Differences Between Rule Based and Machine Learning AI

A number of companies across industries are now exploring and implementing Artificial Intelligence (AI). The subject range from mountains of data to robotics, which then penetrates to automate business processes, improving customer experience, and innovation to develop better products.

According to McKinsey on their website, “AI promises huge benefits to businesses and economic through its contributions to productivity and growth”. But behind all of that there will always be challenges.

Computers and machines didn’t come to this world with inherent knowledge. Like humans, they need to be taught that red light means stop and green means go. So, how do these machines actually get the intelligence? They need to perform tasks like driving cars or diagnose a disease.

There is a lot of ways to obtain Artificial Intelligence, and the most important part is developing it’s data. Without high quality data, Artificial Intelligence is just an illusion. There are two ways data could be manipulated. Whether through Rule-Based or Machine Learning. And a couple of exercises could help you choose between these two methods

Rule-Based System
Long before AI and Machine Learning became general terms in the technological industries, developers coded human knowledge into the computers system as rules stored in the knowledge base. These rules defined all aspect, usually in the form of  an “If”. For Example: “If A, then do B, If not C, then do D”.

While the number of rules to write depends on the number of actions you want the system to handle. For example, 20 actions write and manually code at least 20 rules.

A rule based system in general has a low performance, more effective but risky because these rules won’t change or renew itself. However, rules can limit AI capabilities with rigid intelligence and can only do what the owner has done.

A fixed result. For example, there are only two states that the “add to cart” button could be, whether its pressed or not.

The risk of error is quite high. There is risks of false positive, because it can only read 100% Positive or 100% Negative, according to what has been applied.

Machine Learning System
In machine learning, AI is created with and ability to adapt to the situation and able to simulate human like knowledge. There are still layers of rules that its based on, but the machine will be capable to learn new rules and discard rules that no longer function.

In practice, there is a couple of ways machines could learn, but the training must be supervised. Generally the first step to this program is to give data for the machine to practice with. In the end, machine would be able to interpret, categorize, and perform other task with un-labelled data or information unknown to the owner.

Normal rules doesn’t apply, which means rules that are rigid and standard like from the Rule-Based system won’t be used. The changes would be quick, it will be updated as situations, scenarios, and data changes, and would also continue making new rules.

Summary
Well, those are examples of Rule-Based and Machine learning system differences. The benefits that can be anticipated are high, so the decisions a company makes early in its implementation could prove to be critical to success.

Aligning your technology choices with business goals can underpin what is going to be done, going forward. What problems you face or what hurdles awaits you. The choice to implement Rule-Based system or Machine Learning will have a long term impact on how the companies AI will develop and scale.

To many AI developers, the challenges is when they start the AI development itself. If you are an AI developer, start by determining which method will you use, ruled-base or machine learning. Use the method that works best for you.

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