AI Rule-Based vs Machine Learning Approach for Development

1. AIRule-BasedvsMachineLearningApproachforDevelopment AIand machinelearning are two ofthemostcommonlymisunderstoodterms 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 foryourbusinesscan bea difficultone. Rule-based AI is often used for smaller tasks while machine learningevolves 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 intelligencesoftware would work best for your company! Whatisthe Rule-basedAIapproach? 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 anantivirus program that scans for known maliciouscodeor files beforetheycan affectyourcomputer. What is Machinelearning? Machine learning is a type of Artificial Intelligencethat 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-andimproveitsperformance inareas such asclassificationaccuracyonunseen materials,naturallanguageprocessingforformsofcommunicationwith users,andeven 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 canbe particularly useful when there isn’t anabundance ofinformationabout howsomethingwill work orthe results. Oneexample would be Google’s DeepMindwhich was created to play Atari games at an expert level after beingtrainedonlyusingrandominputs.

2. KeyDifferentiatorsbetween Rule-basedAIand MachineLearningmodels Machine learning has many advantages over rule-based algorithms when dealing with more complexdatasets;however,bothtypeshavetheirownindividualstrengthsthatmaymake themsuitabledependingonthesituation: 1. Probabilistic andDeterministicModels 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 withdatathathascharacteristicssuchasmulticollinearityandnonlinearrelationshipsitisbest to use machine learning algorithms in order to apply more complex solutions. Rule-based AI makestheassumptionof linearitywhich doesnotaccount for thesecomplexities. 2.FeedbackControl Machine Learning uses statistical analysis and estimation techniques to make predictions by creatingcorrelationsbetween variables(i.e.,inputs)andoutcomes(i.e.,target).Machine Learning can alsoincorporatesomeleveloffeedbackcontrolfromobserved resultswhich improves its predictive ability overtime throughthe useofa hypothesistest. Rule-based AI doesnot have this ability to feedback controlbecause its goal is to identify the bestrulefor inputand apply itin ordertoachievespecificoutputs. 3.ProjectScale 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 datasetorproblemspacebutrequires more resourcesthan rule-based AI (i.e., time,money). 4.Datarequirements Rule-based AI doesnotneed a large dataset and canoperatewithonly a fewexamples. 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 resultswillbe. 5. FunctionalProgrammingLanguage Rule-based AI is created using a functional programminglanguage such as Lisp or Prolog, while machinelearninguses aproceduralprogramminglanguage.Though thesyntaxofthese languages is different, they use similar logic to solve problems and create predictions because bothrelyon rulesthat dictate whatwill happennextin responsetoinput data.

3. 6.ProcessingTime Machine Learning has an advantage over rule-based AI whenit 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 Algorithmsbut not MachineLearning. 7. MutableandImmutableData 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.MachineLearning andRule-BasedAlgorithms arenotcompetitors—theyboth have strengthsin different fields.The best solutionisone that fits yourcompany’s needs.