Achieving AGI will be more than just passing all benchmarks, it has to account for the unknown problems too.

metalliqaz 7 hours ago | parent | next [-]

Unless they have something in the labs that massively departs from their current products, AGI isn't on the table and is purely hype for marketing purposes.

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cyanydeez 7 hours ago | parent | prev | next [-]

they should be consulting Donald Rumsfeld and make sure they implement the Unknown-Unknowns benchmark, because thats how they get you

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naikrovek 7 hours ago | parent | prev | next [-]

AGI is a long way off. Unless you’re talking about some unknown-to-me LLM marketing BS which is called “AGI” or something, I guess. Artificial general purpose intelligence is so different to LLMs or image AI that they are completely incomparable, except to say that they are all artificial. AGI will do a lot more than token prediction.

	▲	ACCount37 6 hours ago \| parent \| next [-]
		What's your evidence of that? That AGI requires a truly novel architecture, and not just another iterative "LLM but with an extra trinket and wheels that spin ten times faster".
	▲	theLiminator 5 hours ago \| parent \| prev [-]
		Please define AGI first.

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minimaxir 7 hours ago | parent | prev [-]

This ties into the bias-variance tradeoff (https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff) common with building non-LLM models. The solutions can only be a) figure out how to get LLMs smaller with similar performance so they don't memorize things/game the benchmarks and b) build benchmarks that are indeed comprehensive for all real-world data, which is infeasible.

	▲	sigbottle 7 hours ago \| parent [-]
		I mean, people always say there are tradeoffs, until you reach the next frontier, in which there are tradeoffs at said frontier, and the next, and the next, etc. In one sense, yes, tradeoffs are inescapable as the scope expands to the maximal possible scope. In another sense... it depends on the level of abstraction we're talking about.