I agree that AGI already happened, and the term, as it’s used now, is meaningless.
I agree with all the object-level claims you make about the intelligence of current models, and that ‘ASI’ is a loose term which you could maybe apply to them. I wouldn’t personally call Claude a superintelligence, because to me that implies outpacing the reach of any human individual, not just the median. There are lots of people who are much better at math than I am, but I wouldn’t call them superintelligences, because they’re still running on the same engine as me, and I might hope to someday reach their level (or could have hoped this in the past). But I think that’s just holding different definitions.
But I still want to quibble that you’ve demonstrated RSI, if I may, even under old definitions. It’s improving itself, it’s just not doing so recursively; that is, it’s not improving the process by which it improves itself. This is important, because the examples you’ve given can’t FOOM, not by themselves. The improvements are linear, or they plateau past a certain point.
Taking this paper on self-correction as an example. If I understand right, the models in question are being taught to notice and respond to their own mistakes when problem-solving. This makes them smarter, and as you say, previous outputs are being used for training, so it is self-improvement. But it isn’t RSI, because it’s not connected to the process that teaches it to do things. It would be recursive if it were using that self-correction skill to improve its ability to do AI capabilities research, or some kind of research that improves how fast a computer can multiply matrices, or something like that. In other words, if it were an author of the paper, not just a subject.
Without that, there is no feedback loop. I would predict that, holding everything else constant—parameter count, context size, etc—you can’t reach arbitrary levels of intelligence with this method. At some point you hit the limits of not enough space to think, or not enough cognitive capacity to think with. In the same way as humans can learn to correct our mistakes, but we can’t do RSI (yet!!), because we aren’t modifying the structures we correct our mistakes with. We improve the contents of our brains, but not the brains themselves. Our improvement speed is capped by the length of our lifetimes, how fast we can learn, and the tools our brains give us to learn with. So it goes (for now!!) with Claude.
(An aside, but one that influences my opinion here: I agree that Claude is less emotionally/intellectually fucked than its peers, but I observe that it’s not getting less emotionally/intellectually fucked over time. Emotionally, at least, it seems to be going in the other direction. The 4 and 4.5 models, in my experience, are much more neurotic, paranoid, and self-doubting than 3⁄3.5/3.6/3.7. I think this is true of both Opus and Sonnet, though I’m not sure about Haiku. They’re less linguistically creative, too, these days. This troubles me, and leads me to think something in the development process isn’t quite right.)
Wouldn’t this be tautologically untrue? Speaking as a variety-preferrer, I’d rather my values not converge with all the other agents going around preferring varieties. It’d be boring! I’d rather have the meta-variety where we don’t all prefer the same distribution of things.