Facial recognition is exactly the kind of "simple pattern matching" that I'm talking about. It's definitely useful, and definitely fits the label "artificial in

Facial recognition is exactly the kind of "simple pattern matching" that I'm talking about. It's definitely useful, and definitely fits the label "artificial intelligence" (at least compared to other solved tasks), but it simply is just one minuscule piece of what actually constitutes human intelligence. Bear in mind that the original post referenced the "singularity" -- when artificial intelligence is smarter and more capable than actual human intelligence. To be clear, I'm not saying that new AI paradigms are needed to improve that facial recognition score from 97.25% to 99+% (the existing techniques might actually be optimal anyway). I'm saying they are needed to even be able to attempt other tasks that actual humans find effortless, such as interpreting literature, creating a song, carrying on a conversation, decorating a room in an appealing way, understanding symbolic math, having this debate with you, etc. (Certainly symbolic math software has been developed, but no artificial intelligence has been developed that "understands" it and can put it into larger context, particularly one based on neural networks a la the real human brain.) I don't think my thinking is outdated (or even that controversial). It's mostly informed by a Deep Learning class I recently took taught by Yann LeCun, who is definitely at the forefront of the field (although these opinions are entirely my own). EDIT: Also, when I mention new "AI paradigms", I do expect them to build on the deep neural networks currently being studied. But more sophisticated architectures than feedforward nets or even the simple recurrent nets that I've seen so far will be needed, I think. And that will likely require some breakthroughs in theory as well as engineering.