What Are the Different Types of AI Models?

What is an AI model?

Models are the virtual brains of artificial intelligence. Created using algorithms and data, an AI model learns from experiences and draws conclusions.

AI models need human assistance to understand data and perform tasks beyond their training. You can train an AI model to do almost anything, from simple automated responses to complex problem-solving.

AI models are best at:

• Analyzing datasets
• Finding patterns
• Making predictions
• Generating content

The more data an AI model has, the more accurate it can be when making predictions and decisions.

How do you create an AI model?

Data scientists develop algorithms to build AI models. An algorithm is a set of step-by-step rules and processes that solve a specific problem or complete a task. Once an algorithm is trained with data, it becomes an AI model.

Data scientists also use artificial neural networks (ANNs) to teach computers to process data in a way that mimics how the human brain sends signals and receives information. Just like our brain’s interconnected network of neurons, ANNs are artificial neurons (nodes) that work to solve a problem.

Artificial neural networks are used for tasks that require pattern recognition, like:

• Image and speech recognition
• Summarizing documents
• Natural language processing (NLP)
• Complex decision-making

An example of an algorithm

You probably use one of the most well-known algorithms every day: Google Search.

When you enter a query into Google, its search algorithm crawls billions of web pages to quickly give you the most helpful and relevant results. As the way we use Google evolves, so does their algorithm.

The different types of AI models

Here, we’ll focus on the following types of AI models:

• Machine learning
• Supervised learning
• Unsupervised learning
• Deep learning

Machine learning models

Machine learning is a subset of AI. While all machine learning is AI, not all AI is machine learning.

To create a machine learning model, data scientists train algorithms with labeled, unlabeled, or mixed data. There are different types of machine learning algorithms for different goals:

• Classification recognizes certain entities in the dataset to draw conclusions on how they should be labeled or defined.
• Regression helps make predictions. It understands the relationship between independent and dependent variables.

Data is modified to best execute a specific task, and then it becomes a machine learning model. ML models examine certain variables within the data and find patterns that help make predictions.

Machine learning models will improve over time as they are trained and exposed to more data.

Machine learning example

Let’s say you want to teach an AI model how to identify different types of flowers. First, you need a labeled dataset with images of flowers and their names.

Next, a data scientist or AI engineer feeds the model datasets so it can learn how to identify patterns and trends, just like the human brain.

The ML model will learn from the dataset and start to detect patterns and identify differences in each type of flower. Eventually, the model can tell you if an image is of a sunflower or a rose.

Supervised learning models

Supervised learning is the most common type of machine learning and the simplest way that AI models learn.

It’s called “supervised” learning because the algorithm is trained with human-created, labeled datasets. The labels assist the algorithm and help the ML model understand exactly how to classify data in a way the data scientist wants.

Using labeled datasets with examples of inputs (features) and outputs (labels), supervised learning algorithms are trained to predict outcomes and identify patterns. Once the model is trained and tested, it can make predictions with unknown data based on the previous knowledge it has learned.

Supervised learning example

Thinking of our flower example, supervised learning requires a labeled dataset with examples of flowers and their species names.

The algorithm learns to understand the characteristics that belong to each type of flower, which the labeled outputs provided. You can test the model by showing it a picture of a flower and asking it to guess its name.

If it gives you the wrong answer, that just means you need to keep training the model and adjusting parameters to improve accuracy.

Unsupervised learning models

Unsupervised learning is another type of machine learning, but it’s not as popular as supervised learning.

While supervised learning requires labeled data, unsupervised learning finds patterns without human instruction. Models follow self-learning algorithms that enable them to receive raw data and create their own rules.

The unsupervised learning model structures data based on similarities, differences, and patterns. There’s no data scientist required with unsupervised learning because the model is designed to work without instructions on how to handle each piece of data.

Unsupervised learning example

You can provide a dataset of different types of flowers and the unsupervised learning model will group them into categories, like color and petal shape. As the model matures, the grouping will become more specific.

Deep learning models

Deep learning is an advanced type of ML that learns to identify complex patterns in text, images, and sounds.

With deep learning, data is processed and classified through layers, and each layer has a role to play in the processing of input data.

Here’s a quick look at the different types of layers in a deep learning neural network:

• The input layer receives raw data and passes it through the network.
• Hidden layers assess and process the input data and transform it into an output.
• The output layer uses the processed data to deliver a result.

A basic neural network typically has one or two hidden layers. But a deep learning neural network can have hundreds. All the layers analyze data differently and can identify patterns that aren’t possible with basic machine learning methods.

Deep learning example

Deep learning models can automate complex tasks that normally require human intelligence. This includes things like transcribing sound into text or describing images in detail. Large language models (LLMs) are large, pre-trained deep learning models.

Deep learning powers a lot of the AI applications we use every day, like:

• Automatic facial recognition
• Fraud detection
• Virtual reality
• Digital assistants

Examples of common AI models

There’s an overwhelming number of different AI models. Whether you need to classify different types of flowers or predict healthcare outcomes, there’s a specific model for anything you need to do.

The following is a small sample list of common types of AI models.

Common machine learning models

• Linear regression predicts a continuous value. For example, predicting house prices based on features like size and location.
• Logistic regression is for binary classification tasks. There are only two possible answers the model provides. An example is email spam detection. Logistic regression determines if an email is spam (yes) or not (no).
• Decision Trees are models that use a tree-like graph of decisions and their possible consequences. They are best for classification and regression tasks.

Common deep learning models

• Convolutional Neural Networks (CNNs) are for processing grid-like data such as images. CNNs are powerful for tasks like image classification, object detection, and even playing games.
• Recurrent Neural Networks (RNNs) are suited for sequential data like time series or natural language. RNNs are for applications like language modeling and machine translation.
• Long Short-Term Memory Networks (LSTMs) are a special kind of RNN capable of learning long-term dependencies. This makes them effective for tasks involving sequential data that spans over long sequences.

Common reinforcement learning models

• Q-Learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state.
• Deep Q Networks (DQN) combine Q-learning with deep neural networks. DQNs are for complex decision-making tasks, such as playing video games at a superhuman level.
• Policy Gradient Methods optimize the parameters of a policy directly by gradient descent. These are for scenarios where the action space is high-dimensional or continuous.

Apply AI to your application development goals

To sum things up:

• AI models are the virtual brains of artificial intelligence.
• Once an algorithm is trained with data, it becomes an AI model. The more data the model has, the more accurate it is.
• Some of the different types of AI models are machine learning, supervised learning, unsupervised learning, and deep learning.
• There’s a specific AI model for anything you want to do.

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