Tag Archives: AI
20 Oct High 5s of Intelligent Information Modeling
Joe Roushar – October 2015 Quantitative data is easy to make into useful information by establishing correct associations and providing human experts with the right slicing and dicing tools. But in its native format, data is not independently meaningful. Many qualitative content sources are narrative, born as whole information. Such content is advanced beyond data because of the built-in associations, but inevitably […]
27 Feb Six Reasons Businesses are Implementing Semantics
The wave is coming. I spent some quality time last summer in a large consumer products manufacturer in Cincinnati helping architect semantic capabilities to automate tasks in chemical compliance for selling a large number of products in dozens of countries, each with hundreds of rules that apply to thousands of chemicals constituents. You can imagine the combinatorial explosion. […]
26 Jan Give Me Smart Requirements
I know how to solve this problem! I’ll just blast it to smithereens – Fire – Aim – Ready. Artificial Intelligence (AI) has been explored and developed in universities and startup companies throughout the developed world for decades, but is still struggling to reach the mainstream. There are many companies that effectively use intelligent processes and […]
29 Dec Unhuman Expertise
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 […]
10 Dec 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
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 […]
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 […]
09 Oct Resolving a 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 […]
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 […]
09 Jun In Search of Depth and Breadth
Depth-First Search Search is one of many functions needed in intelligent systems. Web search in systems like Google, Bing, Yahoo and Alta Vista use complex algorithms to help you find the information you want based on words. Words are patterns of letters strung together in a unique way. Searching is a kind of pattern matching. […]