Yea some state of the art results are just inaccessible without large budgets, simply because models can scale (and because some orgs have a lot of money to tra

Yea some state of the art results are just inaccessible without large budgets, simply because models can scale (and because some orgs have a lot of money to train those scaled models). You can always just use smaller models and/or lower resolutions though; of course the results won't be on par but it may reach a qualitative result (for research and experimentation purposes) or good enough result (for personal application purposes). E.g. hobbyists don't need AlphaGo-level go playing AI (which I'm sure had aggregate costs in 5 figures or more to train), reduced versions play all far above our levels -- although in this case there's the interesting effort of pooling hobbyist resources to indeed reach SOTA, see LeelaZero[1] and LCZero. Some kinds of research will be effective only at large orgs, that's always been true. There was indeed a brief period when people realized GPUs could unleash deep learning/CNNs that you could do anything with a good GPU, but that was very much an exception. To borrow from another field, you cannot do a level of car engine research without all infrastructure to fabricate and test engine prototypes (though you can do some/other kinds of theoretical analysis). [1] http://zero.sjeng.org/home http://zero.sjeng.org/home