Partly this will be because in fact current ML systems are not analogous to future AGI in some ways - probably if you tell the AGI that A is B, it will also know that B is A.

One oddity of LLMs is that we don't have a good way to tell the model that A is B in a way that it can remember. Prompts are not persistent, and as this paper shows, fine tuning doesn't do a good job of getting a fact into the model without doing a bunch of paraphrasing. Pretraining presumably works in a similar way.

This is weird! And I think helps make sense of some of the problems we see with current language models.

I think the argument against LeCun is simple: while it may be true that AIs won't necessarily have a dominance instinct the way that people do, they could try to dominate for other reasons: namely that such dominance is an instrumental goal towards whatever its objective is. And in fact that is a significant risk, and can't be discounted by pointing out that they may not have a natural instinct towards dominance.

I just think that to an economist, models and survey results are different things, and he's not asking for the latter.

I think that Tyler is thinking more of an economic type model that looks at the incentives of various actors and uses that to understand what might go wrong and why. I predict that he would look at this model and say, "misaligned AI can cause catastrophes" is the hand-wavy bit that he would like to see an actual model of.

I'm not an economist (is IANAE a known initialization yet?), but it would probably include things like the AI labs, the AIs, and potentially regulators or hackers/thieves, try to understand and model their incentives and behaviors, and see what comes out of that. It's less about subjective probabilities from experts and more about trying to understand the forces acting on the players and how they respond to them.

So... when can we get the optimal guide, if this isn't it? :)