Suppose we had a functionally infinite amount of high quality RL-/post-training environments, organized well by “difficulty,” and a functionally infinite amount of high quality data that could be used for pre-training (caveat: from what I understand, the distinction between these may be blurring.) Basically, we no longer needed to do research on discovering/creating new data, creating new RL environments, and we didn’t even have to do the work to label or organize it well (pre/post-training might have some path dependence).
In that case, what pace would one expect for model releases from AI labs in the short term to be? I ask because I see the argument made that AI could help speed up AI development in the near to medium term. But it seems like the main limiting factor is just the amount of time it takes to actually do the training runs.
If your answer is that we’ll have “continual learning” soon, then I have a followup question:
Using the latest hardware, but staying within the same basic architectures, what would one expect the maximum amount of intelligence possible that could be placed on a given N parameter model is? If God skipped the training process and tweaked all the weights individually to reach the global maximum, how smart could, say, GPT 4o be?
I'm sure that it is crammed to the brim in one sense, but strongly expect that 99.9% of what it's crammed to the brim with is essentially useless.
Also yes, I was including vision, audio, and motor control in that. It's hard to know exactly where the boundaries lie between facts like "a (dis)proof of the Riemann Hypothesis" and patterns of reasoning that could lead to a (dis)proof of the Riemann Hypothesis if required. I suspect that a lot of what is called "fluid" intelligence is actually pretty crystallized patterns of thought that can be used to generate other thoughts that lead somewhere useful - whether the entity using it is aware of that or not.