Epstein Secrets

, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. • CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer c

m's memory of what it's useful to pay attention to, in what contexts. In CogPrime this is managed by the attention allocation subsystem. • Backward Chainer: A piece of software, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. • CIM-Dynamic: Concretely-Implemented Mind D

, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. • CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer c

m's memory of what it's useful to pay attention to, in what contexts. In CogPrime this is managed by the attention allocation subsystem. • Backward Chainer: A piece of software, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. • CIM-Dynamic: Concretely-Implemented Mind D

hing, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer cor

stem’s memory of what it’s useful to pay attention to, in what contexts. In CogPrime this is managed by the attention allocation subsystem. Backward Chainer: A piece of software, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. CIM-Dynamic: Concretely-Implemented Mind Dyn