>>neither the intended nor typical mode of operation Yup, certainly not intended, although I see it as the typical response on the edges of the data set; objec

>>neither the intended nor typical mode of operation Yup, certainly not intended, although I see it as the typical response on the edges of the data set; objects with too few varied representations will always fail in this way. Seems square-cubish as there will always be a volume of solid training data and a surface of partial data, so maybe not severe. >> deriving a proper understanding of mechanics in 3D from one million independent 2D ...extremely difficult at best Yup. This is definitely part of how it is different. Doing the full training set with stereographs would likely improve it, but it'd improve it even more to have the same images manipulated by robots and the feedback integrated. Considering the 3.5 billion parameters of DALL-E, 4.6B for Imagen and 890MM for Stable Diffusion, how many params would be needed to integrate stereo-vision and robotic feedback? 3.5billion squared or cubed? Would that be enough just scaled up, or do we need to qualitatively change the structure? >>I think it requires a different sort of training algorithm entirely. Agree 100%. I think these engines are a part of the solution, but not the whole. I expect we'll need multiple different kinds of training models, and then the methods to integrate them and correlate their 'knowledge'. E.g., figuring out how one part of a moderately complex object (e.g. a human) hides another part in certain positions (e.g., hand behind back) is trivial for a 3D modelling system, but even the massive 2d ones often get it wrong. >>being architected differently doesn't necessarily mean that the higher level functionality is any different Definitely true. Parallel evolution, elec vs ICE powered cars, etc. The question is when we've achieved the same level of functionality. >>how do you propose to distinguish something that's different from something that's incomplete?...achieved a meaningful form of abstraction versus merely averaging bits of the training set together? at this point I'm not actually clear where the line between those two things even lies YES, excellent question. Especially since these models don't do much explaining of their inner workings. Humans also haven't fully figured out our inner workings either. It's looking right now like different AI will arrive faster than biomimicry-based AI, partly because we still don't know the bio at a deep enough level. IDK if it'll stay this way. I remember discussions a long time ago with a scientist who worked on AI for early Mars missions, and how they'd move their machines. He was describing the algos for tracking the world, their machine, and adjusting motion, with the team assuming that they were re-creating the way humans do it. From my experience as an international level athlete and a neuroscience minor in college (inspired by my sport experiences), I could tell that his methods were nothing like how biological systems work. Seeing Google's self-driving car drive around a racetrack was truly impressive, but from my sportscar-racing training 7 experience, I could instantly tell that it was accomplishing the task nothing like any human would, although it was achieving competent levels of performance (in a limited setting). How do draw the line? It may come down to the kinds of clever tests built by childhood and animal behaviorists to study animals who can't self-report on their state or if they actually figure out something or not. That said, I don't think it's impossible for an AI to end up exceeding our capabilities by using different methods. Kind of like Paul Bunyan vs the chainsaw. (BTW, thanks for the lively discussion; it's a pleasure to be pushed to define my thoughts better, and I've learned; happy to keep it going)