Category Archives: Neural Networks
10 Jun Knowledge is the New Foundation for Success
Joe Roushar – June 2020 Retooling for The Beginning of a New Age This year, 2020 will be the turning point in AI adoption because successful implementations will be available to small and large organizations without breaking the bank. Success will be measured in specific business value achieved. Affordability will dramatically improve because of AI […]
08 Nov AI-BI-CI: Steps to Better Decisions
By: Joe Roushar – November 2018 AI: Artificial Intelligence Can you understand the things you observe and use them to make better decisions? Random colored dots that seem chaotic up close, when arranged by an artist in a pointillist painting, can become richly meaningful and appealing. The dots may have great variety in color and […]
21 May What it Means to Be, and Why AI Needs to Know
Joe Roushar – May 2018 I relate, therefor I am Interaction between humans is intricate, intimate and beautiful. Good conversations are like dances and can leave one feeling fulfilled and whole. I have long wanted to build a system that can verbally dance with you. Even longer, one to rapidly translate what you say with […]
04 Jul Cognitive Multi-Processing
Joe Roushar – July 2017 Divide and Conquer Swarm computing applications, with large numbers of autonomous agents are beginning to appear and deliver stunning results. The combination of autonomy, simple tasks and parallelism has great power. Today I’ll address parallel computing and models for breaking down computational problems. I will not address the question of autonomy today, […]
14 Jan Segregating Layers of Intelligence
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
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
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
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
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 […]