It's a shifting goalpost, but one of the things that struck me was how some questions could still be trivial for a fairly qualified human (a doctor in this case) but difficult for an AI model. Reasoning, visual or logic, is built on a set of assumptions that are better gained through IRL experience than crawling datasets and matching answers.
This leads me to believe that much of the future for training AI models will lie in exposing them to "meatspace" and annotating their inferences, much like how we train a child. This is a long, long process, and one that is already underway at scale. But it's what might give us emergent intelligences rather than just a basket of competing yet somehow-magic thesaurus.
https://www.happiesthealth.com/articles/future-of-health/hum...
It's a shifting goalpost, but one of the things that struck me was how some questions could still be trivial for a fairly qualified human (a doctor in this case) but difficult for an AI model. Reasoning, visual or logic, is built on a set of assumptions that are better gained through IRL experience than crawling datasets and matching answers.
This leads me to believe that much of the future for training AI models will lie in exposing them to "meatspace" and annotating their inferences, much like how we train a child. This is a long, long process, and one that is already underway at scale. But it's what might give us emergent intelligences rather than just a basket of competing yet somehow-magic thesaurus.