> Entrants were only allowed to use a single graphics processing unit (GPU) and four days of training time.
Alphastar SCII bot has been using much more resources and time than this to train, so maybe there is one of the reasons no entrant has achieved the goal yet.
> A relatively small Minecraft dataset, with 60 million frames of recorded human player data, was also made available to entrants to train their systems.
It will be interesting to see if these artificial and somewhat arbitrary constraints (although I get that the idea is to restrain it to resources that are somewhat realistically available to a single individual without organizational backing today) will either cripple this challenge or in the end yield some innovative results because the entrants will have to devise algorithms that use much less data and resources than what has been traditionally required to get SOTA results.
Additionally it is also unclear whether the way humans learn to play this game is actually using a smaller or a much bigger dataset to learn from. Sure, a human can learn to play it in 20 minutes, but that's after 9-10 years of other pretraining of seing, understanding and operating in the 3D physical world performing various tasks, getting compressed knowledge from other people by watching and listening to them... Maybe that would be an interesting challenge - to still constrain the final model to 1 GPU for 1 day, but at least allow the model to pretrain on arbitrary similar data, if it is not sourced directly from minecraft or any clones.
> Alphastar SCII bot has been using much more resources and time than this to train, so maybe there is one of the reasons no entrant has achieved the goal yet.
It's probably worthwhile to note that AlphaStar is trying to become as skilled as the strongest human players, whereas in this case it's more of a binary "is capable of getting diamonds" thing, they don't need to be world-class diamond miners.
> 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.
I like the constraints they put. But realistically, if you want to mimick the way humans learn, you want to put in place some kind of transfer learning: You have a model that has already been trained to understand video frames, maybe has played a few video games before. Then, 60M frames of minetest can be used to understand things about the game.
But also, the machine learning algorithms we have nowadays are in many ways superhuman. Seeing how much can be learned with these constraints is interesting as well.
Alphastar SCII bot has been using much more resources and time than this to train, so maybe there is one of the reasons no entrant has achieved the goal yet.
> A relatively small Minecraft dataset, with 60 million frames of recorded human player data, was also made available to entrants to train their systems.
It will be interesting to see if these artificial and somewhat arbitrary constraints (although I get that the idea is to restrain it to resources that are somewhat realistically available to a single individual without organizational backing today) will either cripple this challenge or in the end yield some innovative results because the entrants will have to devise algorithms that use much less data and resources than what has been traditionally required to get SOTA results.
Additionally it is also unclear whether the way humans learn to play this game is actually using a smaller or a much bigger dataset to learn from. Sure, a human can learn to play it in 20 minutes, but that's after 9-10 years of other pretraining of seing, understanding and operating in the 3D physical world performing various tasks, getting compressed knowledge from other people by watching and listening to them... Maybe that would be an interesting challenge - to still constrain the final model to 1 GPU for 1 day, but at least allow the model to pretrain on arbitrary similar data, if it is not sourced directly from minecraft or any clones.