I wrote my first MLP 25 years ago. After repeating some early experiments in machine learning from 20 ywars before that. One of the experiments I repeated was in text to speach. It was amazing to set up training runs and return after seveal hours to listen to my supercomputer babble like a toddler. I literally recall listening and being unable to distinguish the output from my NN from that of a real toddler, I happened to be teaching my neice to read around that same time. And when the NN had gained a large vocabulary such that it could fairly proficiently read aloud, I was convinced that I had found my PHD project and a path to AGI.

Further examination and discussion with more experienced researchers gave me pause. They said that one must have a solution, or a significant new approach toward solving the hard problems associated with a research project for it to be viable, otherwise time (and money) is wasted finding new ways to solve the easy problems.

This is a more general principle that can be applied to most areas of endeavour. When you set about research and development that involves a mix of easy, medium, and hard problems, you must solve the hard problems first otherwise you blow your budget finding new ways to solve the easy problems, which nobody cares about in science.

But "AI" has left the realm of science behind and entered the realm of capitalism where several years of meaningless intellectual gyration without ever solving a hard problem may be quite profitable.