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as it brings together all the innovations of previous systems. The software invents
aesthetic functions, innovates with new concept formation methods that combine
mathematical functions, and generates new wr app ers which turn the functions into
images. Finally, the programmer has the idea ( F p ) of getting the software to write
commentaries, as in [ 41 ], about its processing and its results, which it does in gen-
erative act F g .
Tracking how the system diagrams change can be used to estimate how audiences
might evaluate the change in processing of the software, in terms of the extended
creativity tripod described above. Intuitively, each system represents progress from
the one preceding it, justified as follows:
E g >
1
2 :
<
C g ,
E g > <
C g ,
Simple repetition means that the software has more skill , and the introduction of
independent user selection shouldn't change perceptions about autonomy .
2
3 : S
S
By reducing user intervention in choosing images, the software should appear to
have more skill and autonomy .
1
4 : Introduction of A g and S
(
a g (
e g ))
acts
Machine learning enables the generation of novel aesthetics (albeit derived from
human choices), which should increase perception of innovation , appreciation and
learning , involving more varied creative acts.
4
5 : Introduction of an E p act, T
T
Wrapper generation increases the variety of creative acts, and may increase percep-
tion of skill and imagination .
1
6 : Introduction of A g and S
(
a g (
e g ))
acts
The software has more variety of creative acts, and the invention and deployment of
its own aesthetic—this time, without any programmer intervention—should increase
perception of intentionality in the software.
 
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