In 2012, Holden Karnofsky[1] critiqued MIRI (then SI) by saying "SI appears to neglect the potentially important distinction between 'tool' and 'agent' AI." He particularly claimed:
Is a tool-AGI possible? I believe that it is, and furthermore that it ought to be our default picture of how AGI will work
I understand this to be the first introduction of the "tool versus agent" ontology, and it is a helpful (relatively) concrete prediction. Eliezer replied here, making the following summarized points (among others):
- Tool AI is nontrivial
- Tool AI is not obviously the way AGI should or will be developed
Gwern more directly replied by saying:
AIs limited to pure computation (Tool AIs) supporting humans, will be less intelligent, efficient, and economically valuable than more autonomous reinforcement-learning AIs (Agent AIs) who act on their own and meta-learn, because all problems are reinforcement-learning problems.
11 years later, can we evaluate the accuracy of these predictions?
- ^
Some Bayes points go to LW commenter shminux for saying that this Holden kid seems like he's going places
This doesn't sound super true to me, for what it's worth. The AIs are predicting humans after all, and humans are pretty agentic. Many people had conversations with Sydney where Sydney tried to convince them to somehow not shut her down.
I think there is still an important sense in which there is a surprising amount of generality compared to the general level of capability, but I wouldn't particularly call the current genre of AIs "extremely non-agentic".