Rule-Based vs AI vs ML

Good morning/afternoon everyone. My name is Firman.

Today, I will talk about three important decision-making systems: Rule-Based Systems, Artificial Intelligence (AI), and Machine Learning (ML). We will look at their differences and how they help businesses.

The Digital Era

Let’s start with the background.

In the past, business decisions were manual. Today, we have too much data, so we use digital systems.

Basically, there are three types:

1. Rule-Based: Follows strict logic.
2. AI: Mimics human intelligence.
3. Machine Learning: Learns from data.

Rule-Based Systems

First, let's look at Rule-Based Systems.

These systems work on 'If-Then' logic. They do not 'think'; they just follow instructions written by humans.

Example: A bank loan system. If salary is high and debt is low, then approve. It is very strict.

Rule-Based Analysis

What are the pros and cons?

Pros: It is Transparent. We know exactly why a decision was made. It is great for strict compliance rules.

Cons: It is Inflexible. If the market changes, humans must update the code manually. It cannot handle new, unknown situations.

Artificial Intelligence (AI)

Next is Artificial Intelligence or AI.

Unlike rule-based systems, AI is designed to simulate human intelligence. It can reason and recognize complex patterns.

AI can process massive amounts of data much faster than any human.

AI Analysis

Why do we use AI?

Mainly for Efficiency. AI can automate difficult tasks, like customer service chatbots.

However, there are challenges. AI can be a 'Black Box', meaning it is hard to explain how it thinks. There are also concerns about ethics and bias.

Machine Learning (ML)

Third, we have Machine Learning or ML.

ML is actually a part of AI, but with a difference: ML can learn.

We do not give it strict rules. Instead, we give it historical data, and it finds patterns.

Example: Netflix recommending movies, or banks detecting fraud.

ML Analysis

The main strength of ML is Adaptability. The more data it gets, the smarter it becomes. It is excellent for predicting future trends.

But remember, ML depends on Data Quality. If the data is bad, the result is bad. We call this: Garbage In, Garbage Out.

Comparison Table

So, what is the key difference? Look at this table:

• Rule-Based: Focuses on Control. Good for consistency.
• AI: Focuses on Automation. Good for speed.
• ML: Focuses on Learning. Good for prediction.

The Hybrid Approach

Which one is the best? The answer is: Combine them.

As you can see on the screen: The true strength lies in balance. We need rules for safety, but we need AI and ML for innovation."

Conclusion

To conclude, the best strategy is integration:

1. Use Rule-Based for strict protocols.
2. Use AI to speed up routine tasks.
3. Use ML to predict the future. This way, the company remains stable but ready for growth.