Rule-Based Artificial Intelligence and Machine Learning

Across industries, companies are implementing and exploring AI (artificial intelligence) projects. These projects include everything from big data to robotics. These projects intend for AI to innovate product development, automate business processes, and improve customer experience. According to McKinsey, embracing artificial intelligence promises considerable benefits for economies and businesses. Its contributions to growth and productivity will be very useful. Nevertheless, with that promise, challenges may face us soon.

Machines and Computers do not come into this world with inherent knowledge. Moreover, neither do they understand how things function. Like humans, they must learn that a green light means go, and a red light means stop. Thus, a question may occur to us: how do machines gain the intelligence they need for carrying out tasks like diagnosing a disease or driving a car?

There are several ways to achieve artificial intelligence. Data is essential to them all. Artificial intelligence is a pipe-dream without quality data. From there, you can manipulate data in two ways, either through machine learning or through rules. Those are the ways to achieve artificial intelligence. There are some practices which can help you with choosing between those two methods for the best results.

Developers used to encode human knowledge into computer systems as rules that get stored in a knowledge base, long before ML (machine learning) and artificial intelligence became mainstream terms outside of the high-tech field. It usually happens in the form of “If” statements and those rules define all the aspects of a task.

How Artificial Intelligence Studies

The number of rules that must be written depends on the number of actions you want a system to handle (for instance, 15 actions means manually writing and coding at least 15 rules). Since those rules will not change or update on their own, generally, rule-based systems are lower effort, less risky, and cost-effective. Nonetheless, rules can limit the capabilities of artificial intelligence with rigid intelligence that can only do what they were intended to do.

Thus, a rule-based system has “fixed” intelligence. So, in contract, the machine learning system attempts to simulate human intelligence and is adaptive. Still, there is a certain layer of underlying rules. Nevertheless, instead of a human writing a fixed set, the machine can learn new rules on its own. Moreover, it can discard regulations that no longer work.

There are some ways a machine can learn in practice. Supervised training is generally the first step in a machine learning program. At this point, the device has data to train with. Thus, the machine will be able to categorize, interpret, and perform other tasks with unknown information or unlabeled data on its own.

The anticipated benefits of artificial intelligence are high. Thus, the decisions a company makes early in its execution might be critical to success. Foundation aligns your technological choices to the underlying business goals that artificial intelligence wants to achieve. What challenges are you trying to meet, or what problems are you trying to solve?

The decision to implement a machine learning or rule-based system will have a long-term impact on how a company’s artificial intelligence program scales and evolves.