AI vs Traditional Programming When Rules Are Learned Not Coded
In the world of technology, programming has always meant writing explicit rules that tell computers exactly what to do. Artificial intelligence changes this by enabling machines to learn rules from data instead of having them coded by humans. This article explains how these two approaches differ and what it means for businesses and technology today.
The Classic Approach: Rule-Based Programming
Traditional programming relies on developers creating clear and precise instructions. Each step and rule is spelled out in the code for the computer to follow exactly. This method works well for straightforward tasks like calculating prices or verifying passwords where the rules do not change much.
However, when problems become complex and unpredictable, such as understanding speech or identifying images, it becomes difficult to write every possible rule. The limitations of this approach became clear as data and applications grew in scale.
AI and Machine Learning: Learning Rules From Data
Artificial intelligence, and especially machine learning, solves complex problems by learning patterns and rules from large amounts of data. Instead of programmers defining every rule, AI models train on examples to make decisions or predictions.
For example, in retail, AI can learn from massive sales and browsing data to predict future customer behavior. Voice assistants understand spoken language by learning from thousands of hours of speech rather than being given fixed phrases.
Benefits and Trade-Offs of Both Approaches
Rule-based programming is predictable and easy to explain. You know exactly what will happen because everything is explicitly coded. On the other hand, AI models can feel like black boxes. Their decisions come from patterns in data that may not be obvious to humans.
Choosing between these approaches depends on the problem. For stable environments with clear rules, traditional programming is often best. For messy, changing, or high-volume data tasks like fraud detection or product recommendations, AI's learning methods shine.
Maintaining and Improving Systems
When business rules change in traditional programming, engineers update the code. AI systems adapt by collecting new data and retraining models. This creates feedback loops where AI improves over time as it receives new information.
For instance, predictive maintenance in manufacturing uses sensor data and AI models to foresee machine failures before they happen. The models get better as more data is collected, a capability static programs lack.
Combining Rule-Based and AI Approaches
Many real-world systems blend both methods. For example, a loan approval process may use strict coded rules for eligibility and AI to assess risk factors more subtly. Similarly, healthcare systems use rules for regulatory checks and AI to assist in diagnosing diseases from medical images.
This hybrid approach leverages the predictability of rules and the flexibility of AI, delivering powerful and adaptable solutions.
Understanding these differences helps businesses choose the right tools for their challenges. It is not always a choice between old or new, but how to best combine both to achieve success.
If you want to dive deeper into how data shapes AI models and how to prepare your data for success, listen to Episode 30 of 100 Days of Data titled AI vs Traditional Programming. Join Jonas and Amy as they explain these concepts with examples and practical insights.
Member discussion: