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Comment by saganus | Hacker News Reader

What does AGI look like in your opinion?

Personally, I've used LLMs to debug hard-to-track code issues and AWS issues among other things.

Regardless of whether that was done via next-token prediction or not, it definitely looked like AGI, or at least very close to it.

Is it infallible? Not by a long shot. I always have to double-check everything, but at least it gave me solid starting points to figure out said issues.

It would've taken me probably weeks to find out without LLMd instead of the 1 or 2 hours it did.

In that context, I have a hard time thinking how would a "real" AGI system look like, that it's not the current one.

Not saying current LLMs are unequivocally AGI, but they are darn close for sure IMO.

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> What does AGI look like in your opinion?

Being able to actually reason about things without exabytes of training data would be one thing. Hell, even with exabytes of training data, doing actual reasoning for novel things that aren't just regurgitating things from Github would be cool.

Being able to learn new things would be another. LLMs don't learn; they're a pretrained model (it's in the name of GPT), that send in inputs and get an output. RAGs are cool but they're not really "learning", they're just eating a bit more context in order to kind of give a facsimile of learning.

Going to the extreme of what you're saying, then `grep` would be "darn close to AGI". If I couldn't grep through logs, it might have taken me years to go through and find my errors or understand a problem.

I think that they're ultimately very neat, but ultimately pretty straightforward input-output functions.

Why should implementation matter at all? You should be able to classify a black box as AGI or not.

Well, I guess you lose artificial if there’s a human brain hidden in the box.

If we had AGI we wouldn't need to keep spending more and more money to train these models, they could just solve arbitrary problems through logic and deduction like any human. Instead, the only way to make them good at something is to encode millions of examples into text or find some other technique to tune them automatically (e.g. verifiable reward modeling of with computer systems).

Why is it that LLMs could ace nearly every written test known to man, but need specialized training in order to do things like reliably type commands into a terminal or competently navigate a computer? A truly intelligent system should be able to 0-shot those types of tasks, or in the absolute worst case 1-shot them.

To add to this, previously one could argue that LLMs were on par with somewhat less intelligent humans and it was (at least I found) difficult to dispute. But now the frontier models can custom tailor explanations of technical subjects in the advanced undergraduate to graduate range. Simultaneously, I regularly catch them making what for a human of that level would be considered very odd errors in reasoning. When questioned about these inconsistencies they either display a hopeless lack of awareness or appear to attempt to deflect. They're also entirely incapable of learning from such an interaction. It feels like interacting with an empty vessel that presents an illusion of intelligence and produces genuinely useful output yet there's nothing behind the curtain so to speak.

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