AI Rule-Based vs Machine Learning Approach for Development
                     AI Rule-Based vs Machine Learning Approach for Development
AI and machine learning are two of the most commonly misunderstood terms in business  
today. They both have a lot to offer, but not all job functions are well suited for either AI or  
machine learning development. Some jobs can be improved with rule-based AI while others  
work better with machine learning algorithms. Deciding to choose either machine learning or AI  
for your business can be a difficult one.
Rule-based AI is often used for smaller tasks while machine learning evolves as it does more  
tasks. It's important to remember that rule-based AI and machine learning are not mutually  
exclusive; rather they have different strengths and weaknesses in their applicability to various  
types of applications. In this blog post, we will compare these two approaches so you can make  
an informed decision about which type of artificial intelligence software would work best for  
your company!
What is the Rule-based AI approach?
Rule-based AI is a computer science approach to developing intelligent systems that can be  
divided into two types of subcategories: symbolic and connectionist. Symbolic AI uses rules  
based on logic, while connectionist approaches use neural networks or other models that are  
loosely inspired by biological processes. Rule-based AIs have been around since the 1960 s, and  
throughout the decades they have been used for a variety of tasks. To make sense of large  
amounts of data, organizations often employ rule-based AI that helps them find patterns and  
trends from larger sets of information. One example is an antivirus program that scans for  
known malicious code or files before they can affect your computer.
What is Machine learning?
Machine learning is a type of Artificial Intelligence that includes algorithms and processes to  
automatically learn from data without any human input. It constantly learns as it accesses more  
data over time, meaning the system can adapt to changing environments - like web pages or  
images - and improve its performance in areas such as classification accuracy on unseen  
materials, natural language processing for forms of communication with users, and even  
customer service interactions. Machine learning approaches rely heavily on pattern recognition  
techniques including artificial intelligence methods such as deep neural networks (DNN) and  
support vector machines (SVM). These technologies can be particularly useful when there isn’t  
an abundance of information about how something will work or the results. One example  
would be Google’s DeepMind which was created to play Atari games at an expert level after  
being trained only using random inputs.
Key Differentiators between Rule-based AI and Machine Learning models
Machine learning has many advantages over rule-based algorithms when dealing with more  
complex data sets; however, both types have their own individual strengths that may make  
them suitable depending on the situation:
1. Probabilistic and Deterministic Models
Rule-based AI models provide a deterministic output for every input, while machine learning  
provides probabilistic outputs. In many cases, this may not be an issue; however, when working  
with data that has characteristics such as multicollinearity and nonlinear relationships it is best  
to use machine learning algorithms in order to apply more complex solutions. Rule-based AI  
makes the assumption of linearity which does not account for these complexities.
2. Feedback Control
Machine Learning uses statistical analysis and estimation techniques to make predictions by  
creating correlations between variables (i.e., inputs) and outcomes (i.e., target). Machine  
Learning can also incorporate some level of feedback control from observed results which  
improves its predictive ability over time through the use of a hypothesis test. Rule-based AI  
does not have this ability to feedback control because its goal is to identify the best rule for  
input and apply it in order to achieve specific outputs.
3. Project Scale
Rule-based AI is best suited for smaller projects and problems where the number of possible  
solutions is limited. Machine Learning has a higher ceiling because it can be applied to any size  
data set or problem space but requires more resources than rule-based AI (i.e., time, money).
4. Data requirements
Rule-based AI does not need a large data set and can operate with only a few examples.  
Machine Learning requires more evidence to make accurate predictions because it is based on  
statistical probabilities of events, so the larger the data set or database, the more accurate its  
testing results will be.
5. Functional Programming Language
Rule-based AI is created using a functional programming language such as Lisp or Prolog, while  
machine learning uses a procedural programming language. Though the syntax of these  
languages is different, they use similar logic to solve problems and create predictions because  
both rely on rules that dictate what will happen next in response to input data.
6. Processing Time
Machine Learning has an advantage over rule-based AI when it comes to processing time.  
Algorithms can be developed more efficiently if there's room for error due to large amounts of  
training data (i.e., noise). A small setup error could cause major consequences with Rule-Based  
Algorithms but not Machine Learning.
7. Mutable and Immutable Data
Machine Learning algorithms are more efficient at using mutable data sets, while Rule-Based  
Algorithms excel with immutable data. This means that Machine Learning is better suited for  
real-time learning and can be applied to a wider range of applications in the Internet of Things  
realm.
Conclusion
As you can see, both Machine Learning and Rule-Based Algorithms have advantages in different  
fields. The key to finding the right solution is understanding what your business requirements  
are. Machine Learning and Rule-Based Algorithms are not competitors—they both have  
strengths in different fields. The best solution is one that fits your company’s needs.