From Expert Systems to Machine Learning: a strategic shift toward intelligent sutomation

Introduction
In the 1980s and 1990s, Expert Systems represented one of the most promising solutions in the field of artificial intelligence (AI), as they aimed to replicate human decision-making processes in specific sectors. However, these systems proved limited and inflexible, leading the market and research to shift towards new technologies. The advent of Machine Learning (ML), with its automated learning models, overcame many of the barriers posed by Expert Systems, enabling applications in increasingly complex and diverse contexts.
This article explores the structure of Expert Systems, analyzes their main limitations, and illustrates the reasons behind the transition to Machine Learning, a technology that has revolutionized the automation of decision-making and predictive processes.

Expert Systems: structure and limitations
Expert Systems are software designed to mimic the decisions of a human expert in a specific field. Each Expert System is based on two key components: a knowledge base and an inference engine. The knowledge base gathers facts and rules specific to the domain, typically in the form of “if-then” rules, and represents a structured set of information that the inference engine uses to make deductions.
For example, an expert system in the medical field could contain rules that, based on a patient’s symptoms, suggest diagnoses or recommendations. A doctor can enter the observed symptoms into the system’s interface, obtaining a list of possible diagnoses based on the knowledge base data. However, the limited ability to adapt to new data and the need for constant updates made these systems vulnerable to errors and incapable of effectively responding to unforeseen situations.

Example of implementing an expert system
An expert system can be implemented in various programming languages. Below is a simple example in C# language to illustrate basic decision-making logic. This program diagnoses the flu based on selected symptoms:

using System;
public class FluDiagnosis
{
    public class Symptoms
    {
        public bool Fever { get; set; }
        public bool Cough { get; set; }
        public bool SoreThroat { get; set; }
        public bool MusclePain { get; set; }
        public bool Fatigue { get; set; }
    }

    public static bool HasFlu(Symptoms symptoms)
    {
        if (symptoms.Fever && symptoms.Cough && symptoms.SoreThroat)
        {
            return true;
        }
        if (symptoms.Fever && symptoms.MusclePain && symptoms.Fatigue)
        {
            return true;
        }
        return false;
    }

    public static void Main(string[] args)
    {
        Symptoms patientSymptoms = new Symptoms
        {
            Fever = true,
            Cough = true,
            SoreThroat = true,
            MusclePain = false,
            Fatigue = false
        };

        bool diagnosis = HasFlu(patientSymptoms);

        if (diagnosis)
        {
            Console.WriteLine("The patient has the flu.");
        }
        else
        {
            Console.WriteLine("The patient does not have the flu.");
        }
    }
}

This code is only a basic and limited example; mapping a wide range of symptoms and diagnoses would require thousands of rules, making the system complex and difficult to update. In practice, clinical information is usually stored in a database, making the system more dynamic and manageable.

Advantages and disadvantages of Expert Systems
Expert Systems have several significant advantages, including:

• knowledge preservation: They encode and make human experts’ experience accessible to a wide range of users, allowing broader knowledge distribution.
• consistency in decisions: Predefined rules enable consistent decisions and reduce the risk of human error.
• continuous availability of expertise: In situations where experts may not be readily available, an expert system can provide preliminary guidance.

However, Expert Systems are limited by some critical aspects:

• need for constant updating: The knowledge base must be regularly updated to maintain its relevance.
• structural rigidity: Unable to adapt autonomously to new data, Expert Systems require human intervention for each update.
• scalability issues: Complex domains require an increasing number of rules, making the system difficult to manage and slowing down performance.

The transition to Machine Learning
Technological evolution has led to a decisive turning point with Machine Learning, which has allowed many of the limitations of Expert Systems to be overcome. Unlike Expert Systems, Machine Learning can analyze large amounts of data (big data), detect complex patterns, and update itself autonomously.

Key factors enabling this transition:

• digitization and Big Data: The exponential increase in data availability in various sectors has created new opportunities for machine learning. ML models can dynamically learn from accumulated information, making the system flexible and scalable.
• hardware advancements: The use of advanced processing units (CPU, GPU, and TPU) has significantly reduced calculation times, allowing complex models to be trained with greater speed and efficiency.
• new learning algorithms: With the introduction of deep neural networks, Machine Learning has become capable of learning from large datasets without the need for a predefined knowledge base. Supervised, unsupervised, and reinforcement learning models have become versatile tools for numerous applications, from predictive analysis to medical diagnosis.

In a medical context, for example, an ML system can be trained on historical medical records, automatically correlating symptoms and diagnoses without the need to encode static rules. This model becomes able to make predictions on new cases and can be updated automatically with new data, adapting to the advancement of knowledge in the medical field.

Advantages of Machine Learning over Expert Systems
Compared to Expert Systems, Machine Learning offers a series of crucial advantages for modern applications:

• scalability: ML models can be trained on large amounts of data, allowing complex situations to be managed that would be unfeasible for Expert Systems.
• flexibility and adaptability: ML models can automatically update, improving predictions with the addition of new data.
• efficiency: Once trained, an ML model can make predictions quickly and with less human intervention compared to Expert Systems.
• ability to generalize: ML models can learn from training data and apply this knowledge to make predictions on unseen data, increasing accuracy.

Conclusion
The transition from Expert Systems to Machine Learning marked a fundamental shift in the evolution of artificial intelligence. While Expert Systems, though useful as support tools in specific contexts, demonstrated significant limitations in flexibility and scalability. Conversely, Machine Learning has opened up new possibilities thanks to its ability to autonomously learn from data, constantly improving without extensive manual intervention.

This innovation has led to widespread adoption of Machine Learning across various sectors, from medicine to finance and engineering. With data-driven models, AI is now able to respond dynamically to changes and market needs, supporting organizations in optimizing decision-making processes and introducing new automation solutions.