The implementation of the correct AI strategy is critical for the development and productiveness of an organization.
Rule-based systems and machine learning models are widely used to reach the desired outcomes. Both of these approaches have advantages and disadvantages. While the rule-based approach is a form of predetermined AI, machine learning solutions involve large training datasets for cognitive learning. Businesses are therefore investing in the right AI technique to generate optimum value, and employees must be aware of the nuances and approaches to implement AI. How to know what approach to use when designing systems?
The data science or IT professional who takes a PG in AI and Machine Learning will be better equipped to know what technique to implement. A Post Graduate diploma provides you practical training of large datasets and a deep insight into best practices. This blog is a guide for learners to debate rule-based artificial intelligence vs. learning-based methods.
The demand for artificial intelligence developers is increasing, and certification provides just the right opportunity to be industry-ready.
What is Rule-based AI
A rule-based system is used in AI applications and research to store and manipulate knowledge and interpret information in a useful way. It is applied to systems where rule sets are human-crafted or curated. The rule-based approach uses the principle of solving complex problems by reasoning through bodies of knowledge represented through if-then-else rules. These systems emerged in the 1970s and 1980s. Developers were encoding human knowledge into computer systems as rules and storing it in a knowledge base.
Today the rule-based AI models have a “set of rules” and a “set of facts,” and these two components are fundamental for building a basic AI model. A rule-based system such as a production system and expert system uses rules as the knowledge representation. These rules are coded into the system utilizing the rule of if-then-else statements. The underlying objective is to capture the knowledge of a human expert within a computer system.
Rules-based systems are lower effort but more cost-effective. Implementing rule-based systems is less risky since the rules don’t change or update on their own. However, there are limited AI capabilities as the intelligence is “fixed” to the knowledge base and can only do what it has been written to do.
Rule-based systems are the simplest form of AI with limited ability to simulate intelligence. It is limited by the size of the underlying rule-base rooted in the fixed human knowledge base. So, rule-based systems are not useful for solving problems that are complex or split across multiple domains. Besides, in some situations like cancer detection from medical images, rules cannot be explicitly defined programmatically. That’s when the learning system comes into play.
Machine Learning for development
Rule-based machine learning (RBML) refers to the method that identifies, learns, or evolves ‘rules’ to store, manipulate or apply the contextual knowledge captured by the system.
It is a type of rule-based system that applies learning algorithms to automatically identify useful rules, rather than a human applying prior domain knowledge to manually build a ruleset.
In the machine learning approach, the system defines its own set of rules based on the patterns it sees in the data. It is an alternative to some of the challenges of rule-based systems. The machine learning system constantly evolves and adjusts, taking a probabilistic approach and defining its own set of rules based on the data outputs.
In contrast to rule-based systems, learning systems implement AI through the learning capability of the systems tapping its ability to simulate intelligence. It means that existing knowledge can be changed or discarded and new knowledge acquired during the learning process, and why these systems build the rules on the fly. For instance, in learning systems such as deep neural networks, optimizing the utility function is achieved through traditional optimization techniques.
Difference between Rule-based AI and Machine Learning
The rule-based system uses a mix of human knowledge written into rules. Machine learning uses automation of the rules process.
Rule-based systems rely on clearly written and fixed models of a domain. Learning systems generate their models.
Learning Models
A rule-based system will analyze the inputs and rules to predict whether a given output can be achieved or not. On the other hand, a machine learning solution will learn from the user inputs to predict the best possible outcome for a certain condition. Thus, the learning model to be used, deterministic or probabilistic, decides the use of the approach. The rule-based system is deterministic, whereas the machine learning approach is probabilistic.
Usage
The use case in enterprise AI solutions differs for rules-based AI and machine learning. For instance, a rules-based system is used in software testing, manufacturing process automation or to play an online game. Machine learning solution is used in fraud detection, recommender systems, speech recognition, and virtual assistant, where the machines learn iteratively.
Outcome
Rules are specified in a rule-based approach, so the outcome can be predicted or even updated if one has access to it. In machine learning solutions, the models work independently, and so the outcome is not known. The output also differs for each use case.
Data used
The data required in a rule-based system is generally low as it solely works on the parameters provided. However, machine learning solutions work with a huge dataset.
Flexibility
In spite of many smartest features, rule-based systems are often less flexible than machine learning solutions. In a rule-based approach, the system works as per the initial rules specified by the user. In the case of machine learning, that is not so. The machine learning models continuously learn from the user inputs and upgrade themselves with each use case.
Intelligence
While a rules-based system has “fixed” intelligence, a machine learning system is flexible in its bid to simulate human intelligence. The underlying rules are learned by the machine on its own, and those that do not work are discarded.
Project scale
Rule-based artificial intelligence developer models are not scalable. However, machine learning systems can be easily scaled.
Application
The rule-based approach is generally applied when there is a fixed number of outcomes, or when there is the danger of error and the penalty is too high to risk false positives, or for speedy outputs.
On the other hand, machine learning is applied when there is no easy way to solve a task using simple rules, or when situations and data are changing fast, or for tasks that require natural language processing or pure coding processing.
Conclusion
Machine learning and rule-based models have their advantages and disadvantages. It depends on the situation which approach is appropriate for the development of business.
By taking a PG certification in AI and machine learning, you learn the differences between the two and the potential of each approach.