[publicdaniel]

Here's my really amateur understanding of this:

- Conventional SGD: Fixed target (e.g. "make an exact replica of this butterfly image") and it follows greedy path to minimize the error

- Open Ended Search Process: No predetermined goal, explores based on what's "interesting" or novel. In Picbreeder, humans would see several generated images, pick the "interesting" ones, and the system would mutate/evolve from there. If you were evolving an image that looked like an egg and it mutated toward a teapot like shape, you could pivot and pursue that direction instead.

This is kinda the catch -- there is a human element here where individuals are choosing what's "interesting" to explore, it's not a pure algorithmic process. That said, yes, it does use a genetic algorithm (NEAT) under the hood, but I think what the authors are suggesting is that the key difference isn't whether it's genetic or gradient based optimization... they're getting at the difference in objective driven vs. open-ended search.

I think the main position / takeaway from the paper is that something about conventional SGD training produces these "fractured entangled representations" that work but are not well structured internally so they're hard to build on top of. They look at things like the curriculum / order things are learned in, objective search vs. open-ended search, etc...