Tag Archives: learning
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 […]
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. […]
15 Jul From Perception and Learning to Logic
Perception and Learning I am not a cognitive scientist, so all I have said in this section is based on the work of others. On the other hand, I have probably spent more time seriously studying cognition than most computer geeks, and I have tried to form my perspectives around the best of our knowledge. The […]
01 Jul Chaos About Us
Chaos About Us Chaos is all about us. I know that for certain each time I look into my kids’ rooms. When I recall my own youth, however, it occurs to me that I had a reason for the way I organized my life. It seemed meaningful to me, and although I recall how difficult it was […]
16 Jun Genetic Cross-Pollination
Cross Pollination Many approaches exist for simulating intelligent behavior on computers. In the past, the most popular approach was to focus on developing a technique and applying it to a problem. Most basic research has focused on single paradigms and their properties. Sometimes, however, in domains where multiple approaches to problem solving are possible, some […]
05 Jun Intelligent Traveling Salesmen
Another Sample Problem Several specific reasoning or inference problems have provided fodder for AI textbooks and experiments. One of these is the traveling salesman problem (Get an explanation and an example applet here): Given a traveling salesman who must get to x number of cities, find the shortest route the salesman can travel to reach […]
03 Jun Distributed Knowledge Representation
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 […]