The 8 Different AI Models You Need to Understand Today

To hear people speak of Artificial Intelligence (AI) today, you might get the impression that it’s a new phenomenon. While there are people who praise AI for making some previously impossible jobs easier to perform, there are others who are blaming the technology for the loss of jobs and privacy. Moreover, there are people who raise ethical questions about the close relationship between machines and human beings.

AI is not a new phenomenon. Perhaps the only reason there’s so much talk about AI today is because of the widespread availability and use of supercomputers. As far back as the mid-20^th century, the concept of replacing humans with machines to perform certain tasks was already well-developed. While the computers available then were limited in ability and also super-expensive, technological advances have given us better-performing and affordable computers.

Today, it’s impossible to imagine an industry that does not utilize some form of AI. Living in the “big data” age, just think about the huge amounts of information that we can now collect and then imagine how we could process it without the use of machines. Going forward, we can only expect the use of intelligent machines to become more widespread, and depending on the industry you work in, you’ll need to identify the AI Model appropriate for your needs. Before discussing the different kinds of AI models you need to know today, let’s first find out what an AI model is.

What Is an AI Model?

An AI model is a software program trained to perform specific tasks. The software program is usually trained using specific data to recognize certain patterns and uses this information to perform the task at hand. AI models are used to solve problems and they use pre-defined algorithms to learn and reason from available data to produce desired results. AI modeling helps in decision-making by producing results that are as good as those produced by human beings.

Today, you’ll find AI models used in almost all industries. The complexity of the AI model used in a certain scenario will vary depending on the complexity of the task. AI is now used for tasks such as face recognition, voice assistance, personalized shopping, writing, fraud prevention, and human resource management among many others.

The Different AI Models You Need To Understand Today

The AI models used today differ depending on the tasks they’re required to perform. Since AI models use algorithms to understand inputs before producing an output, it’s possible for AI developers to use multiple algorithms at the same time.

AI models differ depending on how they are created. AI developers could use several creation methods including supervised learning, semi-supervised learning, and unsupervised learning.  Below are the most common AI models in use today.

#1. Linear Regression

Linear regression is the AI model vastly used in statistics. The model uses known data to predict the value of unknown data. By creating linear equations, developers can accurately predict the value of unknown variables by using the known or independent variable.

In business, linear regression is widely used to provide intelligent insights. By using available (known) data, the business can predict future trends to help it make crucial decisions. Linear regression is the AI model that you’ll find widely used by banks, insurance companies, environmental scientists, and even individuals who wish to find out what the data available foretells about changes in the future. Linear regression falls under supervised learning AI models.

#2. Logistic Regression

Logistic regression is closely related to linear regression and it also falls under the supervised learning models. It is used to calculate the values or probabilities of binary equations. A binary equation has a “yes” or “no” answer.

If, for instance, you wished to find out if a person is likely to suffer from heart disease, you could use logistic regression since there are only two possibilities – yes or no. To determine whether the person you’re analyzing is a possible heart disease candidate, you might look at factors like age, nationality, lifestyle (does he smoke and drink? etc.) These factors are independent variables and they will help predict the outcome or dependent variable.

Apart from the healthcare industry (as in our example above) logistic regression is also widely used in marketing. E-commerce businesses find it useful in predicting the probability of capturing a targeted audience – will the audience like our summer collection? Should we market to the people in country X?

#3. Deep Neural Networks

To understand Deep Neural Networks, it would help to first explain what a neural network is. The term neural network refers to technology that is developed to simulate the activities of the human brain. The human brain is complex and has several layers of neural connections. A deep neural network refers to a network with several neural networks and is, therefore, of considerable complexity.

Since deep neural networks are supposed to work at par with the human brain, they are capable of processing data in the most complex ways. With deep neural networks, you’re not just dealing with simple input and output protocols but are also handling unstructured and unlabeled data. The layers in a deep neural network have specific functions but all of them are meant to work in tandem to deliver results.

Just as human brains differ, so do deep neural networks. A network that has more layers is deeper (and better) than one that has fewer layers. Because of their complexity and resemblance to the human brain, deep neural networks have been applied in situations where they can substitute for human labor without compromising performance.

