What Is an AI Model?

An AI model is a computer program that uses algorithms to make informed decisions and predictions based on new data. It is designed to perform tasks that typically require human intelligence, such as learning, reasoning and problem-solving — all without being given explicit instructions for every scenario.

With their unique ability to understand and interpret data, AI models are the backbone of the booming artificial intelligence industry, pushing the boundaries of what’s possible in fields ranging from manufacturing to healthcare.

What Is An AI Model?

An AI model is a computer program, trained on lots of data, that can find patterns and make predictions without human intervention. If you’ve ever chatted with ChatGPT or followed Netflix’s recommendations for what to watch, then you’ve interacted with an AI model.

While most computer programs require precise instructions to perform specific tasks, AI models use algorithms, which are step-by-step rules that process inputs into outputs using arithmetic, repetition and decision-making logic. Algorithms enable AI models to reason, act and learn independently, allowing them to handle more “complex and dynamic problems” than traditional programs, according Archer Chiang, an AI engineer and founder of corporate gifting company Giftpack — tasks like natural language processing and computer vision, which traditional programs would struggle to perform without explicit programming.

AI models come in all shapes and sizes. Each has its own distinct set of abilities, according to the data and decision-making logic they use. For example, large language models (LLMs) process vast amounts of text data to generate human-like responses and assist in various language-related tasks. And convolutional neural networks (CNNs) are good at extracting distinctive patterns and characteristics from images, so they’re typically used in image recognition tasks.

Related ReadingAll Machine Learning Models Explained

How Do AI Models Work?

AI models work by analyzing input data, employing algorithms and statistical methods to uncover patterns and correlations with the data, and using what it learned to draw conclusions and make informed decisions. The process involves three basic steps:

1. Data Collection and Processing

The process begins with collecting a large corpus of data that is relevant to the model’s intended task. For instance, a model designed to recognize images of dogs needs to be given thousands of images of dogs, along with other animals so it can learn the difference. This data can be gathered from open-source repositories, mined from the internet and purchased from private sources like newspapers and scientific journals. Companies can also use their own proprietary data.

The data is then processed and cleaned so that it is in a usable format. This involves correcting errors or inconsistencies in the data, removing duplicate data, filling in missing values and standardizing data entries. 

Data quality is arguably the most important part of AI model development, as it directly influences the model’s accuracy and reliability in making trustworthy predictions and decisions, said Jignesh Patel, a professor in the computer science department at Carnegie Mellon University and co-founder at generative AI company DataChat. “High-quality data is super important to get these models to respond correctly.”

At the same time, low-quality data can ruin an AI model. “[AI models] are going to be a reflection of whatever data went in,” said Andrew Sellers, head of technology at data management company Confluent. “If you train a model on data that is fundamentally biased, then the predictive capabilities of that model will be fundamentally biased.”

2. Training

Next, the AI model needs to be trained. This involves feeding all the data gathered and processed in the first step into the model, testing it and then inspecting the results to confirm that the model is performing as expected. Training is accomplished in one of three ways:

1. Supervised Learning: The model is trained on labeled data, and told what the desired output is. For example, a model might learn to distinguish between pictures of cats and dogs by training on a dataset where each input image is labeled as either “cat” or “dog.”
2. Unsupervised Learning: The model is not given access to labeled data; instead it identifies the connections and trends within the data on its own. For example, a model can analyze customer shopping behavior and, based on patterns, suggest what to buy next.
3. Reinforcement Learning: The model learns to make decisions by interacting with its environment, receiving feedback in the form of rewards for correct outputs and penalties for incorrect outputs. “You don’t say anything about the rules or how it should be, you just give an objective,” said Yigit Ihlamur, an AI researcher and general partner at VC firm Vela Partners. For example, an AI model tasked with winning a game must learn through trial and error, gradually understanding the rules and improving its strategy.

During training, developers adjust the model’s internal parameters (also known as weights) to reduce the likelihood of it making errors in future predictions — an iterative process known as backpropagation, which continues until the model’s outputs are sufficiently accurate. 

Developers also keep an eye out for overfitting and underfitting. Overfitting refers to a model that performs well on training data but not new data, usually due to being trained too much on a single data set. Underfitting is when a model can’t find a relationship between input and output data, performing poorly on both training and new data. This is often because the model needs more time or data for proper training.   

