AGI is a recent term coined precisely because the name AI has become confusing, which is precisely my point. It is also not widely known and used for the large part by people who believe in a sci-fi rapture-like event called the "technological singularity[1]", and so carries a bit of a cultish connotation. It doesn't do much to allay the confusion, though, as the term AI (even if understood to refer to something weaker than AGI) can be confused to be a component of AGI. I imagine that at some point, something called AI may eventually lead to AGI, but we have absolutely no idea whether what we call AI now is even on the right path, so the confusion remains.

It is true that some algorithm has been called AI for decades. It wasn't always this one or anything similar to it. Both Lisp and Prolog were thought to be AI languages at one point.

[1]: https://en.wikipedia.org/wiki/Technological_singularity

My point is that the word "AI" has been referring to "weak AI" for decades. Everything from video game AI, expert systems in the 80's, genetic algorithms, searching algorithms, etc, have been called AI. There is no point arguing about the word "AI" now.

The word AGI is pretty established. There are AGI conferences. I've heard it used by a wide variety of people, not just singularitarians. "Strong AI" is another common term.

I don't think there is much doubt at this point that neural networks are on the right path for AI. They are extremely general, have made remarkable progress in widely different AI domains, and are the closest AI approach to the human brain.

> I don't think there is much doubt at this point that neural networks are on the right path for AI.

Not only is there doubt, I don't think any NN researcher would even dare to suggest (based on scientific knowledge; not as a mere conjecture) that neural networks, and certainly current NN algorithms, have anything to do with AGI, which, at this point, is still a dream or a sci-fi concept.

> They are extremely general, have made remarkable progress in widely different AI domains

They work precisely where statistical clustering works, because that's what they are. Statistical clustering is extremely effective.

> and are the closest AI approach to the human brain.

We don't know that. It is possible, even likely, that statistical learning plays some low-level role in the brain. We know little beyond that, but it is pretty certain that neurons in the brain work very differently from neural networks. I don't think anyone imagines that backpropagation is used by the brain.