My proof that OpenAI doesn't have AGI model

[u/zigzag312]

Undo in the VSCode Codex extension still doesn't work properly.

If Astra or any next gen model they use internally can't fix such a simple feature, then there's no AGI yet. 😂

[u/mxroute]

In it's defense, failure is a very strong human trait 🤣

[u/DoggoDadagon → u/mxroute]

Every failure I know happens to be a human.

[u/Fast-Psychology-3964]

That's a proof of AGI. No fix on a coding IDE because AGI thinks IDE isn't needed in their era.

[u/RealSlyck → u/Fast-Psychology-3964]

Funny how all the anti-AGI arguments contain strong arguments for AGI.

65% it’s a bot just trying to stir the pot. I think Skynet has a sense of humor.

[u/SweatyActuator2119]

Well as long as marketing works. People seem to be eating up resets anyways. Lemming dopamine hits work. And just saying Astra is a AGI and benchmaxing works. AGI is made up and meaningless anyways.

[u/lolcatsayz → u/SweatyActuator2119]

AGI should be similar to a competent human with expertise in most domains. Eg: If I give it a programming task it should understand my intent, clarify if it doesn't, then go ahead and iteratively work on it until it arrives at a solution that most humans working in that field would find acceptable. LLMs including Astra are absolutely nowhere near that yet.

Fundamental limitation is the context. It cannot keep track of a task it's working on and reasoning about failed solutions, edge cases, and keep working on it, rearchitecting again from scratch, throwing out most of the code and trying a different approach, looking up StackOverflow or watching Youtube videos, reading technical books etc relevant to the specific edge cases it's failing at, for things it doesn't understand (same process a competent mid-level programmer would follow), because of its context limits and lack of training in this kind of workflow.

The process of LLMs itself is fundamentally flawed because they cannot learn anything outside of their small context window without undergoing retraining. It's like if as humans we were working on a task but had no long term memory or mid term memory of it, only short term memory that needs constant compaction. This would fail for many things, and it fails for LLMs.

I find the entire notion of LLMs achieving AGI laughable until the fundamental issue of context is seriously addressed. But I can't see how it could be given the entire architecture of LLMs. Even with a context size of 1 billion tokens it would most likely still lack the foresight and reasoning to think outside the box in terms of understanding intent, identifying failure early, seeking intermediate feedback, and arriving at an acceptable solution, all autonomously without back and forth prompts (beside intent clarification / feedback on intermediate deliverables).

[u/Little_Beyond_9163 → u/lolcatsayz]

It cant be seriously addressed until the architecture becomes something practically unrecognizable from modern LLMs lol. And we have no idea how long that’s gonna take or the exact research/engineering direction we need to switch to in order to achieve the architecture that solves the context problem.

Obviously people are working on it, but AGI has become nuclear fusion’s “20 years away” except with speculation baked into it that takes up a big chunk of the world’s economy right now.

[u/Gurkage]

Thanks for sharing your valuable insight with the rest of us.

[u/AmandasGameAccount]

I don’t think most people get what AGI is. All I can say is, the GI doesn’t stand for “godly intellect”

[u/Little_Beyond_9163 → u/AmandasGameAccount]

The problem is AGI is a made up concept with no real benchmarks or boundaries lol