Several industries have found the use of deep neural networks beneficial. Government agencies use this technology to fish out criminals by using face recognition features. Today, you’ll even find robots that understand human voice commands or engage in conversation with humans. Thanks to the use of deep neural networks, we are headed to a future where fighter jets will be unmanned and vehicles will not require drivers.

#4. Decision Trees

Falling under the supervised learning algorithms, the decision trees AI model is widely used for solving classification and regression problems. The decision trees model is pretty straightforward and relies on data used to make past decisions. Decision trees got their name from the way they are hierarchically arranged. Following the structure of a tree, a decision tree will have a root, branches, internal nodes, and leaves.

The root node is the beginning of a decision tree which has outgoing branches. The outgoing branches contain internal nodes (also known as decision nodes). Depending on the data available from the internal nodes, all the possible outcomes are delivered to the leaf notes and will hence be used to make decisions.

While simple decision trees are easy to manage, some trees become so big that they might become unmanageable or too complex to interpret. Complexity increases with the growth in tree size and, as happens in real life, the time must come when the tree will be pruned.

Decision trees are used by businesses in different industries as an aid in decision-making. Since they compare all the possible consequences of making a decision, they help businesses choose the best course of action.

#5. Random Forest

Following the idea of decision trees is the random forest AI model. If your organization is using a single tree to make decisions, don’t you think it would be better served by a random forest of trees?

One of the problems that decision trees are prone to, is bias. When you use a random forest of trees, you will arrive at better decisions especially when the trees are uncorrelated. While you trust every tree to make the best decision, when you have a forest of trees delivering independent decisions, you’ll have benefitted from many unbiased opinions.

To make the best use of random forests, the forest is trained through a process known as bagging. Through this process, the random forest AI model takes the average of the output from various decision trees and from this average you can see the most popular decision. The level of precision increases proportionally to the number of trees in a forest. When you use random forests, you’re basically using multiple classifiers to find the best solution to a complex problem.

Random forests are used in many industries including e-commerce, finance, and healthcare. In finance, for instance, credit managers use random trees to evaluate customers and make a decision on whether or not to give credit.

 #6. Naïve Bayes

Naïve Bayes is an AI model that operates on the assumption that the starting inputs in an algorithm have no relationship. The model is based on the Bayes Theorem for calculating conditional probabilities. The Naïve Bayes model assumes that every input makes an equal and independent contribution to an outcome, hence the use word naïve. Compared to other models, Naïve Bayes is easy to learn and implement yet it delivers outstanding results. In addition, this AI model takes up very little storage space. Naïve Bayes is widely used for spam filtering and document classification among other uses.

#7. K-Nearest Neighbor

K-Nearest Neighbor (KNN) belongs to the family of supervised AI models and is usually chosen because it’s easy to implement and is capable of handling large amounts of training data.

KNN gets its name from the way it classifies data. Once the initial data has been input, any subsequent data will be classified based on how similar it is to the existing data hence the term “nearest neighbor”. KNN can be used for regression but it’s most widely used in classification because of the way input data is stored. This AI model is used by various industries including healthcare facilities, financial institutions, and data processing applications.

#8. Linear Discriminant Analysis

Linear Discriminant Analysis (LDA) also belongs to the class of supervised AI models and is mostly used for classification purposes. This AI model is used to find the best linear discriminant, or separator, between two data classes. You begin by teaching the algorithm the linear discriminant function and once this function is learned it will be used to predict the classification of subsequent data.

Most of the users of LDA like it for its ability to handle huge amounts of data and also because of its flexibility and accuracy. To aid in producing the most accurate results, LDA usually focuses on the most important features of the data. LDA is also used for dimensionality reduction – when presented with datasets that have near-similar features, the algorithm will work to reduce those features and highlight the most crucial ones. This AI model is used in a variety of industries where accuracy is critical. It’s used for face recognition and also helps in email classification.

Conclusion

AI is here to stay and its use will only increase in the future. Whatever business you might be involved in, failure to invest in one or several AI models could only be a disservice to your business. While AI models function differently, they are inevitable assets in the modern (and future) business environment. When you wish to monitor your workforce or conduct background research on new employees, investing in AI could save you money and help you avoid litigation. AI will also help improve business operation speeds, improve customer relations, reduce the number of mistakes made through unavoidable human errors, and help manage and retain talent. In a nutshell, AI should be considered a business investment rather than a cost and its long-term benefits outweigh the initial capital outlay costs by far.