> There's no reason to think AI will stop improving
No, and there’s no reason to think cars will stop improving either, but that doesn’t mean they will start flying.
The first error is in thinking that AI is converging towards a human brain. To treat this as a null hypothesis is incongruent both wrt the functional differences between the two and crucially empirical observations of the current trajectory of LLMs. We have seen rapid increases in ability, yes, but those abilities are very asymmetrical by domain. Pattern matching and shitposting? Absolutely crushing humans already. Novel conceptual ideas and consistency checked reasoning? Not so much, eg all that hype around PhD-level novel math problems died down as quickly as it had been manufactured. If they were converging on human brain function, why this vastly uneven ability increases?
The second error is to assume a superlinear ability improvement when the data has more or less run out and has to be slowly replenished over time, while avoiding the AI pollution in public sources. It’s like assuming oil will accelerate if it had run out and we needed to wait for more bio-matter to decompose for every new drop of crude. Can we improve engine design and make ICEs more efficient? Yes, but it’s a diminishing returns game. The scaling hypothesis was not exponential but sigmoid, which is in line with most paradigm shifts and novel discoveries.
> Making these sort of blanket assessments of AI, as if it were a singular, static phenomena is bad thinking.
I agree, but do you agree with yourself here? Ie:
> no reason to think that these tools won't vastly outperform us in the very near future
.. so back to single axis again? How is this different from saying calculators outperform humans?
Oh. Well that’s not super surprising. LinkedIn has the absolute worst posters. That’s like saying robots can dance better than humans but the humans in question only know how to do The Robot and forgot most of the steps
No, and there’s no reason to think cars will stop improving either, but that doesn’t mean they will start flying.
The first error is in thinking that AI is converging towards a human brain. To treat this as a null hypothesis is incongruent both wrt the functional differences between the two and crucially empirical observations of the current trajectory of LLMs. We have seen rapid increases in ability, yes, but those abilities are very asymmetrical by domain. Pattern matching and shitposting? Absolutely crushing humans already. Novel conceptual ideas and consistency checked reasoning? Not so much, eg all that hype around PhD-level novel math problems died down as quickly as it had been manufactured. If they were converging on human brain function, why this vastly uneven ability increases?
The second error is to assume a superlinear ability improvement when the data has more or less run out and has to be slowly replenished over time, while avoiding the AI pollution in public sources. It’s like assuming oil will accelerate if it had run out and we needed to wait for more bio-matter to decompose for every new drop of crude. Can we improve engine design and make ICEs more efficient? Yes, but it’s a diminishing returns game. The scaling hypothesis was not exponential but sigmoid, which is in line with most paradigm shifts and novel discoveries.
> Making these sort of blanket assessments of AI, as if it were a singular, static phenomena is bad thinking.
I agree, but do you agree with yourself here? Ie:
> no reason to think that these tools won't vastly outperform us in the very near future
.. so back to single axis again? How is this different from saying calculators outperform humans?