agreed on all points. and, I think there are kernels of truth from the things you’re disagreeing-with-the-implications-of, and those kernels of truth need to be ported to the perspective you’re saying they easily are misinterpreted as opposing. something like, how can we test the hard part first?
compare also physics—getting lost doing theory when you can’t get data does not have a good track record in physics despite how critically important theory has been in modeling data. but you also have to collect data that weighs on relevant theories so hypotheses can be eliminated and promising theories can be refined. machine learning typically is “make number go up” rather than “model-based” science, in this regard, and I think we do need to be doing model-based science to get enough of the right experiments.
on the object level, I’m excited about ways to test models of agency using things like particle lenia and neural cellular automata. I might even share some hacky work on that at some point if I figure out what it is I even want to test.
Yeah, I definitely grant that there are insights in the things I’m criticizing here. E.g. I was careful to phrase this sentence in this particular way:
The talk about iterative designs failing can be interpreted as pushing away from empirical sources of information.
Because yep, I sure agree with many points in the “Worlds Where Iterative Design Fails”. I’m not trying to imply the post’s point was “empirical sources of information are bad” or anything.
(My tone in this post is “here are bad interpretations I’ve made, watch out for those” instead of “let me refute these misinterpreted versions of other people’s arguments and claim I’m right”.)
[edit: pinned to profile]
agreed on all points. and, I think there are kernels of truth from the things you’re disagreeing-with-the-implications-of, and those kernels of truth need to be ported to the perspective you’re saying they easily are misinterpreted as opposing. something like, how can we test the hard part first?
compare also physics—getting lost doing theory when you can’t get data does not have a good track record in physics despite how critically important theory has been in modeling data. but you also have to collect data that weighs on relevant theories so hypotheses can be eliminated and promising theories can be refined. machine learning typically is “make number go up” rather than “model-based” science, in this regard, and I think we do need to be doing model-based science to get enough of the right experiments.
on the object level, I’m excited about ways to test models of agency using things like particle lenia and neural cellular automata. I might even share some hacky work on that at some point if I figure out what it is I even want to test.
Yeah, I definitely grant that there are insights in the things I’m criticizing here. E.g. I was careful to phrase this sentence in this particular way:
Because yep, I sure agree with many points in the “Worlds Where Iterative Design Fails”. I’m not trying to imply the post’s point was “empirical sources of information are bad” or anything.
(My tone in this post is “here are bad interpretations I’ve made, watch out for those” instead of “let me refute these misinterpreted versions of other people’s arguments and claim I’m right”.)