Tag Archives: understanding
08 Sep Gnostic Learning Model
In prior posts in this section, and periodically in other sections of my blog, I have been exploring how humans learn, and how we might replicate those processes in computer software or (less likely) hardware. The context of the learning, or knowledge acquisition, upon which I choose to focus is language learning. While knowledge acquisition is much broader, this is an […]
28 Aug Weight Control for Knowledge
Stochastic Models Data, information and knowledge may be stored in many different ways in computers. Most artificial neural models rely heavily on stochastic or probabilistic techniques for establishing the internal structure that represents the data. The generalized delta rule for adaptation is an example of this sort of technique. The generalized delta rule, developed by D.E. […]
25 Aug Determinacy in Neural Connections
For many years, researchers thought that it was wrong to assume that there was a cell or set of cells in the brain that stored the memory of Grandma’s face. Though the comparison with computer memory was appealing, it was thought to be too simplistic and incorrect. Now, more researchers in different academic disciplines are assuming […]
02 Aug Artificial Time
Time is omnipresent – you can’t get away from it. It is woven into everything we do and say and understand. It is an inextricable element of context. I was just speaking of how the connections in our brain develop, grow and evolve over time. Representing and handling this “temporal” element is fundamental to any […]
31 Jul Modeling Non-Random Synaptic Links
I have discussed the different meanings of “random” in “The Random Hamlet” and “That’s so Random!” in which the mathematical definition presumes there is some not yet known law that governs the phenomenon, where other definitions suggest that randomness means that the phenomenon is not governed by any law. Remember our reference to Rosenblatt’s early contributions in […]
28 Jul Patterns in the Mind
As we look for suitable solution designs for representing the knowledge and processes we humans use to communicate, we realize that we have no idea what knowledge in the brain looks like. Further, we only have relatively vague ideas about the processes that occur in the brain as we produce and comprehend words, phrases and sentences. […]
26 Jul Parallel Distributed Pattern Processing
PDP Networks We have discussed recognition processes in the brain. Connectionism, a fundamentally implicit approach to neural modeling, was championed by the parallel distributed processing (PDP) group. PDP networks use many interconnected processing elements (PEs) that, according to the PDP Group, configure themselves to match input data with “minimum conflict or discrepancy” (Rumelhart & McClelland, 1986, Vol. 2, […]
24 Jul Pattern Classification in Space
Pattern Classification Visual patterns can be recognized and classified based on prior knowledge: I see that this hairy animal has four legs and is about the same size as my dog, so I’ll assume it is (or classify it as) a dog. This may not be a correct classification, but it’s more correct than classifying it […]