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thomasahle 2 days ago

> What the hell is general intelligence anyway?

OpenAI used to define it as "a highly autonomous system that outperforms humans at most economically valuable work."

Now they used a Level 1-5 scale: https://briansolis.com/2024/08/ainsights-openai-defines-five...

So we can say AGI is "AI that can do the work of Organizations":

> These “Organizations” can manage and execute all functions of a business, surpassing traditional human-based operations in terms of efficiency and productivity. This stage represents the pinnacle of AI development, where AI can autonomously run complex organizational structures.

TheOtherHobbes 2 days ago | parent | next [-]

There's nothing general about AI-as-CEO.

That's the opposite of generality. It may well be the opposite of intelligence.

An intelligent system/individual reliably and efficiently produces competent, desirable, novel outcomes in some domain, avoiding failures that are incompetent, non-novel, and self-harming.

Traditional computing is very good at this for a tiny range of problems. You get efficient, very fast, accurate, repeatable automation for a certain small set of operation types. You don't get invention or novelty.

AGI will scale this reliably across all domains - business, law, politics, the arts, philosophy, economics, all kinds of engineering, human relationships. And others. With novelty.

LLMs are clearly a long way from this. They're unreliable, they're not good at novelty, and a lot of what they do isn't desirable.

They're barely in sight of human levels of achievement - not a high bar.

The current state of LLMs tells us more about how little we expect from human intelligence than about what AGI could be capable of.

Thrymr 2 days ago | parent | prev | next [-]

Apparently OpenAI now just defines it monetarily as "when we can make $100 billion from it." [0]

[0] https://gizmodo.com/leaked-documents-show-openai-has-a-very-...

olyjohn 2 days ago | parent [-]

That's what "economically valuable work" means.

2 days ago | parent | prev [-]
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