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07 Dec Curating Digital Meaning


I think of museums when I hear about curating. Meaning is, in a strange way, an artifact, simultaneously ancient and modern. Meaning has existed as long as perception has existed in the most rudimentary forms of life. For the purposes of my blog, I define meaning as: “the fruit of understanding and the fuel of […]

30 Nov Architecting Meaningful Relationships


How do you know — anything? Chemicals and electrical impulses splash around in the brain, and voila: we understand the meaning of life, the universe and everything. We have looked at how synapses connect neurons, and how taxonomical and other associations connect concepts, but is it even possible for a computer to understand the complex […]

29 Dec Unhuman Expertise

Expert System Architecture with Common Sense

Artificial Intelligence has suffered from a persistent scale problem: up to now, many techniques have been shown to work well and reliably in narrowly defined domains, but outside the domains of their expertise, they fall apart very quickly. No techniques of which I am aware, have exhibited common sense in the way we expect humans […]

17 Dec Visualizing Knowledge

Visualizations on multiple devices

Visualizing Knowledge – Automatic Generation Words are so symbolic that even symbolic thinkers, like me, understand more when there’s a picture to go along with the words. is partly explains my crazy use of images in this blog. The various forms of graphical representations are superb inventions that enable us to view and understand mathematical data […]

10 Dec Measuring Knowledge

Measuring Knowledge

Sometimes you need to know about your knowledge. When you’re in the middle of trying to build a system that knows stuff, you may ask, how much does the system know after this training or learning cycle as a percent of the total knowable amount? When we test students in their learning cycles, we use a […]

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 […]

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 […]

17 Oct Neural Conceptual Dependency

Representing Conceptual Graphs

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 […]

09 Oct Resolving a Paradox

Square Paradox

In time and space some things are impossible, but the pen is more powerful than reality. I can draw a world in which stairs lead in crazy, mind-bending directions, and I could probably build a structure that implemented upside-down staircases to nowhere. But I could never build the cube shown here, because it violates some […]