Once it has been trained, the AI model can make predictions and decisions based on new data.

3. Monitoring and Maintenance

After an AI model has been deployed, its performance is continuously monitored and updated to maintain accuracy. Models can also continue to learn by leveraging the knowledge gained in previous tasks, creating a kind of “virtuous feedback cycle” in which an output is fed back into a model as input in order to further train it, Sellers said. “The data that’s generated gets fed back into what it knows in subsequent runs.”

More AI Basics7 Types of Artificial Intelligence

AI vs. Machine Learning vs. Deep Learning

Broadly speaking, artificial intelligence is a field of computer science focused on machines that can replicate what is understood as human intelligence. The most basic AI models are programmed to perform specific actions by following a set of rules, such as several if-else statements (think spam filters and automated chatbots).

When people talk about AI models today, they generally refer to either machine learning (ML) or deep learning (DL) models.

Machine Learning (ML) 

Machine learning is a subfield of artificial intelligence in which computers learn from data — either labeled or unlabeled — to make decisions and predictions without being explicitly programmed to do so. ML models use algorithms that identify patterns in past data, which helps them draw conclusions on new data and improve over time.

Common use cases of ML models include: 

• Fraud detection: ML models can analyze financial transactions to determine unusual or risky behavior, noting higher likelihoods of fraud.  
• Facial recognition: ML models can process individual facial features and search for a match among a database of faces, supporting biometrics for mobile devices. 
• Product recommendations: ML models can comb through behavioral data from past purchases, viewing habits and other factors to develop tailored product outreach.

Deep Learning (DL) 

Deep learning is a subfield of machine learning that attempts to mimic the human brain, using multi-layer algorithm structures called neural networks. DL models can identify relationships and patterns within large quantities of unstructured data, allowing them to handle intricate tasks like image and speech recognition. 

Common use cases of DL models include: 

• Self-driving cars: DL models process data collected from various sensors to make informed decisions based on a car’s surrounding environment. 
• Voice assistants: Neural networks are used to process verbal questions or commands, understand their meaning and produce relevant text responses. 
• Social listening: DL models can analyze images and written text on digital platforms and consider context to deliver insights on customer sentiments.

Learn MoreArtificial Intelligence vs. Machine Learning vs. Deep Learning

11 Common AI Model Types (With Use Cases)

Here are some of the most common AI models and how they are used today. 

1. Large Language Models (LLMs)

Large language models are used to generate human-like text. They are trained on enormous amounts of data to learn structure, grammar and patterns, allowing them to predict the next word or sequence of words based on the context provided. Their ability to grasp the meaning and nuances of language allows LLMs to excel at tasks like text generation, language translation and content summarization — making them a key component of the larger generative AI field.

• Use Case: LLMs like GPT-4, Claude, Gemini and Mistral Large are used to power popular AI chatbots, enabling them to carry on natural conversations with users, write poems, edit code and much more.

2. Convolutional Neural Networks (CNNs)

Convolutional neural networks are used to process and analyze visual data, such as images and videos. To accomplish this, CNNs have multiple layers that extract important features from input image data, such as edges, textures, colors and shapes. This process continues, with each layer looking at bigger and more meaningful parts of the picture, until the model decides what the image is showing based on all the features it has found. 

• Use Case: CNNs are used in facial recognition systems, helping to verify or identify a person based on their facial features extracted from images or video frames. CNN-based facial recognition systems can grant entry into secure locations and unlock smartphones.

3. Recurrent Neural Networks (RNNs)

Recurrent neural networks are used to process sequential data, where the order of the data points matters. Because RNNs can retain information from previous inputs through loops in their architecture, they are especially good at tasks like language modeling, speech recognition and forecasting — when understanding the order of and relationship between data points is essential for accurate predictions.

• Use Case: RNNs can analyze historical financial information to predict future fluctuations in stock prices. This helps traders, financial analysts and investors make more informed decisions on what stocks to buy based on potential market trends.

