Rule-based systems and machine learning models are widely used to conclude the data. However, both methods have advantages and disadvantages over each other. Several companies use and evaluate activities related to artificial intelligence to conduct business processes, promote product development and improve the market experience. 

This blog provides some essential points to consider before investing in any strategies. Proper AI strategy is critical to business development. Emerging technologies such as Machine Learning and Artificial Intelligence contribute significantly to development and production. The machine learning certificate gives you a deeper understanding of the field. In addition, this blog provides a business guide to challenge machine learning with cleverly constructed rules. 

Rule-based programs

Long before Artificial Intelligence (AI) and Machine Learning (ML) became commonplace outside of high technology, engineers wrote human information into computer systems as rules stored in the knowledge base. These rules describe all aspects of the work, usually in the form of “If” statements (“if A, then B, if X then Y”).

While the number of rules to be written depends on the number of actions you want the program to handle (for example, 30 actions means handwriting and coding at least 30 sets of rules). This ruled-based system usually requires less effort and is very effective and riskless because these rules will not change or update themselves. However, these rules can limit the power of AI with vital intelligence that can only do what it is designed to do.

How machine learning program helps us?

Machine learning is another way to help deal with some of the problems in legal ways. However, instead of mimicking a professional decision-making process or good practice, machine learning methods often take only professional results.

For example, an insurance specialist can review many cases and decide whether they are fraudulent or not. Exactly how an expert came to their decision is not crucial in machine learning. Their conclusion was that focusing on the results rather than the whole decision-making process can make machine learning more flexible and less risky in some problems encountered by law-based programs.

What is the difference between your two benefits?

Both mechanical and software-based learning aims to make decisions change with the highest levels of accuracy. For example, legislative-based systems always provide output exactly what is planned, which is advantageous when incoming data is very stable but is a problem in a dynamic environment. On the other hand, machine learning algorithms often have much lower accuracy than rules based on rules until they learn from sufficient data.

This does not need to be / or selected, however. The most accurate solutions often include systems based on rules and machine learning. For example, one of our clients in advertising involves editing responses automatically to proposal requests (RFPs). The rule-based system filters the library of answers to previous RFP questions to the appropriate RFP provided (depending on country, industry, etc.). The machine learning algorithm is then used to predict the best answer to each question within the sorted library.

Integrating law-based systems and machine learning enables each method to make up for the shortcomings of the other. 

Legislative-based systems can 

• Make transparent decisions, 
• Provide security mechanisms to avoid unintended consequences, and 
• Make informed decisions before collecting data. 

Machine learning can enhance these systems by improving accuracy over time and responding to changes in the environment.