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28 Jul Patterns in the Mind

Learning Head

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, […]

03 Jun Distributed Knowledge Representation

KR Scheme Symbol

Distributed KR Knowledge representation schemata may be top-down, or bottom-up. In a top-down approach, one would define an area or domain of knowledge, then list the concepts within the domain and their attributes. Behaviors within the domain are often defined by that domain, and the concepts may not be able to exist independently. This approach helps enforce a […]

28 May Data and Modeling

ERD - Entity Relationship Diagram

Data modeling is essential in the early stages of any information system design. By moving toward a data-centered model, we can make our data, and our system, smarter. There are many data-modeling techniques, but we will focus on two for now: Entity Relationship Diagramming (ERD), and Object Role Modeling or object relational modeling (ORM). Entity […]

26 May AI Domains and Approaches

Grouping, Classifying and Categorizing How do you solve big technical problems? Rather than selecting or inventing an approach and then attempting to apply it to a problem to see how well it works, let’s analyze the problem and see if we can find or invent a solution that matches the problem space, and see if […]

06 May Impulse Waves in Layers

Waves

Layered Model Just as the brain has areas with three to six distinct layers, a typical artificial neural systems (ANS) also has several layers. The example at right shows a network with three layers that illustrate a neural network‘s distributed architecture. The uniform circles connected by lines are symbolic of the state of an ANS at […]

05 May Learning from Errors

Error

If at first you don’t succeed, try – try again. Humans are pretty good at learning from our mistakes. In fact, some suggest that whatever doesn’t kill you makes you stronger. Today I’d like to riff on that theme a bit, and talk about ways in which machines can implement learning from errors. Error Minimization […]

26 Apr Continuity of Learning

Language in Head

Production and Comprehension We know that comprehension and language production occur in different areas of the brain and occupy opposite ends of the continuum in the communicative model. The relative independence of the production and comprehension centers suggests one of three possibilities: Syntactic and lexical data are replicated in both the production and comprehension centers of […]

09 Apr Abstract Contexts and Fuzzy Reasoning

The Face of AI

We do not yet know how we remember things, nor do we know how we use remembered things in reasoning. The amazing feedback loops of afferent and efferent fibers between different layers of the cortex give us some amazing clues (Hawkins 2004). Today’s discussion of abstract contexts and fuzzy reasoning is intended as a bridge […]

08 Apr Symmetrical Logic and Lineage

Morpho Butterfly

Symmetry may not immediately appear as a principle of logic or reason, but it should. In mathematics we learn the commutative and associative properties of addition and multiplication. These represent a mirror-like symmetry. Symmetry, or invariance against change, is a fundamental principle of physics and an underlying assumption driving some logical decisions. Causality, for example, […]