Resolution Criteria:

Today, most of the reason the next AI systems are better is because we spent more money training a larger model, and because we as humans have learned more about how to train better models. This question asks if the main reason why future AI systems will be better is because we used the previous generation of AI systems to help us build the next generation. For example: if we're using the prior generation to design better model architectures, write faster training code, or otherwise discover new ideas, this counts. If we're using the prior generation to curate training data, synthetically generate more data, or use some form or reinforcement learning, this counts. (In both cases, as long as the improvement from doing this is significant.) But if the main reason why the next generation is better is because we spent more money training them on faster chips, this doesn't count.

Motivation and Context:

Some processes lead to recursive improvement, others do not. For example: if you're going to build faster computer chips, it really helps to be able to use the previous generation of computer chips to build the next set. This gives you Moore's Law. Or, if you're trying to design high-precision machinery, you need "mostly high-precision machinery" to build the "more high-precision machinery". But for other processes this is not the case. If you're making dinner plates, then your ability to make dinner plates is not significantly improved by having a bunch of other dinner plates. It's constrained by your plate-making machines. Which will be the case for AI systems? Will they be primarily improved because we've spent more money and thrown more data at them? Or because we've used the prior generation of AI systems to help us build the next generation?

Question copied from: https://nicholas.carlini.com/writing/2024/forecasting-ai-future.html