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04 Jul Cognitive Multi-Processing

Layered Model

Today I’ll address parallel computing and models for breaking down computational problems. I will not address the question of autonomy today, but save the question of empowering independent agents for a future post. ANS and Multiprocessors Artificial Neural Systems (ANS) are probably the closest approximation of the mechanical brain paradigm, so it is useful to know how […]

22 Jun [Hockney 1988]

14 Jan Segregating Layers of Intelligence

Layer Cake

Layered Architectures Layers appear regularly in my blog, whether it’s layers of the brain, layers of processing nodes in artificial neural networks or layers in systems architectures. Layering embodies important patterns in the inexorable move toward a knowledge economy with knowledge systems. In today’s post, I’m going to talk about what layering brings to enterprise […]

26 Nov Planning and Scheming

Paint a Brain

Select a Knowledge Representation (KR) Scheme In prior posts I have been describing the steps of building knowledge systems. A major part of Step 3: Task 1 is defining how to store knowledge – selecting a scheme. Giarratano and Riley (1989) suggest making the selection of a scheme, such as rules, frames or logic, dependent upon […]

28 Oct Chemicals And Cognitive Performance

Lightning Brain

Outside Influences Mind-altering chemicals, stimulants, depressants and hallucinogens to name a few, affect the entire process of cognition, from receiving and processing input, through recognition and reasoning. They often even improve or impair our ability to act, affecting everything from muscle performance to language production and comprehension. Bacteria and viruses can also impact people. Things that come from outside […]

21 Oct Fuzzy Interconnectedness

Phone Brain

Fuzzy and Interconnected Techniques Section 5 suggests that the software of cognition is very fuzzy and able to operate efficiently even without having complete or totally accurate information. We said that we want to replicate that flexibility. We spoke in Section 7 about different fuzzy approaches for representing and processing information. These approaches include artificial […]

08 Sep Gnostic Learning Model

Hard Disk in Brain

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 […]

25 Aug Determinacy in Neural Connections

Neural Net

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 […]

18 Aug Modeling Positive and Negative Activation

Blue Neurons

Humans learn from both positive and negative experiences. The electrical flow between neurons can be positive (excitatory), propagating electrical potential flow along neural path to create further excitation and a bubbling-up effect, or negative (inhibitory) reducing or stopping the electrical potential flow along a pathway. Remember that a neural pathway is not like a long line, but like […]

02 Aug Artificial Time

Time and Space Perception

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 […]