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.
Tags: emergence, reading response
I also found the ability to recall past attempts really remarkable. I started to think of how metacat worked like my problem solving strategy. Like with metacat i think of what a could try and decide if it is reasonable to try. In comparison to gaca i like how i was able to understand how the program works. however i can see how metacat works better because of its ability to analyze what the problem is and which attempts are better. While gaca did this it only did this with a numerical score and then reserved the top choices. metacat looks at why something worked and why something didnt worked and i really found this amazing just by the sheer complexity of coding and ingenuity.
(I wish I had a better response for your comment other than “Yay Bethie!”)
I still find it really fascinating that it can remember and then base decisions off of past memories; even if we discussed in class the issues inherent in maintaining human interest in something that can grow boring over time. (Especially if we’re looking at such an issue and then thinking, “I do this anyway, why do I care about the computer doing it too?”)