I don't think AGI is right around the corner

They keep giving me incorrect answers to verifiable questions. They clearly don't 'seek' anything.

Most on HN are tech people and it is tiring to see they did not just spend a sunday morning doing a Karpathy llm implementation or so. Somehow, like believing in a deity, even smart folk seem to think 'there is more'. Stop. Go to youtube or whatever and watch a video of practically implementing a gpt like thing, and code along. It takes very little time and your hallucinations about agi with these models shall be exorcized.

I don't know if you are indirectly referring to me, but I have done such an implementation, and those particular LLMs are very limited. Two things come to mind.

1. It is still correct that the limited "truth-seeking" that I expressed holds. With respect to the limited world model possessed by the limited training and limited dataset, such a model "seeks to understand" the approximate concept that I am imperfectly expressing that it has data for, and then generate responses based in that.

2. SotA models have access to external data, be it web search or RAG+vector database, etc.. They also have access to the Chain of Thought method. They are trained on datasets that enable them to exploit these tools, and will exploit these tools. The zero-to-hero sequence does not lead you to build such an LLM, and the one that you build has a very limited computational graph. So with respect to more... traditional notions of "truth seeking", these LLMs fundamentally lack the equipment to do that that SotA models have.

In the sense that I expressed, has it not already then sought out an accurate meaning that you have asked? And then failed to give a satisfactory answer? I would also ask: is said model an advertised "reasoning" model? Also, does it have access to external facts via a tool like web search? I would not expect great ability to "arrive at truth" under certain limitations.

Now, you can't conclude that "they clearly don't 'seek' anything" just by the fact that they got an answer wrong. To use the broad notion of "seeking" like you do, a truth seeker with limited knowledge and equipment would arrive confidently at incorrect conclusions based on accurate reasoning. For example, without modern lenses to detect stellar parallax, one would confidently conclude that the stars in the sky are a different thing than the sun (and planets), since one travels across the sky, but the stars are fixed. Plato indeed thought so, and nobody would accuse him of not being a truth-seeker.

If this is what you had in mind, I hope that I have addressed it, otherwise I hope that you can communicate what you mean with an example.

I spent an hour on thrusday trying to get some code that would convert one data structure to another in terraform's HCL (which I only deal with once every few years and I find it's looping and eccentricities very annoying).

I opened my 'conversation' with a very clearly presented 'problem statement'. Given this datastructure (with code and an example with data) convert it to this datastructure (with code and the same example data transformed) in terraform.

I went through seven rounds of it presenting me either code that was not syntactically correct or produced a totally different datastructure. Every time it apologized for getting it wrong and then coming back with yet another wrong answer.

I stopped having the conversation when my junior who I also presented the problem to came back with a proper answer.

I'm not talking about it trying to prove to me that trump actually won the 2020 election or that vaccines don't cause autism or anything. Just actual 2+2=4 answers. Much like, in another reply to this post, the guy who had it try to find all the states that have w in their name.