How to choose between a rules-based vs. machine learning system Posted on : Jul 24 - 2020

Debating rules-based systems over machine learning comes down to the complexity of the task at hand. Machine learning dominates complex tasks, but requires more long-term expertise.

For organizations creating algorithms and implementing systems, choosing between rules-based vs. machine learning-based systems is critical to the usability, compatibility and lifecycle of the application.

Getting outputs from a rules-based system can be a simple and nearly immediate application of AI, but an investment in machine learning can handle complex tasks with great speed. Enterprises must understand the core differences between the two, their individual benefits and the limitations of both before taking advantage of either.

Rules-based vs. machine learning

At the core of these two examples of AI, the logic and rules on which the systems or algorithms operate is what differentiates them. For rules-based systems, the logic that the system operates on is instilled at the beginning with little flexibility once deployed. First, a list of rules is created, often by an in-house developer, then an inference engine or semantic reasoner performs a match-resolve-act cycle, measuring information that it takes in against these rules. Here, human knowledge is encoded as rules in if-then statements for a specific rule. For example, in a rules-based algorithm or platform, a bank customer's personal and financial information can be measured against a programmed set of levels, and if the numbers were to match, then a home loan would be granted.

"In this scenario, the customer's information passes through an analysis process created by a human and built upon business rules provided to the developers," said Gus Walker, senior director at San Francisco-based AI tech company Veritone.

On the other end of the spectrum lies machine learning, where rather than the algorithm being hand-coded by a human, it's created by selecting an appropriate AI model and presenting it with a very large dataset, Walker said. The algorithm then analyzes the dataset and determines relationships within that data; logic is embedded in the algorithm and was not coded by a human. As referenced in the name, the model trains itself and learns from the data, creating a cohesive relationship between data inferences and future data outputs.View More