. 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

. 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

eal with for the Robokind project, that we aren't needing to deal with for the current game-Al project, is machine vision. For this we will use the DeSTIN framework, currently being improved my someone working for me as I noted above. This funding for the robotics project is of a similar nature to the

DeSTIN (distinct from his commercial one) to CUDA, a parallel processing language that runs on Nvidia graphics cards. This should enable us to run DeSTIN scalably for robot vision. thx ben On Fri, May 27, 2011 at 5:29 PM, Ben Goertzel <ben@goertzeLorg> wrote: > Hi Jeffrey, > Thanks so much for you

ow links into my OpenCog framework.... He views DeSTIN as an autonomous approach to human-level AGI, which I'm a bit skeptical of, but I want to use DeSTIN as a perceptual processing lobe within OpenCog, and I've had a student in China (actually a Dutch guy in China for a 6 month internship) working on

g Patterns from DeSTIN States 175 29.3.2 Probabilistic Inference on Mined Hypergraphs 176 29.3.3 Insertion of OpenCog-Learned Predicates into DeSTIN's Pattern Library 177 29.4 Multisensory Integration, and Perception-Action Integration 178 29.4.1 Perception-Action Integration 179 29.4.2 Th

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

front of the robot, via pinching it between two fingers and then lifting it. In this case, • The visual scene, including the block, is perceived by DeSTIN; and appropriate patterns in various DeSTIN nodes are formed • Predicates corresponding to the distribution of patterns among DeSTIN nodes are acti

nderstanding or generation. A CogPrime approach to speech would be quite feasible to develop, for instance using neural-symbolic hybridization with DeSTIN or a similar perceptual-motor hierarchy. However, this potential aspect of CogPrime has not been pursued in detail yet, and we won't devote space to

d tool. However, if one represents low-level procedures like this using another method, e.g. learned cell assemblies in a hierarchical network like DeSTIN, then it's very feasible to make Combo programs that invoke these low-level procedures, and encode higher-level actions like "pick up the cup in fro

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

c, 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 Inter in this chapter). 6.4 Memory Types and Associated Cognitive Processes in CogPrime

tiple 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 t

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

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

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