Top Artificial Intelligence Algorithms: Types, and How They Work

Artificial Intelligence (AI) is not just a buzzword these days, it has changed a number of processes in a short span. Close to 35% of businesses worldwide have already adopted AI in some capacity. The proliferation of tools like ChatGPT and Bard has given rise to process automation. Nearly anything and everything can be handled through the deployment of such sophisticated tools. To understand the way these tools work, one must understand Artificial Intelligence Algorithms.  

Stay tuned as this blog will help you understand the way Artificial Intelligence Algorithms work. You’ll also stumble upon the types and the places they’re deployed.   

Table of Contents 

1) What are Artificial Intelligence Algorithms? 

     a) How do Artificial Intelligence Algorithms work? 

2) Artificial Intelligence Algorithms: The categories 

3) Types of Artificial Intelligence Algorithms 

     a) Classification Algorithms 

     b) Clustering Algorithms 

     c) Regression Algorithms 

4) Conclusion 

What are Artificial Intelligence Algorithms? 

Simply put, Artificial Intelligence Algorithms are nothing but a set of rules that guide an AI to perform an action. The absence of an algorithm will send the AI into dormancy or an inactive state. An AI algorithm not only helps the AI perform basic functions but also helps it understand and learn from the actions performed. With the passage of every test run, the AI’s understanding of the environment will evolve. 

AI algorithms, for the most part, detect patterns in the actions performed by the system. Once detected, the pattern is then analysed and understood. Upon gaining an understanding, the algorithm then drives AI to make decisions. Bear in mind that the better your test set is, the more well-informed decisions the AI will make.  

How do Artificial Intelligence Algorithms work? 

Understanding the intricacies of Artificial Intelligence Algorithms requires an elementary understanding of the way it works. Depending on the complexity of the algorithm, there can be n number of steps using which an algorithm works. Here’s the general set of processes involved in an AI algorithm’s working:

1) Data collection: Perhaps the most rudimentary stage of an AI’s journey. Here, you begin the process of relevant data collection. Data collection can be done from a number of sources, the prominent ones being sensors, databases, images, text, etc.  

2) Preprocessing: At times, Artificial Intelligence Algorithms require well-structured data sets. Since raw data can contain clutter, cleaning and segmenting it becomes crucial. That’s why preprocessing of collected data is often done just to enrich the data set. 

3)  Feature selection: This process involves extracting certain features or elements from data sets that will help the AI perform better. It is a crucial stage in data collection and cleansing as the particular features in question here can boost the AI’s accuracy. 

4) Algorithm Selection: There are a number of AI algorithms that can help you perform different actions. However, selecting one based on the data set received and the type of operation is the essence of this step. 

5) Training: Now that the data has been collected and cleansed, the AI will then be taught to look for patterns and relations between elements. This will form the model’s basic understanding of the environment it is dealing with. 

6) Testing: Once trained, the AI is then tested using a completely different data set to gauge its capabilities. This stage is essential as it helps understand the AI’s pattern recognition and correlation capabilities. 

7) Optimisation: Obviously, there will be a few mistakes while running the first few tests. Hence, optimisation of the AI here is essential.  

8) Feedback Loop: All AI algorithms need feedback to improve. This helps the system to evolve and get better at establishing correlations.  

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Artificial Intelligence Algorithms: The categories 

The usage of AI in different realms has led to the establishment of certain requirements if you’re learning about its intricacies. One crucial requirement is to understand the categories that makeup Artificial Intelligence Algorithms. AI algorithms is a sort of blanket term that encompasses a number of algorithms. Here’s a list of algorithm categories that’ll help you understand the overall composition: 

1) Machine Learning Algorithms 

2) Deep Learning Algorithms 

3) Natural Language Processing Algorithms 

4) Computer Vision Algorithms 

5) Optimisation Algorithms 

6) Clustering and Dimensionality Reduction Algorithms 

7) Recommendation Algorithms 

8) Ensemble Algorithms 

9) Anomaly Detection Algorithms 

10) Recommender Systems Algorithms 

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Types of Artificial Intelligence Algorithms 

For ease of understanding, let’s expand upon the following categories to highlight the types of Artificial Intelligence Algorithms: 

1) Classification Algorithms 

2) Clustering Algorithms 

3)  Regression Algorithms 

Classification Algorithms 

As the name suggests, Classification Algorithms perform classification on the data sets available. They create cohorts as per the operation’s requirements and then work further on understanding the created classes. Since they fall into the supervised learning cohort, they’re frequented by human intervention. Simply put, Classification Algorithms understand and create categories of the data sets they receive as input. 

