3 ms·

Yes, true, however, what I find egregious in this case is the comparative lack of standards. Among the other technical and scientific topics often discussed he

Yes, true, however, what I find egregious in this case is the comparative lack of standards. Among the other technical and scientific topics often discussed here, there is a basic general expectation of grounded reference to existing research and bodies of knowledge. Deviations from this standard are usually met with corrections. With the topic of cognition and consciousness, this is not so (generally). The standard is to absolutely not reference existing research and to basically pretend it doesn't exist. Instead, the vacuum of ignorance is filled with whatever comes to mind and threads engage in freeform speculation. This is also ironic since these topics are usually about how LLMs don't understand and just fill the vacuum of its ignorance with whatever happens to be next on the statistical chain, regardless of veracity.

sho_hn 3y ago
I think it's because a lot of people presume the topic at hand to be relatively novel and not have a significant body of work behind it. At to some extent this is because they have been told this is the case - even by experts or their reporters. With physics, for example, people have an understanding that there's a large existing body of work, and that physics has been remarkably successful at building on earlier knowledge for decades without having to debunk a lot of earlier results. It's a familiar story. With AI, however, the popular narrative is that earlier approaches to the problem were all laughing stocks that went nowhere. There's not a good sense for how old some the ideas that work now actually are, and how many of the older theoretical and thought frameworks are still perfectly valid, in particular at the interfaces toward other branches of science and engineering. There's some more nuanced and balanced primers on the topic (e.g. the Wooldridge book), but an awful lot more writing on AI Winters and what not. This snowballs into a lack of awareness of the overlap between "AI" and, say, the body of knowledge in statistics. In other words: Sure, I agree - HN collectively is less knowledge on this than on some other topics (myself included!), and it's worth taking stock of that, I suppose.

sho_hn 3y ago

I think it's because a lot of people presume the topic at hand to be relatively novel and not have a significant body of work behind it. At to some extent this is because they have been told this is the case - even by experts or their reporters. With physics, for example, people have an understanding that there's a large existing body of work, and that physics has been remarkably successful at building on earlier knowledge for decades without having to debunk a lot of earlier results. It's a familiar story. With AI, however, the popular narrative is that earlier approaches to the problem were all laughing stocks that went nowhere. There's not a good sense for how old some the ideas that work now actually are, and how many of the older theoretical and thought frameworks are still perfectly valid, in particular at the interfaces toward other branches of science and engineering. There's some more nuanced and balanced primers on the topic (e.g. the Wooldridge book), but an awful lot more writing on AI Winters and what not. This snowballs into a lack of awareness of the overlap between "AI" and, say, the body of knowledge in statistics. In other words: Sure, I agree - HN collectively is less knowledge on this than on some other topics (myself included!), and it's worth taking stock of that, I suppose.