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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.

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.

What are Expert Systems?

Expert systems are pre-programmed systems that seek to fully replicate the knowledge and responses of true experts in a field.

According to TechTarget:

“An expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expertise and experience in a particular field.”

According to Oxford Languages:

“A piece of software programmed using artificial intelligence techniques. Such systems use databases of expert knowledge to offer advice or make decisions in such areas as medical diagnosis and trading on the stock exchange.’

The way they were designed when they were first revealed commercially in the 1970s and 1980s was such that they sought to fully replace experts in some ways, but also be a co-pilot to experts in another sense.

At present expert systems are similar to and have become ERP systems.

Are ChatGPT and Modern LLMs an Expert System?

To tackle one of the most important conversations of the day, are ChatGPT and other LLMs like Claude and Llama expert systems? The direct answer is no. The long answer is that there is a very clear connection to what an expert system is and set out to be.

OpenAI’s ChatGPT is the closest thing to an expert system that is public facing at scale, but it’s presently not a true expert system. However, there are ways to get to a proto-expert system to solve your everyday tasks.

One way that ChatGPT helps you build your own expert system, is via their data analysis tool. You can upload your own data, or a set of facts, and interact with the data in a way that you couldn’t before.

I have no doubt that OpenAI is and will be working with enterprises on implementing expert systems within their companies. I have no doubt that they are working with the top medical systems using their internal proprietary dataset and a RAG component to be a true expert system.

Has Expert Systems Been Dead Since the 1980s?

When I look on Amazon for books on Expert Systems, I get a lot of old stuff.

Building Expert Systems by Frederick Hayes-Roth was written in 1983:

A Guide to Expert Systems by Donald A. Waterman was written in 1986:

Artificial intelligence and the design of expert systems by George F. Luger and William A. Stubblefield was written in 1989:

I love old books, even if they’re outdated. It’s a version of the Lindy Effect – if we read older material, we can deduce what’s still relevant today. In addition, we can better understand present AI systems by reading the foundations upon which they were built.

Expert Systems Didn’t Fail, they Evolved

In this short video, WPI professor Dmitry Korkin talks with Lex Fridman on the history of expert systems as a precursor to modern AI.

Opinion 1: They were essentially not up to the expectations and became re

Opinion 2: They became too good, and became sort of a household name and became transformed.

The outcome was the same either way, they evolved into something else. There are echos in modern machine learning.

Opinion: Most Excel Jockeys Could Benefit from the Concepts of Expert Systems

As someone with a foot in two worlds: high-tech AI and traditional marketing practices, I’m very aware of some low value work that is done in Excel by millions of people every day – these are administrators, marketers, accountants, finance professionals, the list goes on.

Those who have domain expertise in their field, and it’s all in their head, are not really leveraging concepts like expert systems to any degree. Typically the historical practice of expert systems may be implemented in large corporations to help their entire workforce, but on the individual level I think there’s an opportunity for your average white-collar Excel jockey to understand and implement the concept of expert systems, for themselves.

For example, if you’re a marketing practitioner and a client asks about the latest stats and best practices on Instagram ads, you could reference your past work with other clients and what others in the field are saying, but in a sense you’re limited to your own expertise and your mental recall at the present moment. What if, instead, you had a spreadsheet of the latest Instagram ad stats and click through rates, CPA rates, and creatives that have tested the best, over the last 30, 60, 90 days that you could draw from? Ideally you’d have your own database of knowledge that you could query to get the answers for. This is an ideal way to use and implement expert systems in practice.

I think the disconnect is between experts in the AI and ML field and how they view the history and best practices and latest innovations in the field, and the general business worker bees.

Think about this: your average fresh college graduate has some textbook knowledge and limited experience, but honestly they are a blank slate for all intents and purposes. They have limited experience and lots of opinions and are early on in the Dunning-Kruger Effect Scale, overestimating their capabilities (I was there). What you want to hand them is a well-proven business playbook with best-practices and processes for them to really get the job done. You want dopamine hits (carrots) and corrections (sticks) when they hit or miss targets.

Modern ERPs & Expert Systems

I’m starting to see more modern ERPs that are leveraging concepts from AI and expert systems to deliver a high-value software products to customers.

Naologic looks like a fascinating company. “Naologic automatically builds AI-powered applications that automate your business and financial operations”.

They bill themselves as an “AI assistant with full business understanding”. Based on my limited research, they look to be a more modern, AI-infused ERP that works with both startups and enterprises alike.

They key differentiator here versus something like Retool, another business and enterprise focused systems builder, is their connection to working with your company’s data and infusing AI throughout.

This visual from Dynamics Square really helps understand the full context of ERP systems, within the context of digital transformation:

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While it’s focused on Microsoft’s stack, it really helps show how all the apps are connected in an enterprise.

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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