MindMeld: CEO and Artificial Intelligence Chapter 3 Highlights: Expert Systems

by Thomas B. Cross @techtionary

The following an excerpt to Chapter 3: Expert Systems of  MindMeld: CEO & AI Merging of Mental & Metal book available now via iBooks – Available on iPhone, iPad, iPod touch, and Mac.

Book Review – “As the CEO of a energy industrial company and actively involved in CEO Leadership Forums I have been following AI for more than a decade.  Indeed the promises for improving many technical tasks are interesting yet in reality often prove more complex to manage than proposed.  MindMeld was very profound in proposing that AI starts not at the bottom of the organization but with CXO decision-making and worth reading by anyone in or rising to the boardroom.” George B.

For interviews, professional guidance, product/market research or evaluations, articles, speeches or presentations, please contact cross@gocross.com 

From Chapter 3 – “In simple terms, an expert system (ES) is a computer program or system that organizes knowledge within rules or procedures to solve problems for a particular problem or task. If properly designed and maintained, an expert system can perform at or near the level of a human expert. The key issue is that an expert system is a machine. Current systems often have the constraints of the background and limitations of its creator-designer, and the skill and knowledge of the person who uses it. Presently most expert systems fail because (1) they require too much expertise from the user—it takes an expert to use an expert system—or (2) they solve only certain classes of problems—help you make chicken gumbo soup but not cream of chicken soup. An expert system must have a diverse background reference to be effective, as opposed to an incredible ability to be efficient. Expert systems reflect the rule-based side of decision making; mathematical models, formulas, algorithms, and heuristics can easily be applied, allowing expert systems to be developed and efficiently utilized. However, key point is a great algorithm applied against bad or biased data will only may the outcome worse. Where management or office procedures dictate a certain realm of finite possibilities to the decision makers, an expert system can be a vital management tool. Expert systems are more like productivity aids than truly intelligent software systems. They are tools that help managers improve the flow of information. These AI-assisted “power tools” provide an effective means for improving understanding, problem-solving, or decision-making. These capabilities suggest that a wide range of expert systems will be developed to guide clerks using payroll systems, help engineers with design, and aid doctors in diagnosis. A manager might also use such a tool to develop new models for organizational development, training, and policy analysis. These systems service the areas of business, computing, engineering, finance, geology, manufacturing, medicine, resource management, and science. The expert systems did everything from providing estate planning and investment advice to selecting auditing procedures, configuring computers, diagnosing infectious diseases, and assessing problems with oil wells. In comparing expert systems with other knowledge technologies, the term “expert system” is used to describe rule-based technologies where the information is packaged or premixed. The term knowledge network is used to describe a network of people in a thought-processing system. There are writers who use knowledge engineers synonymously with expert system engineers. To define a point of reference and to signify the role of people in the process and the resulting differences, knowledge networking refers to human-based activities. This is not to say that expert systems are nonhuman. The difference is subtle. In an expert system, experts program their knowledge into a computer.”

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