Epstein Secrets

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

sented online in a wikibook [Goel0b]; and the basic ideas of the design have been presented briefly in a series of conference papers [GPSL03, GPPG06, Goe09c]. But the overall design has not been presented in a coherent and systematic way before this book. In order to frame this design properly, we also pr

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

NG MEMORY Fig. 5.4: Architecture of Long-Term Memory and Deliberative and Metacognitive Thinking that interact with each other in a synergetic way [Goe09c], helping each other to overcome their intrinsic combinatorial explosions. There is significant evidence that these various types of long-term memory

ltiple layers, involving feedfor- ward and feedback dynamics, and adaptation of the links between the elements. An example deep learning algorithm is DeSTIN, which is being integrated with OpenCog for perception processing. Defrosting: Restoring, into the RAM portion of an Atomspace, an Atom (or set ther

al intelligence? In [CCT 2009, Hong Kong, 2009. Goe09b. Ben Goertzel. The embodied communication prior. In Proceedings of ICCI-09, Hong Kong, 2009. Goe09c. Ben Goertzel. Opencog prime: A cognitive synergy based architecture for embodied artificial general intelligence. In [CCI 2009, Hong Kong, 2009. Go