Originally I was going to wait and see what others posted so I could respond. Apparently this is not in the cards. So… The Metacat paper was intriguing. (Yes Doug, I know I need to elaborate on the word.)
In class we covered a lot of pertinent points regarding the Metacat paper: mostly what made Metacat so important to emergence as well as what the differences between Metacat and other models that had come before. I’m fascinated by the different categorizations required to look at the “world” of Metacat. Introducing the idea of having a computer make decisions for itself regarding analogies also emphasizes that the old categorization methods of “Reagan::parents as drugs::candy” does not make sense as the categorization itself is based on something totally different from the computer’s perspective. This seems to stress the fact that computers do not “think” in ways that humans do, but also takes advantage of how a computer “thinks” in order to interpret and create analogies. In this instance, Metacat is really awesome because of “self-awareness” in which there is an additional component of memory. The introduction of bias, even if it is small (as we discussed with the segregation model that was mentioned in class previously) has a noticeable effect on the outcome of a given test. Therefore, the weights are still important, yet useless without the memory implemented by the Metacat model. In this self-referencing in order to look towards the future Metacat is amazingly more concise. Instead of looking only at the present (as seen by most previous models) to springboard to the future speculation, there is also a reference check of past attempts, which makes for a far stronger model of emergence. (We noticed this problem of “no looking back” when we attempted to use the gaca.py to match a string.)
Okay… I think I’m starting to get incoherent, so I’ll sign off for now.