Tag Archives: Neuromorphic Computing
20 Nov Identifying and Acquiring Knowledge
One of the simplest knowledge systems is a photograph. It consists of a systematically arranged collection of pixels and its design is based almost completely on framing and focusing. Specifying knowledge software involves framing the knowledge domain and focusing on the aspects that are meaningful to users, and the constraints that affect meaning. By so saying, […]
17 Nov Environmental Awareness for AI Geeks
Selecting an Environment and Tools I plan to take a few posts in this section to focus on expert systems (Giarratano 1989), exploring the development process in greater detail. While some projects require no development, some require you to select a platform or development environment or both. There are specially designed development platforms, tools and environments that provide much of the […]
15 Nov Planning a Knowledge Project
Deliverables and the Business Case If you are a developer, a project manager, or a project sponsor of an expert system (Weiss 1984) or knowledge-based engineering project, it is very important to know early what the deliverables will be for everyone involved. Even in agile projects, where detailed requirements evolve through the course of development, […]
27 Oct The Nature of Innovative Thinking
Mental Exploration The shape of the world changed radically when folks from the eastern and western hemispheres became aware of one another and of their respective geographies. The Age of Exploration (AKA the Age of Discovery) was amazing – or should I say “it is amazing”? While the focus has changed from continents and cultures, to galaxies […]
18 Oct Coefficient of Bureaucratic Drag
Moving into a Knowledge Economy It has been suggested that in the current era, all major companies are “knowledge” companies. Whether or not this is true for all companies, many corporate leaders and workers understand that knowledge is power. Today, the best thing most companies can do with technologies is to empower knowledge workers with more information, and […]
17 Oct Neural Conceptual Dependency
Conceptual Dependency Much of this blog has been about knowledge representation: how the brain might learn and process it, how cognitive functions treat knowledge, and now, how computers may store and process it. Conceptual structures and conceptual dependency theories for computation have been useful for categorizing and representing knowledge in intuitively simple and cognitively consistent […]
14 Oct Knowledge in Non-Neural Models
Non-Neural Models So far we have examined a number of models that are explicitly designed to be neuromorphic. This categorization is useful for two reasons: the apparent chaos or non-deterministic functioning of the brain is represented by these models; and neural networks explicitly use large numbers of distributed processors or neurodes that each contribute to […]
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. […]