A rule-based system is a method of artificial intelligence that operates through a set of pre-established protocols and decision trees. These systems function using a series of if-then statements and pre-defined algorithms to determine the proper course of action based on the input it receives. The benefit of this approach is that it is straightforward, efficient, and dependable.
In contrast, machine learning systems are AI systems that utilize algorithms and statistical models to gain insights from data and make predictions or decisions. These systems learn from large amounts of data and can continually enhance their performance through experience. Although machine learning systems are more adaptable and flexible than rule-based systems, they can be more complex and require more effort to develop and maintain.
To put it simply, rule-based systems are best suited for situations where the desired outcome can be clearly defined and achieved through pre-set rules. Meanwhile, machine learning systems are ideal for more complex situations where the desired outcome requires the ability to learn and adjust over time.
Model training in AI is like teaching a robot to recognize patterns and make predictions, just like we teach a child to recognize shapes, colors, and objects.
Black-Box AI VS White-Box AI
Understanding the distinction between Black-Box AI and White-Box AI is crucial , as it fundamentally concerns the transparency of AI systems.
Predictive analytics in AI refers to the use of statistical models and ML techniques to analyze data and make predictions about future outcomes.