VENICE LIDO, Italy – When the highly anticipated remake of Frank Herbert's influential '60s novel world premiered in September at the Venice Film Festival, it was "Dune -- Part 1." Now as Denis Villeneuve's lauded adaptation opens nationwide, it's simply "Dune" -- maybe because no one knows if there will be a concluding Part 2. "The biggest challenge," said Villeneuve ("Sicario," "Arrival") "is that the book is so rich and its strength is all in its details. I had to find equilibrium for someone who doesn't know the book at all and be as cinematic as possible. So that they will need to understand the movie without crushing them with exposition. So the ideas could follow the story." "Dune" is set far into a future where Oscar Isaac's Duke rules the kingdom of Atreides.

Classifier systems are machine learning systems incotporating a genetic algorithm asthe learning mechanism. Although they respond to inputs that neural networks can respond to, their internal structure, representation fonnalisms, and learning mechanisms differ marlcedly from those employed by neural network researchers inthe same sorts of domains. As a result, one might conclude that these two types of machine learning fonnalisms are intrinsically different. This is one of two papers that, taken together, prove instead that classifier systems and neural networks are equivalent. In this paper, half of the equivalence is demonstrated through the description of a transfonnation procedure that will map classifier systems into neural networks that are isomotphic in behavior. Several alterations on the commonly-used paradigms employed by neural networlc researchers are required in order to make the transfonnation worlc.

Classifier systems are machine learning systems incotporating a genetic algorithm as the learning mechanism. Although they respond to inputs that neural networks can respond to, their internal structure, representation fonnalisms, and learning mechanisms differ marlcedly from those employed by neural network researchers in the same sorts of domains. As a result, one might conclude that these two types of machine learning fonnalisms are intrinsically different. This is one of two papers that, taken together, prove instead that classifier systems and neural networks are equivalent. In this paper, half of the equivalence is demonstrated through the description of a transfonnation procedure that will map classifier systems into neural networks that are isomotphic in behavior. Several alterations on the commonly-used paradigms employed by neural networlc researchers are required in order to make the transfonnation worlc.

Classifier systems are machine learning systems incotporating a genetic algorithm as the learning mechanism. Although they respond to inputs that neural networks can respond to, their internal structure, representation fonnalisms, and learning mechanisms differ marlcedly from those employed by neural network researchers in the same sorts of domains. As a result, one might conclude that these two types of machine learning fonnalisms are intrinsically different. This is one of two papers that, taken together, prove instead that classifier systems and neural networks are equivalent. In this paper, half of the equivalence is demonstrated through the description of a transfonnation procedure that will map classifier systems into neural networks that are isomotphic in behavior. Several alterations on the commonly-used paradigms employed by neural networlc researchers are required in order to make the transfonnation worlc.