I clicked here and I was like: WTF??! I imagine that a scan of my brain would look even less legible, but I would like to see some useful output based on this.
Miss America 2000's victory walk down the runway and back is an example of walking on two legs, which is an example of a type of temporally stuff-like thing. Yay meta!
From the article:
AIs learn slowly now mainly because they know so little.
This seems implausible, because humans learn almost everything we know over our lifetimes, from a starting instruction set smaller than existing pieces of software. If architecture really is overrated (meaning that existing architectures are already good enough) isn't it more likely that AIs learn so slowly now, simply because the computers aren't powerful enough yet?
Today's post, I Heart CYC was originally published on December 1, 2008. A summary:
Douglas Lenat has a theory which Hanson finds plausible. Lenat argues that architechture of AI is overrated, and that there is only so much you can do before you start trying to generate content for an AI.
Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Disappointment in the Future, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.