371–380 of 496 posts
> Once AGI is declared by OpenAI ... I think it's funny and telling that they've used the word "declare" where what they are really doing is "claim". These guys think they are prophets.
MS: I just wanted you to know that you can't just say the word AGI and expect anything to happen.
OpenAI: I didn't say it. I declared it.
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They have a definition actually) “When AI generates $100 billion in profits” it will be considered an AGI. This term was defined in their previous partnership, not sure if it's still holds after the restructuring. https://techcrunch.com/2024/12/26/microsoft-and-openai-have-...
Which means that given enough time an LLM powered vending machine would be classified as AGI... interesting
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I don't think AGI will happen with LLMs. For example, can an LLM drive a car?? I know it's a silly question but it's a fact.
It can? If you use 'multimodal transformer' instead of LLM (which most SOTA models are), I don't think there's any reason why a transformer arch couldn't be trained to drive a car, in fact I'm sure that's what Tesla and co. are using in their cars right now. I'm sure self-driving will become good enough to be commercially viable in the next couple years (with some limitations), that doesn't mean it's AGI.
If someone wants to claim that, say, GPT-5 is AGI, then it is on them to connect GPT-5 to a car control system and inputs and show that it can drive a car decently well. After all, it has consumed all of the literature on driving and physics ever produced, plus untold numbers of hours of video of people driving.
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Why should LLM failures trump successes when determining if it thinks/understands? Yes, they have a lot of inhuman failure modes. But so what, they aren't human. Their training regimes are very dissimilar to ours and so we should expect alien failure modes owing to this. This doesn't strike me as good reason to think they don't understand anything in the face of examples that presumably demonstrate understanding.
Because there's no difference between a success and failure as far as an LLM is concerned. Nothing went wrong when the LLM produced a false statement. Nothing went right when the LLM produced a true statement. It produced a statement. The lexical structure of the statement is highly congruent with its training data and the previous statements.
>The lexical structure of the statement is highly congruent with its training data and the previous statements.
This doesn't accurately capture how LLMs work. LLMs have an ability to generalize that undermines the claim of their responses being "highly congruent with training data".
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A sufficiently large profit margin is what constitutes AGI? What a fucking joke.
“Only” means that it is a necessary condition, not a sufficient one.
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I don't think AGI will happen with LLMs. For example, can an LLM drive a car?? I know it's a silly question but it's a fact.
It can? If you use 'multimodal transformer' instead of LLM (which most SOTA models are), I don't think there's any reason why a transformer arch couldn't be trained to drive a car, in fact I'm sure that's what Tesla and co. are using in their cars right now. I'm sure self-driving will become good enough to be commercially viable in the next couple years (with some limitations), that doesn't mean it's AGI.
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Most people didn't think we were anywhere close to LLM's five years ago. The capabilities we have now were expected to be a decades away, depending on who you talked to. [EDIT: sorry, I should have said 10 years ago... recent years get too compressed in my head and stuff from 2020 still feels like it was 2 years ago!] So I think a lot of people now don't see what the path is to AGI, but also realize they hadn't seen…
Also possible we get something "close enough" to AGI and it's really fucking useful. AGI is the end-game. There's a lot of room between current LLMs and AGI.
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“Only” means that it is a necessary condition, not a sufficient one.
That's true, but the $100 billion requirement is the only hard qualification defined in earlier agreements. The rest of the condition was left to the "reasonable discretion" of the board of OpenAI. ( https://archive.is/tMJoG )
I want to setup a dev devcontainer where inside I can call ‘supabase start’ and use docker outside of docker. GPT5 was not helpful. AGI should be able to handle things not well expressed in the training data. We are a long ways away.
Was that with Codex iterating autonomously? Or a one shot.
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The "G" part of AGI implies it should be able to hit all the arbitrary yard sticks
That is stupid. It would be possible to be infinitely arbitrary to the point of “AGI” never being reachable by some yard sticks while still performing most viable labor.
Probable the biggest problem as others have stated is that we can’t really define intelligence more precisely than that it is something most humans have and all rocks don’t. So how could any definition for AGI be any more precise?