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Artificial Intelligence - Expert Systems | LeanIX

Posted by Lesa Moné on 18 January 2018

Artificial Intelligence - Expert Systems

As enterprises got comfortable with the terms artificial intelligence, machine learning, and big data in 2017, 2018 will be the year that organizations begin architecting a roadmap for implementation.

The main factors that hold companies back from applying artificial intelligence are the lack of knowledge, resources, and the immense gap between research and actual artificial intelligence usage. When implemented correctly, artificial intelligence technologies can prepare your company for new streams of income. AI has been successfully used in many industries from healthcare to fraud detection to intelligent recommendation and data security. Given its vast versatility and applicability, there is no doubt 2018 will be a big year for artificial intelligence with massive earning potential for organizations.  

In preparing for artificial intelligence, it is imperative to know all of the necessary components. This blog post will explore expert systems, and give examples of current expert systems.

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 What is an expert system?

In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code.

Expert systems have specific knowledge to one problem domain, e.g., medicine, science, engineering, etc. The expert’s knowledge is called a knowledge base, and it contains accumulated experience that has been loaded and tested in the system.  Much like other artificial intelligence systems, expert system’s knowledge may be enhanced with add-ons to the knowledge base, or additions to the rules. The more experience entered into the expert system, the more the system can improve its performance.

 Characteristics of expert systems:

  • Highly responsive
  • Reliable
  • Understandable
  • High performance

Expert systems today:

Although the public opinion differs on if our jobs will be replaced by artificial intelligence or not, expert systems are the artificial intelligence that will come for analytical, white collar jobs. Expert systems are proficient in reasoning, classification, configuration, pattern matching, diagnosis, and planning, certain industries are set up for disruption. Financial services, healthcare, customer service, aviation, and written communication can all be carried out by expert systems.

The first expert system to be approved by the American Medical Association was the Pathfinder system. Built at Stanford University in the 1980s, this decision-theoretic expert system was built for hematopathology diagnosis. In short – Pathfinder is an expert system that seeks and diagnoses lymph-node diseases. In the end, Pathfinder deals with over 60 diseases and can recognize over 100 symptoms. The latest version of Pathfinder outperforms its creators - the world’s leading pathologist.

 

 

Expert systems in business:

As expected, expert systems are being developed and deployed worldwide in myriad applications, mainly because of their symbolic reasoning and its explanation capabilities. A recently developed expert system ROSS, the AI attorney. ROSS is supported by self-learning systems that use data mining, pattern recognition, deep learning, and natural language processing to mimic the way the human brain works.
It may not be time for expert systems in your enterprise, but it is an exciting development to watch. 

Enterprise architect's guide to machine learning

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