> Powerful pattern matching is still just pattern matching.

Higher order pattern matching is Turing complete. Transformers are Turing complete. Memory augmented LLMs are Turing complete. Neural networks can learn to reproduce any function. These have all been proven.

So if computers can be intelligent and can solve novel problems in principle, then LLMs can too if given the right training. If you don't think computers can be intelligent, you have a much higher burden to meet.

> Human level reasoning includes ability to learn, so that people can solve novel problems, overcome failures by trial and error, exploration, etc.

You keep bringing this up as if it's lacking, but basically all existing LLM interfaces provide facilities for memory to store state. Storing progress just isn't an issue if the LLM has the right training. HN has some recent articles about Claude code just being given the task to port some GitHub repos to other programming languages, and they woke up the next morning and it did it autonomously, using issue tracking, progress reports, PRs the hole nine yards. This is frankly not the hard part IMO.

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HarHarVeryFunny 5 days ago | parent [-]

Being Turing machine complete means that the system in question can emulate a Turing machine, which you could then program to do anything since it's a universal computer. So sure, if you know how to code up an AGI to run on a Turing machine you would be good to go on any Turing machine!

I'm not sure why you want to run a Turing machine emulator on an LLM, when you could just write a massively faster one to run on the computer your LLM is running on, cutting out the middle man, but whatever floats your boat I suppose.

Heck, if you really like emulation and super slow speed then how about implementing Conway's game of Life to run on your LLM Turing machine emulator, and since Life is also Turing complete you could run another Turing machine emulator on that (it's been done), and finally run your AGI on top of that! Woo hoo!

I do think you'll have a challenge prompting your LLM to emulate a Turing machine (they are really not very good at that sort of thing), especially since the prompt/context will also have to do double duty as the Turing machines (infinite length) tape, but no doubt you'll figure it out.

Keep us posted.

I'll be excited to see your AGI program when you write that bit.

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naasking 4 days ago | parent [-]

The point has nothing to do with speed, but with expressive power / what is achievable and learnable, in principle. Again, if you accept that a computer can in principle run a program that qualifies as AGI, then all I'm saying is that an LLM with memory augmentation can in principle be trained to do this as well because their computation power is formally equivalent.

And coincidentally, a new paper being discussed on HN is a good example addressing your concern about existing models learning and developing novel things. Here's a GPT model that learned physics just by training on a data:

https://arxiv.org/abs/2509.13805

	▲	HarHarVeryFunny 4 days ago \| parent [-]
		You seem to want to say that because an LLM is Turing complete (a doubtful claim) it should be able to implement AGI, which would be a logical conclusion, but yet totally irrelevant. If the only thing missing to implement AGI was a Turing machine to run it on, then we'd already have AGI running on Conway's game of Life, or perhaps on a Google supercomputer. > Here's a GPT model that learned physics just by training on a data It didn't learn at run-time. It was PRE-trained, using SGD on the entire training set, the way that GPT's (Generative PRE-trained Transformers) always are. In order to learn at run-time, or better yet get rid of the distinction between pre-training and run-time, requires someone to invent (or copy from nature) a new incremental learning algorithm that: a) Doesn't require retraining on everything it was ever previously trained on, and b) Doesn't cause it to forget, or inappropriately change, things it had previously learnt These are easier said than done, which is why we're a decade or so into the "deep learning" revolution, and nothing much has changed other than fine-tuning which is still a bulk data technique.