Re:Seven replies to the viral Apple reasoning paper – and wh...
by godelski on Saturday June 14, 2025 @11:48PM
(#44280393)
For comparison, the average person can't print Hello World in python. Your average programmer (probably) can.
It's surprisingly simple to be above average in most tasks. Which people often confuse with having expertise. It's probably pretty easy to get into the 80th percentile of most subjects. That won't make you the 80th percentile of people that do the thing, but most people don't. I'd wager 80th percentile is still amateur.
Re:Seven replies to the viral Apple reasoning paper – and wh...
by MoonGhost on Sunday June 15, 2025 @02:34AM
(#44280911)
> The average human is useless for pretty much everything but capable of learning to perform almost any task
But only the limited number of tasks per human.
> Or perhaps AGI should be able to reach the level of an experienced professional in any task.
Even if it performs just better than untrained human but on any task this will be superhuman level. As no human can do it.
Re:Seven replies to the viral Apple reasoning paper – and wh...
by jltsiren on Sunday June 15, 2025 @02:58AM
(#44280985)
The G in AGI stands for "general", not for "superhuman". An intelligence that can't learn to perform information processing and decision-making tasks people routinely do does not seem very general to me.
Re:Seven replies to the viral Apple reasoning paper – and wh...
by MoonGhost on Monday June 16, 2025 @01:48AM
(#44286914)
Here is the big question: should it be equal or better then every single person? If we assume that every healthy person is 'generally intelligent' then probably this is a benchmark. Because not every person can do the tasks that other persons do routinely. Probably we shouldn't demand it from AGI either. At least not from a single model. But it makes sense to request that specialized model can be created (or trained, fine tuned) for every task humans can do.
Or perhaps AGI should be able to reach the level of an experienced professional in any task. Maybe a single system can't be good at everything, if there are inherent trade-offs in learning to perform different tasks well.