Most breakthroughs have historically been made by university researchers, then put into use by corporations. Arguably, including most of the latest developments,. But university researchers were never going to get access to the $100 million in compute time to train something like GPT-4, lol.
The human brain has 100 trillion connections. GPT-4 has 1.76 trillion parameters (which are analogous to connections). It took 25k GPUs to train, so in theory, I guess it could be possible to train a human-like intelligence using 1.4 million GPUs. Transformers (the T in GPT) are not like human brains though. They “learn” once, then do not learn or add “memories” while they’re being used. They can’t really do things like planning either. There are algorithms for “lifelong learning” and planning, but I don’t think they scale to such large models, datasets, or real-world environments. I think there needs to be a lot theoretical breakthroughs to make AGI possible, and I’m not sure if more money will help that much. I suppose AGI could be achieved by trial and error (i.e. trying ideas and testing if they work without mathematically proving if or how well they’d work) instead of rigorous theoretical work.