At the moment, AI improves rapidly simply because current algorithms yield significant improvements when increasing compute. It is often better to double the compute than work on improving the algorithm. However, compute prices will decrease less rapidly in the future. Then, AI will need better algorithms. If these can not be found as rapidly as compute helped in the past, AI will not grow on the same trajectory any more. Progress slows. Then, a second AI winter can happen.
I kinda disagree with this, especially the first sentence. “Increasing compute” is indeed one thing that is happening in AI, and it’s in the headlines a lot, but it’s not the only thing happening in AI. Algorithmic innovations are happening now and have been happening all along. Like, 3 years ago, the Transformer had just been invented … 5 years ago there was no BatchNorm or ResNets …. In the area I’m most interested in (neocortex-like models), the (IMO) most promising developments are in a very early research-project-ish stage, maybe like deep neural nets were in the 1990s, probably years away from progressing to parallelized, hardware-accelerated, turn-key code that can even begin to be massively scaled.
Agreed. Open AI did a study on the trends of algorithm efficiency. They found a 44x improvement in training efficiency on ImageNet over 7 years.