The following algorithms form the superstructure of Classification Algorithms: 

1) Naïve Bayes: This algorithm takes a probabilistic approach toward problem-solving. It works on Bayes theorem and is usually equipped with a set of previously recorded probabilities. Upon ingesting data, the algorithm updates the set of pre-recorded probabilities to form a new one. This is then used to predict what class an element will fall into.
 

2) Decision Tree: Another subset of Artificial Intelligence Algorithms, a Decision Tree breaks down a big problem set into smaller ones. Here, the nodes represent a decision, while the branches represent the outcome of a test.  

3) Random Forest: A Random Forest is the culmination of multiple Decision Trees. This combination is done in order to get better at obtaining probabilistic values. The better the values are, the more accurate the predictions will be.  

4) Logistic Regression: This algorithm is used for the binary classification of problems/tasks. It works by establishing a relationship between the independent variables and a binary dependent variable. Once established, it then estimates the probability of a class being true or false.  

5) Support Vector Machines: The Support Vector Machines or SVMs help in classification and regression tasks. They’re effective in situations where there are no clear linear separations between classes. SVMs find a hyperplane that best separates classes to get the job done. 

6) K Nearest Neighbors: This algorithm works by making predictions based on the closeness of data points to query points. It is an instance-based learning algorithm.  

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Clustering Algorithms 

Clustering Algorithms stem from Unsupervised Machine Learning Algorithms. As the name suggests, their job is to create clusters of patterns, similarities, etc. They form a crucial component of Artificial Intelligence Algorithms and are used in segmenting data, identifying relationships, etc. Given below are some of the commonly found Clustering Algorithms: 

1) K-Means Clustering: It is one of the simplest algorithms to perform clustering. What it does is it calculates the position of a centroid and then calculates the distance of all data points from the centroid. Once done, it then assigns the tested data point to the closest cluster.  

2) Fuzzy C-Means: Just like the aforementioned algorithms, this one works on probability as well. Clusters are considered rather than created, which is why the algorithm is called “Fuzzy.” Data points here don’t have an absolute relation with a particular cluster.  

3) Expectation-Maximisation (EM): Expectation Maximisation is used in statistical tasks. Commonly used in clustering, missing data handling, and density estimation, the algorithm is used to calculate the maximum likelihood estimation. It works using the Gaussian Mixture Model. 

4) Hierarchical Clustering: This algorithm is yet another important component of Artificial Intelligence Algorithms as it helps in clustering similar data points. It produces a tree-like structure comprising clusters called a dendrogram. The dendrogram shows relationships between data points and other clusters. 

Regression Algorithms 

Regression algorithms are the ones capable of predicting the output based on the input provided. They’re heavily used in weather prediction, stock market prediction, etc. Among other Artificial Intelligence Algorithms, Regression Algorithms are relatively easy to use. The following are the types of Regression Algorithms: 

1) Linear Regression: Linear Regression is one of the fundamental Machine Learning Algorithms. It works by establishing a linear relationship between input features and the target variable. It finds the best linear equation that helps in accurately predicting the target variable based on the input features.  

2) Lasso Regression: The “Lasso” in Lasso Regression stands for least Absolute Shrinkage and Selection Operator. It works by adding a regularisation term to the objective function. A key feature of Lasso Regression is its ability to push down low-priority elements to zero. It only keeps the elements that aren’t pushed to zero in your linear equation.  

3) Logistic Regression: Logistic Regression comes in handy in situations where elements come equipped with uncertainty. It plots a probability curve by using variables and multiplying them with weights. The higher the value reaches on the curve, the closer the probability is to 1 and vice versa. This curve is called a sigmoid curve.  

4) Multivariate Regression: Multivariate Regression is an evolved form of Linear Regression in which it predicts the output by ingesting more than one input. This algorithm also considers the concept of weights, and it correlates weights to different elements. What it does next is that it finds the right balance between the input elements and the weights to generate a prediction.
 

Conclusion 

When you consider Artificial Intelligence Algorithms and the way they work, it becomes a lot easier for you to understand AI. With every iteration, AI models across the globe are growing more complex. Understanding the basics of Machine Learning, the Type of AI Models, and their associated algorithms is crucial as it’ll help you develop an understanding of AI. Once equipped with the right know-how, you’ll not only be able to spot and correct errors, but you’ll also be able to create models of your own. AI has been around for quite some time, but its potential has only recently been tapped into.  

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