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Comment by opdahl | Hacker News Reader

Wouldn’t the real AGI test be that an AI would be able to do what the author did here and write this blog post?

I won't be surprised if GPT-5 would be able to do it: it knows that it's LLM, so it knows its limitations. It can write code to pre-process input in a format which is better understood, etc.

https://chatgpt.com/share/2fde1db5-00cf-404d-9ae5-192aa5ac90...

GPT-4 created a plan very similar to the article, i.e. it also suggested using Python to pre-process data. It also suggested using program synthesis. So I'd say it's already 90% there.

> "Execute the synthesized program on the test inputs."

> "Verify the outputs against the expected results. If the results are incorrect, iteratively refine the hypotheses and rules."

So people saying that it's ad-hoc are wrong. LLMs know how to solve these tasks, they are just not very good at coding, and iterative refinement tooling is in infancy.

Yep, but a float is more useful than a bool for tracking progress, especially if you want to answer questions like "how soon can we expect (drivers/customer support staff/programmers) to lose their jobs?"

Hard to find the right float but worth trying I think.

I agree, but it does seem a bit strange that you are allowed to "custom-fit" an AI program to solve a specific benchmark. Shouldn't there be some sort of rule that for something to be AGI it should work as "off-the-shelf" as possible?

If OpenAI had an embedded python interpreter or for that matter an interpreter for lambda calculus or some other equally universal Turing machine then this approach would work but there are no LLMs with embedded symbolic interpreters. LLMs currently are essentially probability distributions based on a training corpus and do not have any symbolic reasoning capabilities. There is no backtracking, for example, like in Prolog.

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