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Isn’t this “finding” (the fact that simple rulesets yield complex behavior that is hard to predict and/or derive using all kinds of models) the point that dr. S

Isn’t this “finding” (the fact that simple rulesets yield complex behavior that is hard to predict and/or derive using all kinds of models) the point that dr. Stephen Wolfram has been making for years? (Beautiful illustration of the “simple rules” that yield complex behaviors: https://www.wolframphysics.org/visual-summary/dark/ https://www.wolframphysics.org/visual-summary/dark/)

eternalban 6y ago
> the point that dr. Stephen Wolfram has been making for years? Stephen Wolfram, along with many others (predating him): An example. Note the dates. (A New Kind of Science is from 2002.) http://csc.ucdavis.edu/~chaos/Publications/JPC-Publications.html#EvCA http://csc.ucdavis.edu/~chaos/Publications/JPC-Publications.... Also see: Chris Langton: https://en.wikipedia.org/wiki/Christopher_Langton https://en.wikipedia.org/wiki/Christopher_Langton C. Langton, Studying arti􏱆cial life with cellular automata, Physica D 22 (1986) 120–149. Complexity of Langton’s ant: https://arxiv.org/abs/nlin/0306022 https://arxiv.org/abs/nlin/0306022
pedrosbmartins 6y ago
> the fact that simple rulesets yield complex behavior that is hard to predict and/or derive using all kinds of models I don't think this is the paper's "finding" at all. Sure, the Game of Life indicates that simple systems can result in complex, emergent behavior. But, exactly for being a simple set of rules, it is trivial to create a Game of Life implementation (or following this paper's definition, to solve the problem of predicting the nth next state, given the current state). The findings are rather that neural networks, despite its universality and prediction power, have a hard time learning parameters that solve this trivial task using a minimal architecture, so they require a larger amount of parameters or a very good initial set of weights ("lottery ticket").

eternalban 6y ago

> the point that dr. Stephen Wolfram has been making for years? Stephen Wolfram, along with many others (predating him): An example. Note the dates. (A New Kind of Science is from 2002.) http://csc.ucdavis.edu/~chaos/Publications/JPC-Publications.html#EvCA http://csc.ucdavis.edu/~chaos/Publications/JPC-Publications.... Also see: Chris Langton: https://en.wikipedia.org/wiki/Christopher_Langton https://en.wikipedia.org/wiki/Christopher_Langton C. Langton, Studying arti􏱆cial life with cellular automata, Physica D 22 (1986) 120–149. Complexity of Langton’s ant: https://arxiv.org/abs/nlin/0306022 https://arxiv.org/abs/nlin/0306022

pedrosbmartins 6y ago

> the fact that simple rulesets yield complex behavior that is hard to predict and/or derive using all kinds of models I don't think this is the paper's "finding" at all. Sure, the Game of Life indicates that simple systems can result in complex, emergent behavior. But, exactly for being a simple set of rules, it is trivial to create a Game of Life implementation (or following this paper's definition, to solve the problem of predicting the nth next state, given the current state). The findings are rather that neural networks, despite its universality and prediction power, have a hard time learning parameters that solve this trivial task using a minimal architecture, so they require a larger amount of parameters or a very good initial set of weights ("lottery ticket").