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True, sentient Artificial Intelligence is still more science fiction than science fact, but we can use AI models to accomplish many tasks and solve complex problems. AI models rely on Machine Learning algorithms in order to be effective at analyzing information, making decisions and predictions, and providing valuable insights.

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Let’s take a closer look at AI models and what they are. We will also see how AI models differ from Machine Learning models and the different types of Machine Learning models used in the creation of AI models.

What is an AI Model?

Artificial Intelligence is one of the most fascinating fields of computer science. The goal of Artificial Intelligence studies is to create a machine that can replicate human intelligence. Currently, true AI that can think like a human has not been achieved. However, this doesn’t mean that we can’t benefit from the use of AI.

Today, we use AI models for a number of analytical and decision-making tasks. An AI model is a program or algorithm that relies on a set of data to recognize patterns and make predictions or decisions. The more data points an AI model receives, the more accurate it can be in its predictions and analysis.

AI models rely on computer vision, natural language processing, and Machine Learning in order to recognize different patterns. AI models also use decision-making algorithms in order to learn from the training they have received, the data they have collected and reviewed, and ultimately apply what they have learned to achieve their predefined goals.

AI models are very good at solving difficult problems that have a large amount of data. They are able to accurately solve complex problems with a very high degree of accuracy.

What is the Difference Between Machine Learning Models and AI Models?

Many people erroneously conflate Machine Learning and Artificial Intelligence. This is likely because Machine Learning is a subset of Artificial Intelligence. However, there is a key difference between the two that you should be aware of.

As we have previously defined, Artificial Intelligence is concerned with creating machines that can simulate human thoughts, intelligence, and behaviors. On the other hand, Machine Learning is concerned with providing machines with the ability to learn for themselves from experience and lessons without the need to be explicitly programmed.

All Machine Learning models are AI models, but not all AI models will necessarily be Machine Learning models. This is an important distinction to make, and it will help you understand this topic in greater detail.

Machine Learning models are an essential part of AI development. After all, a big part of human intelligence is the ability to learn things and predict future outcomes based on past experiences and lessons.

The Different Types of Machine Learning

There are many different types of AI models, and we will go over some of the most prominent ones later in this post, but before we do that, we must go over the different types of Machine Learning. There are three main types of Machine Learning:

  • Supervised
  • Unsupervised
  • Semi-supervised

Supervised Machine Learning

In a supervised learning model, a human trains or teaches the algorithm what to look for. Often this person doing the training is a data scientist who is a subject matter expert in whatever task the algorithm is being taught to do.

For example, an algorithm working on image recognition might be taught by a data scientist which images contain offensive or explicit content. The algorithm then uses this information to find other images that contain similar content.

AI models that are built with supervised learning are most often used to perform predictive analysis. These models use past decisions made by subject matter experts to predict future decisions that an expert might make.

Unsupervised Machine Learning

AI models don’t have to be developed through human training. In an unsupervised learning model, software trains the algorithm. In some cases, the training method used by the training software will mimic that of a human, but they don’t necessarily have to teach in the same way.

AI models built with unsupervised learning are most often used to perform descriptive analysis. These tasks include content summary, content classification, and content extraction. These types of AI models are capable of identifying patterns and categorizing data without human training.

Semi-Supervised Machine Learning

Semi-supervised learning models combine a little bit of both previous models we have discussed. In a semi-supervised learning setting, a human does part of the training, and software is left to handle the rest based on the initial training done by the human.

Since AI models built with semi-supervised learning get a bit of both learning styles, they are also capable of performing both predictive and descriptive analysis tasks depending on the purpose they are designed for.

Common AI Models

There are a number of different AI models, and they all work a little bit differently. Some of the most popular models are:

  • Deep neural networks
  • Linear regression
  • Logistic regression
  • Decision trees
  • Random forest

Deep Neural Networks

The deep neural network is one of the most popular, widely used AI/ML models. The design for this model was inspired by the human brain and its neural network. This AI model uses layers of artificial neurons to combine multiple inputs and provide a single output value.

Deep neural networks have been used widely in mobile app development to provide speech and image recognition services and natural language processing. This AI model represents the cutting edge of Artificial Intelligence. It is very adept at solving complex problems that possess large data sets.

Linear Regression

This AI model is very popular with data scientists working in statistics. Linear regression is based on a supervised learning model. These AI models are tasked with identifying the relationship between input and output variables.

A linear regression model is capable of predicting the value of a dependent variable based on the value of an independent variable. These models are used in a number of different industries including, healthcare, insurance, and banking.

Logistic Regression

This is another popular AI model, and it is closely related to the linear regression model. What makes the logistic regression model different from the linear regression model is that it is only used to solve classification-based problems.

Logistic regression is the best AI model for solving a binary classification problem. This model is adept at predicting the value or class of a dependent variable based on a set of independent variables.

Decision Trees

This AI model is very simple and also extremely efficient. The decision tree uses data from past decisions to arrive at a conclusion. These trees often follow a basic if/then pattern. For example, if you eat a sandwich at home, then you will not need to buy lunch.

Decision trees can be used to solve both regression and classification problems.

Random Forest

If one decision tree is a powerful AI model, how powerful is an entire forest? A random forest is a collection of decision trees. Each decision tree returns its own result or decision, which is then merged with the results from every other tree in the forest. The merged results are used to make a more accurate prediction or decision.

The random forest is a great AI model when you have a large set of data. This model is used for solving both regression and classification problems.

Final Thoughts

While sentient Artificial Intelligence, like the kind made popular by science fiction, is not a reality, there are still many practical, important uses for AI models and tools. If your organization is looking to get the most out of its data and make better decisions, using AI is a great way to accomplish this.

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