Epstein Secrets

. PLN, a probabilistic logic engine; MOSES, an evolutionary program learning algorithm; ECAN, a nonlinear-dynamical attention allocation framework; DeSTIN, a deep learning perception algorithm) within a human-like cognitive architecture. The integration of these multiple learning algorithms is achieved

in terms of algorithmic information theory, and quantitatively measured in operational Al systems (Goertzel, Pennachin and Geisweiller, 2014). The CogPrime AGI design comprises an ambitious theoretical framework and practical blueprint for construction of an Artificial General Intelligence system with capab

. PLN, a probabilistic logic engine; MOSES, an evolutionary program learning algorithm; ECAN, a nonlinear-dynamical attention allocation framework; DeSTIN, a deep learning perception algorithm) within a human-like cognitive architecture. The integration of these multiple learning algorithms is achieved

in terms of algorithmic information theory, and quantitatively measured in operational Al systems (Goertzel, Pennachin and Geisweiller, 2014). The CogPrime AGI design comprises an ambitious theoretical framework and practical blueprint for construction of an Artificial General Intelligence system with capab

e we describe in detail the strategy that would be used to integrate 129 EFTA00624276 130 26 Perceptual and Motor Hierarchies CogPrime with the DeSTIN framework for AGI perception/action (which was described in some detail in Chapter 4 of Part 1). In terms of the integrative cognitive architecture

ical structure and less of a tie to the spatiotemporal structure of physical reality. The latter view is the one we are primarily taking within the CogPrime AGI approach, viewing perceptual, motoric and goal hierarchies as "lower level" subsystems connected to a "higher level" system based on the CogPrime A

spatiotemporally hier- archical deep learning system is an effective way to handle representation and learning of low-level sensorimotor knowledge. DeSTIN is one example of a deep learning system of this nature that can be effective in this context. i. One effective way to handle goals is to represent

e next decades as computers get more and more powerful and our understanding of neuroscience and cognitive science gets more and more complete. The CogPrime AGI design presented here is based on this working hypothesis. Many of the ideas in the book are actually independent of the "mind can be implemented d

a spatiotemporally hier- archical deep learning system is an effective way to handle representation and learning of low-level sensorimotor knowledge. DeSTIN is one example of a deep learning system of this nature that can be effective in this context. i. One effective way to handle goals is to represent

the next decades as computers get more and more powerful and our understanding of neuroscience and cognitive science gets more and more complete. The CogPrime AGI design presented here is based on this working hypothesis. Many of the ideas in the book are actually independent of the “mind can be implemented di

ric, cognitive and linguistic functions. We will achieve this via the creation and real-time utilization of links between the nodes in CogPrime’s and DeSTIN’s internal networks (a topic to be explored in more depth later in this chapter). 6.4 Memory Types and Associated Cognitive Processes in CogPrime N

ll use different processes to fulfill the various roles identified in Figure ?? above. Here we briefly preview the basic cognitive processes that the CogPrime AGI design uses for these roles, and the synergies that exist between these. 8.3.1 Cognitive Processes in CogPrime : a Cognitive Synergy Based Architec