4. Generative Adversarial Networks (GANs)

Generative adversarial networks are deep learning models that have two competing neural networks: generators and discriminators. The generator creates fake outputs that resemble real data (like text, images, audio), while the discriminator works to differentiate the artificial data from real data provided in a training dataset. Over time, the generator makes increasingly realistic data and the discriminator gets better at detecting it, resulting in high-quality synthetic data like AI-generated images, audio and video. 

• Use Case: GANs are used to create deepfakes, a form of artificial visual media used in the entertainment industry to swap actors’ faces in scenes, alter an actor’s appearance or age and much more.

More on Artificial IntelligenceExplore Built In’s AI Coverage

5. Logistic Regression Models

Logistic regression models are used in binary classification tasks, where the goal is to estimate the probability of one of two possible outcomes — yes/no, true/false, spam/not spam — based on a set of independent variables. 

• Use Case: Logistic regression models are used in banking to help detect fraudulent transactions. By analyzing various historical data, such as transaction amount, location and frequency, these models can help financial institutions flag suspicious activities on customers’ credit and debit cards — marking each transaction as either fraud or not fraud.

6. Linear Regression Models

Linear regression models are used to predict the value of a dependent variable (output) based on given independent variables (inputs). Using a linear equation, the model establishes a relationship between input data points to estimate the value of an output. Linear regression models are often used to predict continuous outcomes, such as forecasting sales or predicting trends.

• Use Case: In the real estate industry, linear regression models can be used to predict the price of a house based on factors like square footage, location and age. By analyzing relevant past sales data, the model can figure out how each of these factors influences the value of a property, helping real estate agents price it accordingly.

7. Decision Trees

Decision trees use a “tree-like structure” to organize data into small groups and then use those groups to predict outcomes. “Each node in the tree represents a feature, and branches represent decisions, leading to leaf nodes that indicate the output,” Chiang said. Decision trees are intuitive and easy to interpret, making them helpful decision-making tools in high-stakes fields like healthcare and finance, where the choices these models make can significantly affect people’s lives.

• Use Case: Decision trees can help companies analyze factors like market trends, customer preferences and competitors’ offerings, and then break down decisions into simple steps that should be focused on.

8. Random Forests 

Random forests break down complex decision-making processes into a series of individual “leaves,” combining multiple decision trees to make more accurate predictions. Each tree in the forest uses a random subset of features to draw a conclusion, all of which are aggregated and averaged out in order to arrive at a final decision. Although random forests tend to be harder to interpret than single decision trees, they are usually more accurate and can handle larger volumes of diverse data.

• Use Case: In banking, random forests can be used to predict which customers are more likely to repay their debt on time, taking into account factors like credit history, income levels, loan amounts and other past purchasing behaviors.

9. Support Vector Machines (SVMs)

Support vector machines are designed to solve binary classification and regression problems, where it has to organize data into one of two groups. These models work by creating a line (or hyperplane) separating data into different classes, with the goal of maximizing the distance between the hyperplane and the closest data points in each category — thus making it easy to distinguish between data classes. SVMs are versatile and can handle nonlinear relationships between data, which means they’re good at distinguishing complex patterns.

• Use Case: SVMs are often used in the field of biometrics, helping to identify people’s voice, face, fingerprint, handwriting, gait and more based on unique physiological and physical characteristics.

10. Foundation Models 

Foundation models are neural networks trained on massive volumes of data to perform a variety of tasks. These models use a technique called transfer learning, taking knowledge from one task and applying it to another.

• Use Case: Industry-leading chatbots use foundation models to process publicly available data and handle a broad range of prompts. For example, ChatGPT’s foundation models leverage data gained from the internet, third parties and training sessions to answer questions, find information and brainstorm ideas.  

11. Recommender Systems

Recommender systems, or recommendation systems, attempt to predict what products or content users will be interested in and send personalized recommendations based on user data. Depending on the context, this data can include demographic information, past purchases, viewing habits and browsing history. 

• Use Case: Streaming services like Netflix use recommender systems that analyze users’ viewing history to recommend related shows and movies. YouTube also uses a recommendation system to populate users’ homepage and “Up Next” panels.

It’s important to remember that no AI model is perfect — they all get things wrong, and it can be challenging (if not impossible) to fully understand why they make the decisions they do.

Frequently Asked Questions