What are these jargons really; Machine Learning vs. Rules-based Testing? Pretty alien, huh? So let’s clear our basic concepts first. Artificial intelligence is a pretty vast field with numerous approaches to implement it. Predominantly, it is distinguished between machine learning and rule-based techniques.
To put it plainly, a system that fetches artificial intelligence through a rule-based method is termed a rules-based AI system. At the same time, a system that achieves artificial intelligence through a machine-learning process is known as an AI learning system. Pretty simple, right?
But you’d still be curious to know the meaning and difference between these two approaches in detail. Let us spell it out for you…
What is Machine Learning in Artificial Intelligence?
It is an application of artificial intelligence that enables the system to learn and augment the experience without being given directions. Its core focus is on computer development to make them automatically access data and learn from it. This is made possible by observing a large quantity of data in different forms like examples, direct experiences, or instructions, ultimately enabling computers to learn all by themselves without human assistance.
What is Rule-Based Testing in Artificial Intelligence?
It is a system that mimics human intelligence. It follows human-made rules to sort, store and even manipulate data. Fundamentally, it needs a source of data or a set of facts and directions to work. All in all, it is a logical system that employs predefined choices to perform actions. It works by comprehending the ‘if statements.’ For instance, if X happens, perform the Y function.
But How is Machine Learning vs. Rules-Based Testing Different?
Now you are familiar with Machine Learning and Rule-based testing in artificial intelligence. However, you’d still be wondering how Machine Learning vs. Rules-Based testing are different from each other. Let’s get right into the differences listed below;
- Machine learning demands more data in comparison to rules-based systems in AI. While simple data and information suffice for the workings of rule-based testing in artificial intelligence, machine learning needs plenty more data to function like full demographic details.
- The machine learning model is scalable. It means if, for example, we have data of some cities along with several Mexican restaurants in it, machine learning can do a great job in suggesting Mexican restaurants in a town. On the contrary, it would be too tricky for a rules-based testing system to recommend Mexican restaurants in a particular city since it is not scalable by nature.
- In Machine Learning vs. Rules-based testing comparison, Machine learning systems are probabilistic in nature, whereas rules-based systems are deterministic. It implies that if you ask your system about an employee’s loan repayment, the probabilistic approach will apprise you of all the insights based on statistical rules. For instance, how much loan is he likely to pay, how much is he likely to commit fraud, etc. On the other hand, the deterministic approach of rule-based testing in artificial intelligence will follow all the directions and rules to conclude whether he will pay the loan or not.
- Another major difference between Machine Learning vs. Rules-based testing, Machine learning is a mutable model—objects whose state can be modified—that enables organizations to transform value and data by utilizing ever-changing programming languages such as Java). When it comes to rules-based systems, they are strictly immutable where changes in outputs and fields are very hard to change.
Machine Learning will be Utilized When;
- Simple guidelines do not apply
- Pure coding process
- Pace of change
Rules-based will be Utilized When
- Danger of error
- Speedy outputs are needed
To Sum it Up
Both, Machine Learning vs. Rules-Based testing, have their distinct advantages and disadvantages, but principally, it depends on the situation which method is needed for business development. On the whole, machine learning is best for the long run as it can adapt to constant changes through data preparations and algorithms.