A Deep Dive Into What an AI Model Is

With the introduction of ChatGPT, many people are trying to understand more about artificial intelligence. They are interested in knowing how it functions and how it is connected to machine learning (ML). Here is a deep dive into AI models and how they work.

What Is an AI Model and Why Is It Important?

Artificial intelligence generally refers to a machine or program that can replicate human intelligence. All of this happens in real-time. An AI model is a program that can analyze data to identify patterns it can use to make decisions and predictions.

Naturally, if the model has more data, the accuracy of the predictions is better. While AI has undoubtedly advanced in recent years, true artificial intelligence has not been created yet.

AI models are important because they help people or organizations solve problems. Many models can automate tedious, time-consuming or dangerous tasks and allow humans to focus on other essential operations.

These models can solve intensely complex problems if they have access to large data sets. To aid with identifying patterns to form assumptions, AI models use different technologies such as machine learning, computer vision and natural language processing.

Is an AI Model and a Machine Learning Model the Same?

An AI model and a Machine learning model are not the same. ML is an area within artificial intelligence. As mentioned, an AI model is a program that analyzes a database to form predictions. As the name would suggest, ML is a series of algorithms with the ability to teach itself.

In other words, one is a machine that replicates human intelligence behaviors and the other one is a machine with the ability to teach themselves how to learn and operate. The difference between these two is very important.

A vital concept to understand is that all ML models are artificial intelligence, but not all AI is machine learning. To make this easier to understand, try to think of it in terms of vehicles. For example, all cars are vehicles, but not all vehicles are cars — trains, bicycles, motorbikes, tractors and buses.

When ML is added to AI, the machine becomes inherently smarter. It now possesses the ability to learn for itself. Machine learning is not a program but a series of algorithms that work together.

A machine with an ML model will learn from past experiences and decisions. This allows it to increase its intelligence the more it learns from data and these experiences.

4 Different Types of ML Models

Knowing the various machine learning types is essential to understand the different AI models. Many AI systems are based on these ML models.

1. Unsupervised Learning

Unlike other machine learning models, this one does not require people to train it. Instead, it uses software. The machine or algorithm seeks to identify patterns in data to learn from. This learning type is excellent for discovering patterns and using data to form conclusions or decisions.

2. Supervised Learning

This learning model is not trained on its own but through a human who teaches it what to look for. This model gets its name from someone supplying the algorithm with data — meaning it is supervised.

This learning type is frequently used for predictive analytics. This a popular model and many algorithms use it, such as linear regression and decision trees.

3. Semi-Supervised Learning

This model combines the two learning methods discussed above. A human trains the algorithm and then software takes over. The software continues from where the person stops. In general, this learning model is suited for descriptive and analysis operations.

4. Reinforcement Learning

In this learning type, the algorithm learns by interacting with its environment. Based on this process, it will receive either a positive or negative reward. This model requires more computing power and is not as frequently utilized as the others.

6 Different Types of AI Models

There are many various AI models. While some of these share similarities, they are not the same. All of them have their unique advantages and disadvantages. While there are many models, here are the six most common ones.

1. Deep Neural Networks

As machine learning is a part of AI, deep neural networks (DNN) are a part of ML. DNN closely resembles a human brain with its neural network. This model has multiple layers where it assesses various variables.

DNN utilizes these layers to combine the variables to form a single conclusion or value. Deep neural networks are one of the most popular models within AI and ML.

It is responsible for making AI technology more intelligent. DNN is also the leading candidate for getting humans closer to developing true AI.

Deep neural networks are particularly good at solving complex problems. They can provide excellent performance in the applications they work with. For example, DNNs can provide great value in mobile development in terms of speech recognition and image classification.

There are also three types of deep neural networks.

• Convolutional Neural Networks (CNN)
• Multi-Layer Perceptrons (MLP)
• Recurrent Neural Networks (RNN)

2. Linear Regression

Linear regression is another popular AI model that seeks to find a connection between input and output variables. Data scientists frequently use this model. This is because linear regression is suited perfectly for work with statistics.

Linear regression utilizes independent variables to calculate the value of dependent variables. The dependent changeable is the response variable and the other is the explanatory variable.

Linear regressions follow the supervised learning model. Many sectors, such as banking, health care and insurance, utilize this model.

3. Logistic Regression

This model is similar to linear regression but has some notable differences. Logistic regression is also used for finding a connection between dependent and independent variables.

However, what sets it apart from linear regression is that it makes assumptions around categorical variables instead of continuous ones. This model is primarily used in solving classification-based problems.

4. Decision Trees

This model gets its name due to the way data is categorized. The divided data resembles the outline of a tree, hence the name. Decision trees are a very common AI model and follow a simplistic process.

Decision trees form predictions based on the data on it’s previous decisions. This model starts with a root node and then, with the help of branches, forms other decision/internal nodes. Decision tree models are great for solving classification-based or regression problems.

5. Random Forest

The best way to describe this model is to envision multiple decision trees within each other. A random forest model creates various decision trees. Each tree is made up of its own data it trains itself on and then combines all of them to arrive at a calculated conclusion.

In other words, it considers all the trees to form decisions and predictions. Like regular decision trees, this model solves regression and classification-based problems.

6. Naive Bayes

This AI model is based on a mathematical formula from the 18th-century — Bayes Theorem. It is named after the mathematician Thomas Bayes. The formula is for calculating conditional probability — the chance of an outcome occurring based on past events under the same circumstances.

The Naive Bayes AI model follows this principle to a certain extent. For example, it assumes that the appearance of one feature does not affect the presence of another one. In other words, the model thinks all predictors are independent.

This is why the model is called naive because most of the assumptions are almost always false. Hence the name. The Naive Bayes is simplistic and particularly good for solving classification problems.

The Power of AI Models

AI models assist humans in solving tasks. They make life easier for us and can help to solve complex problems. As this technology advances, it will become more widely implemented in industries.