Epstein Secrets

HOE Higher-Order Inference • HOPLN: Higher-Order PLN • HR: Historical Repository (in a GOLEM context) • HTM: Hierarchical Temporal Memory • IA: (Allen) Interval Algebra (an algebra of temporal intervals) • IRC: Imitation / Reinforcement Correction (Learning) • LIFES: Learning-Integrated Feature S

, 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

HOE Higher-Order Inference • HOPLN: Higher-Order PLN • HR: Historical Repository (in a GOLEM context) • HTM: Hierarchical Temporal Memory • IA: (Allen) Interval Algebra (an algebra of temporal intervals) • IRC: Imitation / Reinforcement Correction (Learning) • LIFES: Learning-Integrated Feature S

, 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

amming HOI: Higher-Order Inference HOPLN: Higher-Order PLN HR: Historical Repository (in a GOLEM context) HTM: Hierarchical Temporal Memory IA: (Allen) Interval Algebra (an algebra of temporal intervals) IRC: Imitation / Reinforcement / Correction (Learning) LIFES: Learning-Integrated Feature Sele

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