What Are AI Algorithms? Types of AI Algorithms + Use Cases

The line between human and artificial intelligence is growing ever more blurry. Since 2021, AI has deciphered ancient texts that have puzzled scholars for centuries, detected cancers missed by human radiologists, and identified traces of organisms as old as 3.3 billion years—more than twice the age of those previously discovered.

Algorithms are the engine behind artificial intelligence’s increasingly human-like ability to reason, and are pushing the limits of how we understand the world around us. This article explains how these AI algorithms work, details their power and limitations, and explores how they can play a part in growing your business. 

What are AI algorithms?

AI algorithms are sets of instructions that enable machines to learn from data, identify patterns, and make decisions with minimal human intervention. Unlike traditional programming, where developers explicitly code every rule, machine learning algorithms improve their performance through experience.

Algorithms are crafted by data scientists and AI researchers who design the necessary mathematical frameworks and neural networks—interconnected computational nodes modeled after the structure of the human brain. An algorithm’s efficacy depends heavily on several factors: the quality and quantity of training data, the model’s architecture, and the accuracy with which its performance can be measured. Without quality data and proper validation methods, even the best deep learning algorithms fail to deliver accurate predictions. 

AI algorithms can be used in several ways across industries, including:

• Scientific research. AI accelerates discovery by automating experiments and analyzing results in real time. 

• Dangerous environments. AI-powered robots support disaster response efforts, navigating dangerous environments where human access is limited. 

• Cybersecurity. Security systems leverage AI to detect threats. AI’s ability to process massive datasets and analyze multiple vulnerabilities enables it to catch anomalies faster and more effectively than human analysts. 

• Sustainability. AI is being used to understand and prevent climate change—while also contributing to it through its insatiable energy demand—processing massive environmental datasets to track deforestation, predict weather patterns, and optimize renewable energy systems. 

• Personalization. In marketing, automation transforms how brands reach customers. For example, algorithms can be used to analyze customer data and create hyper-personalized ad campaigns (like Pods’ hyper-localized billboard trucks, which adapted as the vehicles drove around New York).

• Predictive maintenance. Manufacturing facilities use predictive maintenance algorithms to predict equipment failures before they happen. Using sensors on equipment to monitor elements like temperature and vibration, these algorithms help humans proactively repair equipment and improve safety. 

At their core, algorithms excel at pattern recognition—analyzing data points to identify relationships and regularities within large datasets. Research on pattern benchmarking reveals that while AI excels at standard pattern-recognition tasks, there remains a substantial gap between machine pattern recognition and human-level concept learning. In short, humans can learn complex patterns from minimal examples and apply them creatively to new situations—a capability that continues to challenge even the most advanced deep neural networks. 

Types of AI algorithms

• Supervised
• Unsupervised
• Semi-supervised
• Reinforcement

Understanding the types of AI algorithms helps demystify how these systems learn and how they can be applied to your ecommerce business. Each type solves different problems and requires different approaches to training data. 

Supervised

Supervised learning algorithms learn from labeled data, where the correct answers are provided during training. Think of it as learning with a teacher who shows you examples and tells you whether you got them right or wrong. 

Common supervised learning algorithms include:

• Linear regression. Predicts continuous numerical values by fitting the best line through data points, such as forecasting house prices based on square footage or predicting sales revenue from advertising spend. 

• Logistic regression. Handles classification problems that categorize data into discrete groups, like detecting spam, fraud, or medical conditions.

• Support vector machines. Finds the optimal decision boundary that separates classes, making them particularly effective for high-dimensional tasks such as image classification and text categorization. 

• Decision trees. Builds interpretable, rule-based models that split data into branches based on feature values, mimicking human decision-making processes in applications like credit approval. 

• Random forest algorithm implementation. Combines multiple decision trees for more robust predictions, like predicting customer churn or assessing loan risk. 

In natural language processing (NLP) systems—which help computers process, understand, and generate human language—large language models (LLMs) and robots use supervised learning to interpret human language by training on large volumes of labeled text, enabling applications like sentiment analysis and chatbots. These models perform best when the data is well-prepared and relevant to the task at hand. Similarly, supervised learning can be used for computer vision applications, where systems process visual input by training on labeled image data sets. This powers technologies such as facial recognition and autonomous vehicle navigation.

In ecommerce, fraud detection systems use supervised learning to identify suspicious transactions. By training on historical data containing both legitimate and fraudulent purchases, the model learns to distinguish normal from abnormal patterns. These algorithms analyze factors like translation amount, location time, and purchasing history to accurately assess new transactions.

Unsupervised

Unsupervised learning algorithms work with unlabeled data, discovering hidden patterns without predetermined categories. These algorithms analyze data to reveal natural groupings and relationships that humans might miss, making them ideal for exploratory data analysis—when you don’t know what patterns exist in your raw data. This approach is particularly valuable when labeling data is expensive or time-consuming. 

Unsupervised learning algorithms include:

• Principal component analysis. Reduces high-dimensional data into fewer dimensions while preserving the most important information, making complex datasets easier to visualize and analyze. 

• Anomaly detection systems. Identifies outliers and unusual patterns that deviate from normal behavior, enabling applications like fraud detection, network intrusion detection, and quality control. 

• Clustering methods. Groups similar data points together to find natural structure in unstructured data, with applications like customer segmentation and document organization. 

Customer segmentation is a common unsupervised learning use case. By analyzing unlabeled data points like purchase history, browsing behavior, and engagement metrics, clustering algorithms group customers with similar characteristics. These clusters reveal distinct buyer personas that you can target with tailored marketing strategies. 

Semi-supervised

Semi-supervised learning algorithms bridge the gap between supervised and unsupervised learning by combining a small amount of labeled data with a larger pool of unlabeled data points. 

This type of algorithm maximizes the value of input data while reducing the burden of creating extensive labeled datasets. Semi-supervised learning is particularly effective for natural language processing tasks, image recognition, and domains where acquiring labeled data requires expert knowledge, such as in medical imaging diagnosis.

In ecommerce, product classification can involve using semi-supervised learning to categorize vast product catalogs. Semi-supervised learning algorithms learn from a few labeled examples and apply that knowledge to classify unlabeled products. When combined with semi-supervised techniques, convolutional neural networks can identify product attributes from images without needing people to label most of them.

Reinforcement

Reinforcement learning algorithms learn through trial and error, receiving rewards for good decisions and penalties for poor ones. Much like training a puppy with treats, these reinforcement algorithms optimize their behavior based on positive or negative feedback from their environment. 

Deep reinforcement learning powers game-playing AI, robotics control systems, and autonomous vehicles. These algorithms must perform tasks in complex environments where optimal decisions depend on understanding long-term consequences (which AI comprehends by mathematically estimating future rewards or punishments for each action). When combined with artificial neural networks, this approach lets AI learn complex patterns of behavior that would be too difficult for humans to program step by step. One useful technique is Sim2Real learning, where AI practices in computer simulations before handling real-world situations that might be too dangerous or impractical to use for training, such as natural disasters or exploring remote areas.

Dynamic pricing is an example of reinforcement learning in practice. Reinforcement learning algorithms continuously adjust prices based on demand, competition, inventory levels, and customer behavior. The system learns which strategies maximize revenue over time, adapting to changing market conditions without explicit programming for every scenario.