It feels to me like you are mixing up together a bunch of things when you talk about AI IQ. Like mixing having a model to extrapolate to predict future abilities, with the tendency of different abilities to correlate within models, with there being a slow takeoff. These seem like quite different things to me, and e.g. I’d strongly expect that there’d be a positive manifold of correlations for different language models. I once saw a paper claiming to prove it, but I didn’t like their test because a lot of the models they tested were just different finetunings of the same base models, which seems sketchy to me. But considering how you consistently get better performance when you have larger models and more data, it’s hard to see how you could not have a positive manifold. But even with a positive manifold among existing models, you are correct that this doesn’t mean we can necessarily predict the order in which new abilities will appear in the future.
It feels to me like you are mixing up together a bunch of things when you talk about AI IQ. Like mixing having a model to extrapolate to predict future abilities, with the tendency of different abilities to correlate within models, with there being a slow takeoff. These seem like quite different things to me, and e.g. I’d strongly expect that there’d be a positive manifold of correlations for different language models. I once saw a paper claiming to prove it, but I didn’t like their test because a lot of the models they tested were just different finetunings of the same base models, which seems sketchy to me. But considering how you consistently get better performance when you have larger models and more data, it’s hard to see how you could not have a positive manifold. But even with a positive manifold among existing models, you are correct that this doesn’t mean we can necessarily predict the order in which new abilities will appear in the future.