[voidspark]

Technically it is a refinement, as it distinguishes levels of performance.

The General Intelligence part of AGI refers to its ability to solve problems that it was not explicitly trained to solve, across many problem domains. We already have examples of the current systems doing exactly that - zero shot and few shot capabilities.

> We have no evidence of this.

That's my point. Humans are not "autocompleting words" when they speak.

[JumpCrisscross]

> Technically it is a refinement, as it distinguishes levels of performance

No, it's bringing something out of scope into the definition. Gluten-free means free of gluten. Gluten-free bagel verus sliced bread is a refinement--both started out under the definition. Glutinous bread, on the other hand, is not gluten free. As a result, "almost gluten free" is bullshit.

> That's my point. Humans are not "autocompleting words" when they speak

Humans are not. LLMs are. It turns out that's incredibly powerful! But it's also limiting in a way that's fundamentally important to the definition of AGI.

LLMs bring us closer to AGI in the way the inventions of writing, computers and the internet probably have. Calling LLMs "emerging AGI" pretends we are on a path to AGI in a way we have zero evidence for.

[voidspark]

> Gluten-free means free of gluten.

Bad analogy. That's a binary classification. AGI systems can have degrees of performance and capability.

> Humans are not. LLMs are.

My point is that if you oversimplify LLMs to "word autocompletion" then you can make the same argument for humans. It's such an oversimplification of the transformer / deep learning architecture that it becomes meaningless.

[JumpCrisscross]

> That's a binary classification. AGI systems can have degrees of performance and capability

The "g" in AGI requires the AI be able to perform "the full spectrum of cognitively demanding tasks with proficiency comparable to, or surpassing, that of humans" [1]. Full and not full are binary.

> if you oversimplify LLMs to "word autocompletion" then you can make the same argument for humans

No, you can't, unless you're pre-supposing that LLMs work like human minds. Calling LLMs "emerging AGI" pre-supposes that LLMs are the path to AGI. We simply have no evidence for that, no matter how much OpenAI and Google would like to pretend it's true.

[1] https://en.wikipedia.org/wiki/Artificial_general_intelligenc...

[famouswaffles]

Why are you linking a Wikipedia page like it's the ground zero for the term? Especially when neither article the page link to justify that definition see the term as a binary accomplishment.

The g in AGI is General. I don't what world you think Generality isn't a spectrum, but it's sure as hell isn't this one.

[voidspark]

That's right, and the Wikipedia page refers to the classification system:

"A framework for classifying AGI by performance and autonomy was proposed in 2023 by Google DeepMind researchers. They define five performance levels of AGI: emerging, competent, expert, virtuoso, and superhuman"

In the second paragraph:

"Some researchers argue that state‑of‑the‑art large language models already exhibit early signs of AGI‑level capability, while others maintain that genuine AGI has not yet been achieved."

The entire article makes it clear that the definitions and classifications are still being debated and refined by researchers.

[voidspark]

Then you are simply rejecting any attempts to refine the definition of AGI. I already linked to the Google DeepMind paper. The definition is being debated in the AI research community. I already explained that definition is too limited because it doesn't capture all of the intermediate stages. That definition may be the end goal, but obviously there will be stages in between.

> No, you can't, unless you're pre-supposing that LLMs work like human minds.

You are missing the point. If you reduce LLMs to "word autocompletion" then you completely ignore the the attention mechanism and conceptual internal representations. These systems have deep learning models with hundreds of layers and trillions of weights. If you completely ignore all of that, then by the same reasoning (completely ignoring the complexity of the human brain) we can just say that people are auto-completing words when they speak.

[JumpCrisscross]

> I already linked to the Google DeepMind paper. The definition is being debated in the AI research community

Sure, Google wants to redefine AGI so it looks like things that aren’t AGI can be branded as such. That definition is, correctly in my opinion, being called out as bullshit.

> obviously there will be stages in between

We don’t know what the stages are. Folks in the 80s were similarly selling their expert systems as a stage to AGI. “Emerging AGI” is a bullshit term.

> If you reduce LLMs to "word autocompletion" then you completely ignore the the attention mechanism and conceptual internal representations. These systems have deep learning models with hundreds of layers and trillions of weights

Fair enough, granted.

[voidspark]

> Sure, Google wants to redefine AGI

It is not a redefinition. It's a classification for AGI systems. It's a refinement.

Other researchers are also trying to classify AGI systems. It's not just Google. Also, there is no universally agreed definition of AGI.

> We don’t know what the stages are. Folks in the 80s were similarly selling their expert systems as a stage to AGI. “Emerging AGI” is a bullshit term.

Generalization is a formal concept in machine learning. There can be degrees of generalized learning performance. This is actually measurable. We can compare the performance of different systems.