What Is an AI Model? Here’s what you need to know

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AI models are becoming increasingly useful in almost every sector due to their ability to solve a variety of problem-solving tasks. In the simplest terms, an AI model is a tool or algorithm, which is based on a certain data set through which it can arrive at a decision – all without the need for human interference in the decision-making process.

In this article, you will learn about:

  • What is an AI model
  • What is an ML model
  • What are the most popular AI models

So let’s get started without further ado!

What Is An AI Model?

AI or Artificial Intelligence is a subfield within computer science associated with constructing machines that can simulate human intelligence.

An AI model is a program or algorithm that utilizes a set of data that enables it to recognize certain patterns. This allows it to reach a conclusion or make a prediction when provided with sufficient information.

This is especially useful for solving complex problems using huge amounts of data with high accuracy and minimum costs. To learn more about one of the most complex AI tasks, check out our complete article about Pattern Recognition.

 

Human Intelligence vs. Artificial Intelligence
Barn owl or apple? Image recognition tasks can be equally challenging for AI models and humans. – Source @teenybiscuit

What Is An ML Model?

Often we come across the terms Artificial Intelligence and Machine Learning being used interchangeably. Therefore many may wonder what a machine learning model is and how it is different from an AI model.

Well, both AI and ML are part of computer science and contribute to the creation of intelligent systems. But while these two technologies are related, these are not synonymous terms.

AI is a larger concept associated with the creation of machines that can simulate human behavior and intelligence.

On the other hand, ML is a subset within AI associated with providing machines the ability to learn from experience without the need to be programmed explicitly. Or, in simple words, ML or machine learning is a part of AI. So while all ML models are by default AI models, the opposite may not always be true.

If you are looking for a more detailed guide about the topic, we recommend you to check out our complete overview of AI, Machine Learning, Deep Learning, and their differences.

 

data-science-artificial-intelligence-machine-learning-vs-deep-learning
Artificial Intelligence (AI) includes the sub-field Machine Learning.

Next, we will list the top, most used AI models which you need to know. Then, we will go through the entire list and describe them one by one.

List of the Most Popular AI Models

  • AI Model #1: Linear Regression
  • AI Model #2: Deep Neural Networks
  • AI Model #3: Logistic Regression
  • AI Model #4: Decision Trees
  • AI Model #5: Linear Discriminant Analysis
  • AI Model #6: Naive Bayes
  • AI Model #7: Support Vector Machines
  • AI Model #8: Learning Vector Quantization
  • AI Model #9: K-nearest Neighbors
  • AI Model #10: Random Forest

Overview of the Most Important AI Models

In the next step, we will look into those AI techniques and highlight their most important characteristics.

1. Linear Regression

Used extensively in statistics, Linear Regression is a model that is based on supervised learning. The main task of this model is to find the relationships between the input and output variables. In simpler words, it predicts the value of a dependent variable based on a given independent variable.

Linear regression models are widely used in various industries, including banking, healthcare, insurance, etc.

2. Deep Neural Networks

One of the most popular AI/ML models, Deep Neural Networks or DNN, is an Artificial Neural Network (ANN) with multiple (hidden) layers between the input and output layers. Inspired by the neural network of the human brain, these are similarly based on interconnected units known as artificial neurons. To learn more about this AI model, read our guide about how Deep Neural Network models work.

DNN models find application in several areas, including speech recognition, image recognition, as well as, natural language processing.

AI model - Deep Neural Networks
Video analytics with Deep Neural Networks for vehicle detection
3. Logistic Regression

A very popular ML model, Logistic regression is the preferred method for solving binary classification problems. It is a statistical model that can predict the class of the dependent variable from the set of given independent variables.

This is similar to the Linear regression model, except that it is only used in solving classification-based problems.

4. Decision Trees

In the field of Artificial Intelligence, the Decision Tree (DT) model is used to arrive at a conclusion based on the data from past decisions. A simple, efficient, and extremely popular model, Decision Tree is named so because the way the data is divided into smaller portions resembles the structure of a tree. This model can be applied for both regression and classification problems.

5. Linear Discriminant Analysis

Linear Discriminant Analysis or LDA is a branch of the Logistic Regression model. This is usually used when two or more classes are to be separated in the output. This model is useful for various tasks in the field of computer vision, medicine, etc.

6. Naive Bayes

Naive Bayes is a simple yet effective AI model useful for solving a range of complicated problems. It is based on the Bayes Theorem and is especially applied for test classification.

The model works on the assumption that the occurrence of any particular feature does not depend on the occurrence of any other feature. Since this assumption is almost never true, the model is called ‘naive’.

It can be used for both binary and multiple-class classifications. Some of its applications include medical data classification and spam filtering.

7. Support Vector Machines

SVM, or Support Vector Machine, is a quick and efficient model that excels in analyzing limited amounts of data. It is applicable for binary classification problems.

Compared to newer technologies such as artificial neural networks, SVM is faster and performs better with a dataset of limited samples – such as in text classification problems.

This is a supervised ML algorithm that can be used for classification, outlier detection, and regression problems.

8. Learning Vector Quantization

Learning Vector Quantization (LVQ) is a type of Artificial Neural Network that works on the winner-takes-all principle. It processes information by preparing a set of codebook vectors that are then used to classify other unseen vectors. It is used for solving multi-class classification problems.

9. K-nearest Neighbors

The K-nearest Neighbors (kNN) model is a simple supervised ML model used for solving both regression and classification problems. This algorithm works on the assumption that similar things (data) exist near each other.

While it is a powerful model, one of its major disadvantages is that the speed slows down with an increase in the data volume.

10. Random Forest

Random Forest is an ensemble learning model useful for solving both regression and classification problems. It operates using multiple decision trees and makes the final prediction using the bagging method.

To simplify, it builds a ‘forest’ with multiple decision trees, each trained on different data subsets, and merges the results together to come up with more accurate predictions.

What’s Next?

To sum up, different AI models are used for solving different problems. Therefore, being aware of the models is essential for identifying the one best suited for a particular task. With the improvement in technology, these models are sure to be applied in far wider areas across the fields of computer vision, healthcare, business, finance, banking, etc., in the near future.

To read more about related topics, we recommend you the following articles:

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