As applications and computing systems are increasingly getting intelligent and responsive to human actions thanks to sophisticated technologies like artificial intelligence (AI), machine learning (ML), and a variety of complex rule-based algorithms, applications have multiple choices for empowering their capabilities. While there were always rule-based algorithms that work on the basis of preset rules and conditions, the AI and machine learning systems now can take instant decisions based on user inputs and the user context.
So, what should you choose rule-based algorithms or systems, or the AI-based intelligent ones? Well, both of these choices have their own merits. Here we are going to explain both of these approaches and their individual merits and demerits.
A rule-based algorithm always performs actions based on some preset rules and conditions. These rules can be very simple or utterly complex ones. Because it is restricted to respond within the scope of preset rules and conditions, any new question or condition that it has no clue about cannot be addressed by such algorithms.
On the other hand, because of their simplicity and the straightforward approach, they can be widely used for a variety of applications and purposes. From simple apps to websites to many systems that need to respond in a binary method may find the rule-based approach effective and useful for their purposes.
Let us now mention the pros and cons of the rule-based approach.
- The rule-based approach is easy to implement and integrate into the systems.
- The rule-based approach is straightforward, and this makes it ideal for simple applications.
- Rule-based algorithms have already been used in too many systems, and hence they have an industry standard of best practices to follow.
- The rule-based approach is less expensive and less time-consuming.
- If you want to introduce simple Chatbots for FAQ or similar section, a rule-based approach proves to be very effective.
- The rule-based approach has very limited capacity in terms of addressing complex problems and queries.
- The rule-based approach is static and hence cannot be improved or updated much.
- Rule-based approaches may not serve the purpose of applications that are trying to address specific user contexts.
- For complex and multifaceted applications, the rule-based approach can be very limited.
Artificial Intelligence (AI) Based Systems
In complete contrast to rule-based systems, AI-powered intelligent systems are more context-driven and responsive as they interact and make decisions based on user information and user interactions. Machine Learning technology, which is a subset of artificial intelligence, actually goes one step further and allows the systems to learn from the user interactions, responses, and activities and accordingly make decisions. Unlike rule-based systems that are static, AI-based systems are continuously updated and more powerful with new user inputs and information.
Let us now have a look at the key pros and cons of AI-based systems.
- AI-based systems make machines and applications intelligent and more user-focused in their output.
- AI-based systems help applications to be more responsive and context-driven for specific use cases.
- As AI and ML systems try to adapt to personal user preferences, there is a bigger and better scope of personalization.
- In the years to come, AI-based systems will continue to grow, become more popular, and become more capable.
- AI-driven systems are now enjoying the powerful backing of the world’s leading tech companies.
- This is just the beginning of AI-based systems, and so there is no global standard to evaluate their output.
- AI-based systems are expensive in terms of development cost and time.
- AI-based development needs expertise in different technologies.
- Small businesses and startups may find it harder to integrate AI systems.
- AI and Machine Learning models need to be trained frequently with new data and data-driven insights.
Rule-based System vs AI-based System: Which Method Should I Use?
Now coming to the question of making a choice between these two approaches, we must admit that there is no straightforward answer, and there are several factors for influencing this choice. The rule-based systems can be designated as clumsy and limited in terms of scope. In contrast, AI-based systems are more versatile and multifaceted in nature.
The AI-based systems are increasingly growing in influence and presence simply because of the opportunity they offer in addressing user concerns more specifically. Taking this advantage one step ahead, Machine Learning allows optimizing the systems even more by learning about the consumers. This also ensures a more personalized user experience compared to rule-based systems. But at the same time, these solutions take longer development time and a bigger development budget in comparison.
Rule-based systems, on the other hand, can deliver fast-paced and instant solutions for user query handling. When you need to provide a simple automated solution based on binary condition, the rule-based approach will always seem to be ideal. But for complex solutions focused on individual needs and a variety of conditions AI and ML-based approaches will be more appropriate.
Algorithms are almost everywhere. From Chatbots to simple connected devices and mobile apps, they have penetrated every technological space. With the proliferation of intelligent Chatbots, AI-based systems are likely to enjoy more popularity, but at the same time, rule-based systems will continue to be present for certain purposes.
About the author
Paul Osborne is Chief Technology Officer at Cerdonis Technologies LLC mobile app development company providing a wide range of services of UI/UX development, Future technology development, digital marketing, app/web development. He is a keen reader & is always curious to know about new technologies.