| It’s always been nebulous but that was fine because we were so incredibly far away from it. We never really planned to be close to it figuring out the edge. Some have it at human or beyond for all tasks, but then rarely touch on “one human” or “all humans”. Personally having been in AI since before deep nets, systems have been incredibly narrow for decades. Classifiers on images were battling with ten classes in 2010. Imagenet had 1k classes and people were getting half of the things wrong then and that was frankly amazing at the time. And they only did images, only to known classes, only with very specific inputs. Text classifiers only did a few classes usually and mostly threw all the words together. The most advanced things I saw in the late 2000s were struggling so much to make general systems that the most general ones were still incredibly limited and bad at those things (we had a robot learning to play games that you
showed it). Things like asking a thing for a book and having it parse the sentence, identify what was needed, that it didn’t know where it was but that was knowledge another human had and asking them - that was impressive yet also limited to very small sets of interactions. The idea of a machine getting sarcasm, even if mostly built for it, was wild. General meant capable of a broad range of tasks without retraining. To me we have agi. It’s general, and it’s good enough to be useful. |