Indeed, and I’m glad we’ve converged on (2). But...
Do technologies that have lots of resources put into their development generally improve discontinuously or by huge slope changes?
… On second thoughts, how did we get there? The initial disagreement was how plausible it was for incremental changes to the LLM architecture to transform it into a qualitatively different type of architecture. It’s not about continuity-in-performance, it’s about continuity-in-design-space.
Whether finding an AGI-complete architecture would lead to a discontinuous advancement in capabilities, to FOOM/RSI/sharp left turn, is a completely different topic from how smoothly we should expect AI architectures’ designs to change. And on that topic, (a) I’m not very interested in reference-class comparisons as opposed to direct gears-level modeling of this specific problem, (b) this is a bottomless rabbit hole/long-standing disagreement which I’m not interested in going into at this time.
2 out of 3 of the technologies you listed probably have continuous improvement despite the lineage change
That’s an interesting general pattern, if it checks out. Any guesses why that might be the case?
My instinctive guess is the new-paradigm approaches tend to start out promising-in-theory, but initially very bad, people then tinker with prototypes, and the technology becomes commercially viable the moment it’s at least marginally better than the previous-paradigm SOTA. Which is why there’s an apparent performance-continuity despite a lineage/paradigm-discontinuity.
Indeed, and I’m glad we’ve converged on (2). But...
… On second thoughts, how did we get there? The initial disagreement was how plausible it was for incremental changes to the LLM architecture to transform it into a qualitatively different type of architecture. It’s not about continuity-in-performance, it’s about continuity-in-design-space.
Whether finding an AGI-complete architecture would lead to a discontinuous advancement in capabilities, to FOOM/RSI/sharp left turn, is a completely different topic from how smoothly we should expect AI architectures’ designs to change. And on that topic, (a) I’m not very interested in reference-class comparisons as opposed to direct gears-level modeling of this specific problem, (b) this is a bottomless rabbit hole/long-standing disagreement which I’m not interested in going into at this time.
That’s an interesting general pattern, if it checks out. Any guesses why that might be the case?
My instinctive guess is the new-paradigm approaches tend to start out promising-in-theory, but initially very bad, people then tinker with prototypes, and the technology becomes commercially viable the moment it’s at least marginally better than the previous-paradigm SOTA. Which is why there’s an apparent performance-continuity despite a lineage/paradigm-discontinuity.