Difference Between Traditional AI and Machine Learning

When people think about Artificial Intelligence (AI), it is natural to associate it with Machine Learning algorithms that learn from data. However, this was not always like that. Before Machine Learning became a mainstream way to build AI systems, a significant portion of them relied on explicitly defined rules, logic, and knowledge that the machine could use. Thus, the key question becomes: what is actually different between traditional AI and Machine Learning?

Traditional AI (symbolic, rule-based, or GOFAI) is built mostly relying on explicitly defined knowledge and logic. Instead of learning a pattern from thousands of examples, we instruct the machine to follow certain rules. For example, we could construct a system that would help to identify whether the email is spam or not. A traditional approach could have a set of rules such as whether there are any certain words in the email, whether the sender is unknown, whether the email has suspicious patterns and so on. Based on the rules, the system makes a decision. It works great if the problem is clear and its rules are known. However, this is not the case when the problem is too complex to be expressed in rules.

Machine Learning changes the question from: "What rules should I program?" to: "What can the model learn from the data?" Instead of writing explicit rules, we provide examples and let the algorithm learn the patterns from them. So, to solve the same problem with Machine Learning, we don't write down explicit rules. Instead, we provide a number of emails and labels for them (spam or not). Based on this, the model learns the patterns in order to make a prediction. This is one of the key differences between the two approaches.

No, it doesn't. Both traditional AI and Machine Learning are just different approaches to solving intelligent tasks and both of them can be helpful in their particular cases. Rule based systems can be more intuitive when their logic is well understood. On the other hand, Machine Learning becomes helpful when the patterns are hard to define manually, but enough data is available to train a model on. AI systems can even use several approaches combined. We could create a solution that uses a learned model for prediction and traditional rules for validation or decision logic.

The explanation of the difference between the two approaches helps to understand how AI evolves. While traditional approaches relied heavily on knowledge representation, rules, logic, and search, Machine Learning changed the focus to data, statistical patterns, optimization, and learned representations. But there is more to come. Modern Deep Learning and Generative AI systems rely on Machine Learning concepts and push the idea of learning from data even further. For me, the answer to the question is important, as before learning how modern AI works, it is interesting to understand how we got there.