Hi Caleb! Very nice to read your reflection on what might make you think what you think. I related to many things you mentioned, such as wondering how much I think intelligence matters because of having wanted to be smart as a kid.
You understood correctly that intuitively, I think AI is less of a big deal than some people feel. This probably has a lot to do with my job, because it includes making estimates on if problems can be solved with current technology given certain constraints, and it is better to err to the side of caution. Previously, one of my tasks was also to explain people why AI is not a silver bullet and that modern ML solutions require things like training data and interfaces in order to be created and integrated to systems. Obviously, if the task is to find out all things that can future AI systems might be able to do at some point, you should take a quite different attitude than when trying to estimate what you yourself can implement right now. This is why I try to take a less conservative approach than would come naturally to me, but I think it still comes across as pretty conservative compared to many AI safety folks.
I also find GPT-3 fascinating but I think the feeling I get from it is not “wow, this thing seems actually intelligent” but rather “wow, statistics can really encompass so many different properties of language”. I love language so it makes me happy. But to me, it seems that GPT-3 is ultimately a cool showcase of the current data-centered ML approaches (“take a model that is based on a relatively non-complex idea[1], pour a huge amount of data into it, use model”). I don’t see it as a direct stepping stone to science-automating AI, because it is my intuition that “doing science well” is not that well encompassed in the available training data. (I should probably reflect more on what the concrete difference is.)
Importantly, this does not mean I believe there can be no risks (or benefits!) from large language models, and models that will be developed in the near future.
I think it is very hard to be aware of your intuitions, incorporate new valid information to your world view and communicate with others at the same time. But I agree that for everyone it is better if we create better opportunities to do that, because otherwise we will lose information.
not to say non-complexity would make the model somehow insignificant, quite the opposite, it is fascinating what attention mechanisms accomplish not only in NLP but on other domains as well
Hi Caleb! Very nice to read your reflection on what might make you think what you think. I related to many things you mentioned, such as wondering how much I think intelligence matters because of having wanted to be smart as a kid.
You understood correctly that intuitively, I think AI is less of a big deal than some people feel. This probably has a lot to do with my job, because it includes making estimates on if problems can be solved with current technology given certain constraints, and it is better to err to the side of caution. Previously, one of my tasks was also to explain people why AI is not a silver bullet and that modern ML solutions require things like training data and interfaces in order to be created and integrated to systems. Obviously, if the task is to find out all things that can future AI systems might be able to do at some point, you should take a quite different attitude than when trying to estimate what you yourself can implement right now. This is why I try to take a less conservative approach than would come naturally to me, but I think it still comes across as pretty conservative compared to many AI safety folks.
I also find GPT-3 fascinating but I think the feeling I get from it is not “wow, this thing seems actually intelligent” but rather “wow, statistics can really encompass so many different properties of language”. I love language so it makes me happy. But to me, it seems that GPT-3 is ultimately a cool showcase of the current data-centered ML approaches (“take a model that is based on a relatively non-complex idea[1], pour a huge amount of data into it, use model”). I don’t see it as a direct stepping stone to science-automating AI, because it is my intuition that “doing science well” is not that well encompassed in the available training data. (I should probably reflect more on what the concrete difference is.)
Importantly, this does not mean I believe there can be no risks (or benefits!) from large language models, and models that will be developed in the near future.
I think it is very hard to be aware of your intuitions, incorporate new valid information to your world view and communicate with others at the same time. But I agree that for everyone it is better if we create better opportunities to do that, because otherwise we will lose information.
not to say non-complexity would make the model somehow insignificant, quite the opposite, it is fascinating what attention mechanisms accomplish not only in NLP but on other domains as well