AI vs. ML vs. RBL: The differences between Artificial Intelligence and Machine Learning and Rules Based Learning

Introduction

Artificial intelligence and machine learning are two terms that are often used interchangeably, but they are actually quite different. Machine Learning (ML) and Rules Based Learning (RBL) are both types of Artificial Intelligence.

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Artificial Intelligence

AI is an umbrella term that refers to the development of computer systems capable of performing tasks that would typically require human intelligence. These tasks may include speech recognition, problem-solving, learning, and decision-making. AI can be achieved using a variety of techniques, including rule-based systems, expert systems, and machine learning.

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Machine Learning

Machine learning is a specific approach within AI that creates algorithms that can learn and improve with experience and data. According to Google, "ML gives computers the ability to learn without explicit programming".

Instead of being explicitly programmed to perform a specific task, machine learning algorithms use data and examples to "learn" how to make predictions or decisions.  Unlike static rule-based systems, ML systems can adapt over time. As they get exposed to more data, they refine their models.

ML is particularly suited for scenarios involving massive datasets or intricate patterns because of its ability to process vast amounts of data and uncover insights. Examples include image recognition and natural language processing.

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Rules Based AI

Rule-based systems are another type of AI where decisions are made based on a set of predefined rules. These rules are often derived from human expert knowledge. For instance, a rule-based system might be used for tax calculations, where there are clear rules about how taxes should be calculated based on different incomes and expenses. Many early AI systems were Rules Based.

Rule-based systems don't "learn" in the way machine learning systems do, but they do emulate human expertise, a form of artificial intelligence, by automating the application of expert knowledge.

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‍Real-world Applications of AI, ML and Rules Based Applications

Here are some real life examples using Artificial IntelligenceI, Machine Learning or Rules Based Learning.

AI Applications:

• Speech Recognition: Systems like Apple's Siri or Google Assistant.
• Problem Solving: Chess games where the computer decides the best move.
• Learning: Adaptive learning software that modifies content based on a user's learning pace.
• Decision Making: Systems that assist in medical diagnoses by analysing patient data.

ML Applications:

• Predictive Models: Forecasting stock market trends or predicting equipment failures in manufacturing plants
• Classifying Data: Email filters distinguishing between spam and legitimate emails or categorising photos in online libraries
• Recognising Patterns: Face recognition systems in security systems or anomaly detection in credit card transactions to spot potential fraud.

Rule-based Applications:

• Financial Decisions: Automated systems that evaluate loan applications based on predefined criteria.
• Tax Calculations: Systems determining tax liabilities based on set rules about incomes, expenses, and applicable tax rates.
• Diagnostic Systems: Medical software that suggests potential diagnoses based on a set of symptoms and medical guidelines.
• Automated Customer Support: Chatbots that respond to customer queries based on a set of predetermined answers and decision trees.

Conclusion

AI encompasses a broad range of methodologies, including machine learning and rule-based learning, that emulate human intelligence. When deciding on an approach for a particular problem, it's not a matter of choosing between AI and ML or RBL, but rather choosing the best technique under the AI umbrella. The choice often comes down to selecting between ML or RBL, depending on the specific problem at hand, the nature of the data available, and the desired outcome. ML is typically chosen when the system needs to learn, adapt, and improve from data, especially in situations with large datasets or complex patterns. In contrast, RBL is preferred in scenarios where clear, predefined rules can be established based on expert knowledge.

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