Showing posts from October, 2007

Perception as construction of stable interpretations

I've been spending a lot of time lately thinking about the nature of perception. As I've said before, I believe AI has gotten stuck at the two coastlines of intelligence: the knee-jerk-reaction of the sensory level and the castles-in-the-sky of the conceptual level. We've been missing the huge interior of the perceptual level of intelligence. It's not that programmers are ignoring the problem. They just don't have much in the way of a theoretical framework to work with, yet. People don't really know yet how humans perceive, so it's hard to say how a machine could be made to perceive in a way familiar to humans. Example of a stable interpretation I've been focused very much on the principle of "stable interpretation" as a fundamental component of perception. To illustrate what I mean by "stable", consider the following short video clip: Click here to open this WMV file This is taken from a larger video I've used in

Rebuttal of the Chinese Room Argument

While discussing the subject of Artificial Intelligence in another forum, someone brought up the old "Chinese Room" argument against the possibility of AI. My wife suggested I post my response to the point, as it seems a good rebuttal of the argument itself. If you're unfamiliar with the CR argument, there's a great entry in the Stanford Encyclopedia of Philosophy . It summarizes as follows: The argument centers on a thought experiment in which someone who knows only English sits alone in a room following English instructions for manipulating strings of Chinese characters, such that to those outside the room it appears as if someone in the room understands Chinese. The argument is intended to show that while suitably programmed computers may appear to converse in natural language, they are not capable of understanding language, even in principle. Searle argues that the thought experiment underscores the fact that computers merely use syntactic rules to manipulate symb

Video stabilizer

I haven't had much chance to do coding for my AI research of late. My most recent experiment dealt more with patch matching in video streams. Here's a source video, taken from a hot air balloon, with a run of what I'll call a "video stabilizer" applied: Full video with "follower" frame. Click here to open this WMV file Contents of the follower frame. Click here to open this WMV file The colored "follower" frame in the left video does its best to lock onto the subject it first sees when it appears. As the follower moves off center, a new frame is created in the center to take over. The right video is of the contents of the colored frame. (If the two videos appear out of sync, try refreshing this page once the videos are totally loaded.) This algorithm does a surprisingly good job of tracking the ambient movement in this particular video. That was the point, though. I wondered how well a visual system could le