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14 Oct Knowledge in Non-Neural Models

Concept Graph

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 Jul Playing the Slots

Curving Matrix or Frame

Frames and More You could just throw everything into the blender and see if a nice smoothie comes out. But for some cuisine and some complex processing tasks, the blender model is unsatisfactory. With neural networks and semantic networks and concept graphs, it may be best to separate things by category, choose different blender speeds, […]

07 Jul SPARQL Fireworks

Sparkler

How do you get at knowledge in conceptually structured information stores such as graphs? There are multiple ways to get data and information in broad use today. The most common is Structured Query Language (SQL) which is used as the almost universal access formalism for getting, storing and manipulating data in relational databases. An emerging standard […]

03 Jul Do Yawl do Petri Nets

Reactive vs Transformational Systems

Where do you draw a line? In geometry, digital theory, language and time, patterns tend to be linear: they bear distinct sequences. The sequences in these domains either contribute to the meaningfulness of the patterns, or, in the case of time, are the foundation of the patterns. Any logic that focuses on these sequential patterns is linear logic. Temporal Logic […]

23 Jun Information Transformation

Inachis Io Chrysalis Transform

Information Exchange and Transformation Knowledge does the most good when shared. Knowledge that gets lodged in one place may not be particularly useful to many people. But moving digital information from place to place has its dangers. Automating data movement can introduce security or confidentiality issues, data duplication challenges, as well as raising the specter of […]

16 Jun [Haley 2008]

Chain

10 Jun Survival of the Fittest Knowledge

Genetic Search

Genetic Algorithms in Search I think we can safely assume that intelligent applications, including accurate language interpreters and translators, will possess large amounts of knowledge to be processed and searched. Genetic algorithms are great for searching for obscure data in massive search spaces. The mechanism for association in computers can be defined as searching, just as humans describe their […]

05 Jun Intelligent Traveling Salesmen

Roadmap with Pins

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 […]

02 Jun Framing Formal Logic

Fuzzy Brain

Formal Logic Formal logic often uses set theory. Set theory uses existential (an assertion that something applies to some members of a set) and universal (a statement that applies to all members in a set) quantifiers. Despite the utility and noncommittal correctness of existential quantifiers, set operations using existential quantifiers are weaker then those using universal quantifiers. The […]

27 May Machine Components for Intelligence

Complex Flow Chart

If an abacus or a log and rope can be considered intelligent machines, then we can decompose their parts, possibly rearrange them, and get different kinds of intelligent machines. I know this is an extreme example of absurd reasoning. Let’s go from the opposite direction in the complexity spectrum. Can we use the human brain and its parts as […]