Jagged AGI: o3, Gemini 2.5, and everything after

The capabilities of AI post gpt3 have become extraordinary and clearly in many cases superhuman.

However (as the article admits) there is still no general agreement of what AGI is, or how we (or even if we can) get there from here.

What there is is a growing and often naïve excitement that anticipates it as coming into view, and unfortunately that will be accompanied by the hype-merchants desperate to be first to "call it".

This article seems reasonable in some ways but unfortunately falls into the latter category with its title and sloganeering.

"AGI" in the title of any article should be seen as a cautionary flag. On HN - if anywhere - we need to be on the alert for this.

I think a reasonable definition of intelligence is the application of reason on knowledge. An example of a system that is highly knowledgeable but has little to no reason would be an encyclopedia. An example of a system that is highly reasonable, but has little knowledge would be a calculator. Intelligent systems demonstrate both.

Systems that have general intelligence are ones that are capable of applying reason to an unbounded domain of knowledge. Examples of such systems include: libraries, wikis, and forums like HN. These systems are not AGI, because the reasoning agents in each of these systems are organic (humans); they are more like a cyborg general intelligence.

Artificial general intelligence are just systems that are fully artificial (ie: computer programs) that can apply reason to an unbounded domain of knowledge. We're here, and we have been for years. AGI sets no minimum as to how great the reasoning must be, but it's obvious to anyone who has used modern generative intelligence systems like LLMs that the technology can be used to reason about an unbounded domain of knowledge.

If you don't want to take my word for it, maybe Peter Norvig can be more convincing: https://www.noemamag.com/artificial-general-intelligence-is-...

I think the thing missing would be memory. The knowledge of current models is more or less static save for whatever you can cram into their context window. I think if they had memory and thus the ability to learn - “oh hey, I’ve already tried to solve a bug in these ways maybe I won’t get stuck in loop on them!” Would be the agi push for me. Real time incorporating new knowledge into the model is the missing piece.