> single graphics processing unit (GPU) and four days of training time.
Maybe there is a reason they had this restriction? I can only think of allowing the winning AI to be ready for end users whic h generally have a single GPU?
The resources devoted to training don't really translate to the resources needed to run the trained model. A model trained on a thousand GPUS for a week might still run in real time on a single GPU once trained.
The restricted training resources are just part of the challenge. They point out that a human child can learn the necessary steps in minutes by watching someone else do it, so they wanted to see if anyone could make a computer learn it with relatively limited resources.
A level playing field is good for a challenge like this. Those that could solve it without these constraints are pushed to improve their existing approach. Those that don’t normally compete in these kind of challenges aren’t put off by a potential competitor having way more resources.
Imposing constraints forces people to be more creative in their approach. This seems like it was explicitly a goal of the challenge, to find more clever ways to solve problems without just relying on more data.
They're just biasing competitors towards efficient solutions.
Maybe there is a reason they had this restriction? I can only think of allowing the winning AI to be ready for end users whic h generally have a single GPU?