hinges on its potential relevance to AI safety, which in turn hinges on how good the model is for the sort of AI that will actually be built. Here I don’t mean “Is the model exactly right?” — I recognize that you’re not claiming it to be — the question is whether the model is in the right ballpark.
A case for the model being a good one requires pointing to a potentially promising AGI research program to which the model is relevant. This is the point that I feel hasn’t been addressed.
Some things that I see as analogous to the situation under discussion are:
A child psychology researcher who’s never interacted with children could write about good child rearing practices without the research being at all relevant to how to raise children well.
An economist who hasn’t looked at real world data about politics could study political dynamics using mathematical models without the researcher being at all relevant to politics in practice.
A philosopher who hasn’t study math could write the philosophy of math without the writing being relevant to math.
A therapist who’s never had experience with depression could give advice to a patient on overcoming depression without the advice being at all relevant to overcoming depression.
Similarly, somebody without knowledge of the type of AI that’s going to be built could research AI safety without the research being relevant to AI safety.
Does this help clarify where I’m coming from?
I also feel somewhat at a loss for where to proceed if I can’t say “But just look at the ideas behind Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, that’s obviously important conceptual progress because...” In other words, you see AI doing a bunch of things, we already mostly agree on what these sorts of surface real-world capabilities are, but after checking with some friends you’ve concluded that this doesn’t mean we’re less confused about AGI then we were in 1955. I don’t see how I can realistically address that except by persuading your authorities; I don’t see what kind of conversation we could have about that directly without being able to talk about specific AI things.
I’m open to learning object level material if I learn new information that convinces me that there’s a reasonable chance that MIRI’s FAI research is relevant to AI safety in practice.
Meanwhile, if you specify “I’m not convinced that MIRI’s paper has a good chance of being relevant to FAI, but only for the same reasons I’m not convinced any other AI work done in the last 60 years is relevant to FAI” then this will make it clear to everyone where you’re coming from on this issue.
Yes, I would welcome his perspective on this.
I think I’ve understood your past comments on this point. My questions are about the implicit assumptions upon which the value of the research rests, rather than about what the research does or doesn’t succeed in arguing.
As I said in earlier comments, the case for the value of the research hinges on its potential relevance to AI safety, which in turn hinges on how good the model is for the sort of AI that will actually be built. Here I don’t mean “Is the model exactly right?” — I recognize that you’re not claiming it to be — the question is whether the model is in the right ballpark.
A case for the model being a good one requires pointing to a potentially promising AGI research program to which the model is relevant. This is the point that I feel hasn’t been addressed.
Some things that I see as analogous to the situation under discussion are:
A child psychology researcher who’s never interacted with children could write about good child rearing practices without the research being at all relevant to how to raise children well.
An economist who hasn’t looked at real world data about politics could study political dynamics using mathematical models without the researcher being at all relevant to politics in practice.
A philosopher who hasn’t study math could write the philosophy of math without the writing being relevant to math.
A therapist who’s never had experience with depression could give advice to a patient on overcoming depression without the advice being at all relevant to overcoming depression.
Similarly, somebody without knowledge of the type of AI that’s going to be built could research AI safety without the research being relevant to AI safety.
Does this help clarify where I’m coming from?
I’m open to learning object level material if I learn new information that convinces me that there’s a reasonable chance that MIRI’s FAI research is relevant to AI safety in practice.
Yes, this is where I’m coming from.