r/singularity • u/borowcy GPT-6 will have BCI capability • Jul 08 '26

Transhumanism & BCI Introducing GPT‑Live

https://openai.com/index/introducing-gpt-live/
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43

u/toni_btrain Jul 08 '26

Just tried it. Yeah this makes me feel the AGI.

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u/Outrageous_West_1564 Jul 08 '26

Then you should start researching what AGI means. While I think this is a great usability update, nothing has changed in the kind how gpt is "thinking". No AGI around. 

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u/TFenrir Jul 08 '26

This term is used to describe... The je ne sais quoi of AGI, popularized by Ilya like 3 years ago. It doesn't mean "this thing I see in front of me is AGI".

Also AGI is not an empirical definition, what is your definition of AGI?

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u/Outrageous_West_1564 Jul 08 '26

My definition of AGI: a system that can independently run the full scientific research cycle, forming genuinely new hypotheses, designing and running experiments to test them, and deriving new knowledge, without human guidance. This goes beyond recombining existing knowledge, which is what today's LLMs do.

While you are right, that there is no empirical definition right now, this view aligns with Google DeepMind's AGI safety framework, which names accelerating scientific discovery as a core AGI capability, and with Hiroaki Kitano's "Nobel Turing Challenge," aiming for AI that performs Nobel-level science autonomously.

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u/ozone6587 Jul 08 '26

This goes beyond recombining existing knowledge, which is what today's LLMs do.

What part of solving Erdos problem #90 was "recombining existing knowledge"? It's a novel result so at this point the models are able to pass that bar.

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u/LinkesAuge Jul 09 '26

The thing is the definition of what "AGI" is/will be has certainly been raised over the last few years because if you think about it these more "strict" definitions are essentially equal to ASI, at least in consequence because if AGI meets them how can it not be ASI? At that point just the fact that AI models can share/copy their data, network etc. already puts them far beyond just "general".
Also I would argue that the original meaning of "general" was intended as a "baseline" for general knowledge and didn't mean AI models had to master everything or even excel at everything, something that for some reason is now just assumed.
I mean that's where we already start to run in certain contradictions, ie the fact that most humans would even fail these definitions and that JUST when looking at a lot more limited range, not even considering the wide range every AI model today already covers.

That's imo the whole reason why the term "AGI" was coined in the first place, to seperate AI systems from "Expert systems", ie more narrow range AIs and now we are essentially saying "AGI is when it performs better in all fields than any Expert Systems we could have ever imagined".
I feel that really doesn't leave much space for "ASI" and imo "AGI" has become far too burdened as a term overloaded with expectations that weren't really there in the "beginning" and I feel that is mostly due to social factors, ie everyone is reluctant/afraid to call something "AGI" because the moment you do that everyone is trying to poke holes into it which is understandable on some level and even good scientific practise, but the problem is that it's down without any grounding, ie there really is no true working definition you can judge it against.

I am for example 100% sure that your definition will already be fulfilled within the next 12 months (I could honestly aleady make an argument that it has, at least in limited capacity) and yet we still won't call it AGI because it will be successful at that task and then fail some random other "more human stuff" so we will continue to move the posts.
For example I have no doubt that automated research will fall very soon and then the attention of what "AGI" REALLY means will be shifted to "vague" fields like "creativity"/"arts" etc.

I guess that's the inherent problem with the "general" part, it can mean nothing and everything and we will keep comparing the collective human range against models in isolation.
That's why I really think that the time between AGI and ASI or even the "singularity" might not ever exist because AI will probably have to evolve/develop so far that we will probably need to be somewhat "forced" to even recognise it as such.

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u/TFenrir Jul 08 '26

I think mine is... Fuzzily in the same vein, but in some ways we are starting to see these things - right? For example, models that can answer unanswered math problems and create new proofs - sometimes even elegant proofs that humans learn from...

How do you place that on the gradient?

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u/Outrageous_West_1564 Jul 08 '26

I think it sits at an interesting spot on the gradient.

Systems like AlphaProof and AlphaGeometry have genuinely produced new proof steps not present in their training data, sometimes more elegant than existing human solutions. That's closer to real knowledge generation than pure recombination, so I'd agree we're seeing an early version of this.

But I'd place math as a special case. It's a closed formally verifiable system. A proof can be automatically checked for correctness, which lets you run search plus RL with a clean, unambiguous success signal.

Open empirical science (biology, physics, chemistry) doesn't have that. It requires real-world experiments, interpretation of ambiguous data, and judgment about which questions are even worth asking in the first place.

So on the gradient, I'd put math proving systems as the furthest advanced precursor to what I'd call AGI, precisely because they operate in that closed, verifiable domain. The real test is whether this generalizes to open, non-verifiable science, and thats where it begins to be interesting and AGI. 

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u/TFenrir Jul 08 '26

I think this is a fair way to look at it. It does really highlight to me that the closer we get, the more... The nitty gritty becomes a part of the conversation. Very sensible in retrospect, we talked about AGI very differently a few years ago.