[mannykannot]

It is interesting that you can write thirteen posts on the topic without being able to define it.

It also seems very odd that you can differentiate between some things that you think are intelligent, and some things that you think definitely are not, yet you are incapable of extracting any sort of goal from that knowledge.

If you could tell us your criteria, perhaps we could help you with that...

[timid_oshima]

I’m simply very curious about the subject, it’s super important :)! Given that, I’m also frustrated with what seems like a popular lack of critical thought and curiosity on the specifics.

In these comments, when I’ve talked about an intelligence I can distinguish, I’ve been talking about human / animal intelligence. AGI implies an intelligence independent of that, so I’m asking about the specifics there - what are we calling intelligence if not “what humans do”?

If we are calling it just that, then I’d argue everything I know about how these models do things is very different from what I know of how humans approach the specific tasks the models are built against. And I’ve read that that’s intentional. So, even with that sort of definition I don’t see how it follows that these approaches are on any linear path to AGI (maybe nonlinear if we learn limits and such from mistakes).

I’ve since read more of the article (it’s long, huh?) I like the framework they use from Roitblat in Section 2 - and again, don’t see how LLMs and such are on the road to fulfilling those criteria.

[mannykannot]

Fair enough, though I feel you are a bit too eager to push back against ideas that go counter to your initial thoughts. Of course, because I hold differing opinions, you could reasonably object that it is just what I would say!

I have a different idea of what AGI means: in my view, it is a retronym created in the 1980s in order to refer to AI of the sort Turing envisioned (which was more or less "what humans do") and differentiate it from things that were then being called AI, such as IBM's Deep Blue, which were mostly brute force applied to conceptually narrow problems.

You mentioned Roitblat's framework, and I would draw your attention to one aspect of it: it is not just a list of things that humans do, but those things which humans do considerably better than other animals, yet for all of them, there are other species that do them to some extent. As an evolutionist, I suppose there was a relatively recent time in the past when some of our ancestors or sibling species (all now extinct) had some or all of these skills to some intermediate level. In this view, intelligence is not an all-or-nothing concept, and achieving some of it is still progress.

Here's a view which you may not have seen: the pace of progress in AGI has not been constrained by an inability to define what we want, but by the pace at which we see ways to make what we see we need. For example, it is clear that current LLMs have a problem with truth, but it is not clear from what has been made public so far that anyone has a solution. Some people think that what's being done now with LLMs, but more of it, will be enough to get us to what will be generally accepted as AGI; I am skeptical, but I am willing to be persuaded otherwise if the evidence warrants it.