Well, machine learning might have a ceiling. We just don’t know. Everything gets better, until it doesn’t.

Maybe machine learning can help us find other paths that succeed it’s limits. Or maybe it too hits roadblocks before finding the real AGI/ASI route.

There is a lot of hype right now. Some deserved, some perhaps a bit carried away.

At some point, LLM's did not work because we did not have the computing power for it. The alternative approaches will probably lead to AGI. The computing power just might not be here yet.

Even MS are speculating that LLM alone are not going to solve some of the problems they see with ChatGPT's ability to reason. ChatGPT has no ability to plan, or to solve problems that require a leap of logic. Or as they put it, the slow thinking process that overseas the fast thinking process. They have acknowledge solutions proposed by other authors that have recognised the same issue with LLM's have suggested a different architecture may be required. But this seemed to be the least fleshed out part of the paper.

Symbolic failed because it was difficult for people to come up with the theory of mind first and lay down the formats and the functions and the rules to create the base knowledge and logic. And from what was created (which did have a lot of use, so I wouldn't say it amounted to nothing) they couldn't find a way to make it scale, and so it couldn't learn much or independently. On top of that, they were probably limited by hardware too. Researchers focus on ML because it's comparatively 'easy' and because it has produced results that so far can scale. What I suspect they'll try doing with LLMs is learning how they work and building structure into them after the fact, and finding that their performance has degraded or can't be improved significantly. In my opinion, neurosymbolic will be the ideal way forward to achieve AGI and ASI, especially for safety reasons, and will take the best of both symbolic and ML, and together helping with the drawbacks to both.