HHR
I find very interesting the general idea of re-writing. It's also a complicated problem, when you have more high level programs. What does it mean to rewrite? Usually, for example, as the program becomes more complex, you introduce the distinction between data structures and control structures. And in this case, normally, the re-writing is only happening on the data part.
DP
Yeah, but of course they are intimately linked. Somehow, it's the same thing as you said, it's similar to rewrite the algorithm that processes it. It has a similar effect. It's not so direct, but of course they are so interlinked that there you can't really distinguish.
HHR
I mean, you could argue the same speaking of neural networks, because the cell holds a state, and this state evolves. Basically that's a kind of rewriting of the algorithm.
POZ
But maybe it could even be applied to the algorithm itself?
DP
Yeah, but for example in a neural network the program continuously changes its data, if you consider "data" the weights, for example. And that actually alters the program, it modifies the neural network behaviour. So it's very difficult to find a clear boundary there.
HHR
I mean, there's always a boundary. There is always a little program that evolves another program. And that little program, of course, you define it once, usually. Or you iterate and then you have it, basically. That's also at the core, for example, of why people are not convinced that google page rank is an algorithm, or timeline presentation is an algorithm, because without all the models and data that it uses it's nothing. The algorithm is embedded in all these models that are making up most of the energy that is used. Maybe that's interesting, to think about: how often the symbols are touched. This might help in understanding which one is actually the important part of an algorithm. For example, if you need to traverse all the data a lot of times, it's obvious that the data somehow has a stronger weight in the overall formulation of the algorithm.
this conversation continued on the distinction between
Data and Process
HHR
So if we think about the situation, and we have this kind of environmental signal, which is the microphone, I have the feeling that it would be useful to have something that is not just representing that, but that can build something, in a way. Something that has a space that can grow and shrink somehow. That's why I'm kind of interested in this rewriting, as a possibility to grow and shrink, as a very abstract idea. L-Systems is useful in that case, as a general idea. Also because it works with symbols usually, which means it's kind of unclear how you come to symbols from an acoustical signal.
POZ
There are other rewriting algorithms, like grammar rules and stuff
DP
Yeah, there are a lot.
HHR
It's kind of interesting also because I remember when I was working in the POINT project with Gerhard Nierhaus, I saw this kind of different cultures. My background is more like signal processing, and so on. And his world is this kind of symbol spaces and dictionaries. From a signal processing point of view you don't have symbols, you have vectors, you have numbers.
DP
Which are all symbols in some way.
HHR
Yeah but symbols are more nominal, they don't have a relation to one another somehow. They are discrete things, like dictionaries or alphabets and so on. And so I never really thought that I would be interested in that, but somehow I find it challenging, especially if we can come up with something which is not, you know, the growing l-tree and so on.