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It would be amazing if Google was a true expert system.

You have a weird thing on your foot, WebMD doesn’t know?

Google should know – they should connect you to the best doctor in the world that has a 5 minute video and a 1-pager on that malady.

Currently it doesn’t work that way.

OpenAI’s ChatGPT is the closest thing to an expert system, but it’s presently

From this thought experiment and thinking about the future, I am personally interested in expert systems, as I think they are a foundation of the next 10 years with AI.

I think whoever builds the first way to truly have a multi-disciplinary expert system will be the next Google or more.

Google should be primed to become this and make this transition. But if they don’t, OpenAI or someone like it will.

I am researching expert systems as a beginner. I don’t know much about them. This is my research list which I will read, add notes from, and build my understanding of expert systems in this 101 guide.

I am a fan of “researching in progress” – which is like “building in progress”, but at the micro level of a blog post. Why not publish my initial thoughts and research list, get it indexing in Google, and then update it over time in a public way? It’s more iterative and fun that way.

Here’s the simple list in progress. I have not read all of these yet, just scanned them for quality control:

Research List (In Progress)

  • Wikpedia on Expert Systems: https://en.wikipedia.org/wiki/Expert_system
  • Are expert systems dead? https://towardsdatascience.com/are-expert-systems-dead-87c8d6c26474
  • TechTarget – expert systems definition – https://www.techtarget.com/searchenterpriseai/definition/expert-system

More Questions I Have, and ChatGPT’s Response to Them

These are the questions in my head. And here are ChatGPT’s responses. Don’t assume these are correct:

My Questions

  • Is google search an expert system?
  • What is an expert system?
  • Are LLMs an expert system?
  • Explain an expert system vs LLMs?
  • Explain an expert system vs AI?
  • Does Google have an expert system within the company that they use?
  • But it might not be deployed across general search?
  • Is the challenge that the best expert systems are small rather than broad?
  • Should building an expert system start with a particular industry?
  • Should building an expert system start with a particular problem to be solved with a high ROI?
  • What are popular examples of expert systems?
  • Do expert systems apply more to a B2B scenario?
  • What are some wild predictions about expert systems in the 2025-2030 time range?
  • What are the necessary components of expert systems?

ChatGPT Responses – Take with Grain of Salt

Expert Systems: Definition and Characteristics

An expert system is a type of artificial intelligence (AI) program that emulates the decision-making ability of a human expert. These systems are designed to solve complex problems by reasoning through bodies of knowledge, represented primarily as if-then rules rather than through conventional procedural code. Key characteristics include:

  • Knowledge Base: A comprehensive collection of domain-specific knowledge.
  • Inference Engine: Applies logical rules to the knowledge base to deduce new information or solve problems.
  • Explanation Facility: Provides explanations and justifications for the conclusions reached.
  • User Interface: Enables interaction between the user and the system.

Expert Systems vs. Large Language Models (LLMs)

  • Knowledge and Learning: Expert systems contain explicit, predefined knowledge, while LLMs like ChatGPT learn implicitly from vast datasets.
  • Reasoning: Expert systems use rule-based reasoning, whereas LLMs use statistical pattern recognition and prediction.
  • Flexibility: LLMs can handle a broader range of topics but may lack depth in specific domains compared to expert systems.
  • Explainability: Expert systems can often explain their reasoning process, a feature that’s not inherent in LLMs.

Expert Systems vs. General AI

  • Scope: Expert systems are specialized for specific domains, whereas general AI aims to perform a wide range of tasks with human-like intelligence.
  • Adaptability: General AI is designed to learn and adapt, while expert systems operate within a fixed framework of rules and knowledge.
  • Application: Expert systems are ideal for niche, rule-based tasks, whereas general AI has broader applications.

Google’s Use of Expert Systems

Google may use expert systems for specific tasks or problems within the company, especially in areas requiring specialized knowledge or rule-based decision-making. However, this isn’t publicly detailed, and their general search engine relies more on complex algorithms and AI models than on traditional expert systems.

Scope and Development of Expert Systems

  • Niche vs. Broad: The effectiveness of expert systems often depends on their focus. Broad systems can be challenging due to the complexity and depth of knowledge required.
  • Industry-Specific Development: Starting with a particular industry can be beneficial, especially in fields with well-defined knowledge and rules, like medicine or law.
  • Problem-Specific Approach: Focusing on a high-ROI problem can guide the development of an expert system, ensuring practicality and relevance.

Popular Examples of Expert Systems

  • Medical Diagnosis Systems: Like IBM’s Watson for Oncology.
  • Financial Decision Systems: For investment and credit scoring.
  • Manufacturing Process Control Systems.

Expert Systems in B2B Scenarios

Expert systems are indeed more common in B2B scenarios where they can address specific industry-related challenges, provide specialized knowledge, or automate complex decision-making processes.

Predictions for Expert Systems (2025-2030)

  • Integration with Other AI Technologies: Combining with machine learning and data analytics for enhanced performance.
  • Expansion in Healthcare and Legal Fields: More sophisticated diagnostic and legal advisory systems.
  • Increased Personalization in Business Applications: Tailored solutions for unique business problems.

Necessary Components of Expert Systems

  1. Knowledge Base: Domain-specific, structured knowledge.
  2. Inference Engine: Logic-based mechanism to apply rules.
  3. User Interface: Efficient, user-friendly interaction.
  4. Explanation Facility: To justify decisions and processes.
  5. Knowledge Acquisition Facility: For updating and expanding the knowledge base.

More to come next:

  • Summaries of those articles on expert systems
  • Better build out of subtopics in this area
  • List new questions per area
  • Attempt to answer
  • Quote leaders in the field more
  • Gather best YouTube videos
  • Reach out to experts for unique quotes
  • Add helpful expert systems infographics
  • Add quizzes on expert systems
  • Add templates on expert systems design

Joe Robison

Founder & Consultant
Joe Robison is the founder of Green Flag Digital. He founded the agency in 2015 and has been heads-down scaling content marketing and SEO services for clients ever since. He is an occasional surfer, fledgling yogi, and sucker for organized travel tours.
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