A Guide to AI Models: What They Are and How They Work
Artificial intelligence (AI) is transforming how businesses operate, enabling faster decisions, deeper insights, and scalable automation. This guide breaks down what AI models are, how they differ from machine learning (ML) and deep learning (DL), and how leading companies are applying them to drive real results.
- Overview
- How AI Models Differ from ML and DL Models
- Popular AI Models and Their Practical Applications
- Resources
Overview
Artificial intelligence is becoming core to modern business operations, especially those that are data-driven. AI models accelerate the process of understanding and interpreting data. With their ability to quickly analyze data, find patterns and make predictions, these powerful programs have become essential to efficient—and sometimes automated—decision-making.
How AI models differ from ML and DL models
Artificial intelligence is a broad term referring to a group of technologies that use machines to simulate how the human mind works. Machine learning (ML) and deep learning (DL) are subsets of AI, each with its own set of processes for training machines to perform human-like cognitive processes.
ML models
Machine learning is a branch of AI that trains machines to learn from experience. ML models are provided with training data that’s been labeled (supervised learning) or with unlabeled, raw data (unsupervised learning). ML models mimic the way humans learn—by trial and error. Over time, well-trained models will make increasingly accurate predictions. ML models are widely used, with applications around forecasting (e.g., predicting next month's sales), segmentation (e.g., determining whether a transaction is fraudulent or not) and clustering (e.g., identifying items shopped by similar customers) and other recommendation systems.
DL models
Deep learning is a subset of machine learning. A DL model is essentially a multi-layered neural network, with each layer comprising a series of interconnected nodes. The “depth” of the deep learning model depends on its purpose. Some advanced DL models are made up of a thousand or more layers, with each successive layer further refining the accuracy of the model’s predictions. This type of AI model powers many applications that use large amounts of unstructured data (e.g., images, video, documents) such as self-driving vehicle technologies, digital voice assistants and social listening solutions.
Popular AI models and their practical applications
There are many different AI models in use today. Here are six of the most common, with a brief explanation of how each works and real-world use cases.
Linear regression
Linear regression is an ML model designed to find the linear relationship between input and output variables. By identifying the best-fit straight line between these two variables, linear regression models can be trained to accurately predict the value of the output variable based on the given input variable. Linear regression models are a popular choice for risk analysis, playing an important role in helping financial institutions assess risk to identify where they may be overexposed.
Logistic regression
A close cousin to linear regression, logistic regression is frequently used for solving classification-based problems. This technique is ideal for estimating the probability of an event occurring using a set of independent variables. One application of logistic regression is in the field of medical research. Researchers can use it to understand how certain diseases, such as cancer, are influenced by genetic factors, enabling the development of more accurate tests for these conditions.
Decision trees
Decision trees segment data using a series of if-else statements. At their core, decision trees are algorithmically generated flow charts that categorize or make decisions based on the answers to previous questions. They are particularly useful for generating fine-grained customer segments that can be used to create personalized offers, reduce churn and remain competitive.
Random forests
Random forests are an entire grove of decision trees bound together, with each generating its own result or decision. The input from all of the trees is then aggregated to create a single, more precise decision or prediction. Retailers can use random forests to more accurately predict buyer behavior using the insights this model provides to increase sales.
Neural networks
Neural networks underpin deep learning. These models comprise numerous layers of interconnected nodes. Each of these nodes is a computational unit with at least one weighted input connection, a transfer function that combines inputs and an output connection. The nodes are organized into connected layers, mirroring the structure of the synapses in the human brain. Neural networks excel at managing complexity and are incredibly useful for analyzing large amounts of diverse data. They are often used to provide decision support for supply chain managers, predict demand and optimize inventory levels.
Large language models
Large language models (LLMs) are a type of deep learning that can understand and generate language. Trained using enormous data sets, these AI models are incredibly versatile and can perform a range of tasks, including generating written responses to questions, language translation, summarizing documents and much more. Businesses across industries are using LLMs to enhance their customer service, which is incredibly useful for detecting sentiment and emotion. Organizations can leverage LLMs to analyze social media activity, online reviews and customer service chat interactions to build a deeper understanding of customer opinions, monitor how the brand is perceived and improve product and service offerings.
