What Are AI Algorithms, and How Do They Work?

AI algorithms are sets of instructions that tell artificial intelligence technology how to process information, react to data, and make decisions autonomously.

In essence, these algorithms help AI models perform nuanced tasks that would typically require human intelligence, such as solving complex problems or understanding natural language inputs.

Programmers can tightly control algorithms at every stage or leave them unsupervised to recognise patterns without human intervention. It all depends on the method used and the organisation’s goals.

Let’s explore how AI algorithms work, the techniques used to train them, and their real-world applications.

You could think of AI algorithms as smart instructions that look for patterns in data. Over time, they get better at tasks without a human constantly telling them what to do.

In contrast, algorithmic AI follows a strict set of instructions. Algorithmic AI doesn’t learn like AI algorithms. Rather, it solves problems by following fixed steps, such as searching for the best move in a board game.

In short, AI algorithms are built to improve and adapt, whereas algorithmic AI sticks within predetermined rules. Both use systematic instructions. Only AI algorithms can truly evolve over time.

• AI algorithms adapt and improve with new data
• Algorithmic AI sticks to predetermined rules and logic
• AI algorithms can handle changing conditions better, while algorithmic AI follows the same path every time

AI algorithms work by analysing and exploring training data to discover patterns and learn over an extended period. Let’s break down the elements that make this possible.

Pattern recognition allows algorithms to discover similarities and recognise rules by repeatedly analysing and learning from data. The algorithm can then use this understanding to make autonomous decisions.

Consider how Netflix’s AI algorithm learns your viewing habits and uses them to make content recommendations. Or the way in which Alexa becomes better at recognising your voice with time. The more AI algorithms can learn your preferences, the better they’ll be at making personalised predictions.

Of course, an AI algorithm can’t be right all the time, which is why it needs to iterate its approach based on feedback.

For instance, if a Netflix recommendation diverges from your usual viewing habits, the algorithm will take this into account, adjust its parameters, and tweak its content predictions. This iterative learning process is why you’ll often hear that AI models can learn and improve with time.

As you might expect, learning to make decisions autonomously means AI algorithms require enormous amounts of training data. The more unbiased, high-quality information they receive, the better they can improve their knowledge and refine their approach.

AI developers can label rich datasets with tags and classifications to help an AI algorithm understand the context of the information.

Developers also work with unlabeled data, which is raw data without annotations. This raw data requires the algorithm to identify patterns on its own without guidance or human intervention.

Let’s dive deeper into the different types of AI algorithms and how they differ.

There are four primary categories of AI algorithms:

• Supervised learning algorithms
• Unsupervised learning algorithms
• Semi-supervised learning algorithms
• Reinforcement learning algorithms

What sets each of these algorithm classifications apart is how developers train them and how trained and how they function. Let’s explore each one by one.

Supervised learning algorithms require programmers to feed in labelled, classified datasets. This annotated data matches inputs with corresponding outputs, making it easier for the algorithm to recognise patterns and improve its accuracy with time.

There are many subtypes of supervised learning algorithms, such as:

• Linear regression: Linear algorithms plot data on a graph and use this to determine trends. The algorithm uses this information to predict future outputs.
• Logistical regression: This common algorithm predicts probabilities from a set of independent variables and maps them to binary values (0/1).
• Decision trees: These algorithms split data into branches to represent all possible outcomes. Each split represents a different decision rule, creating a tree-like structure that is easy to interpret.
• Random forest: This method combines multiple decision trees together to achieve more accurate results.
• Naive Bayes: This structure is based on Bayes’ theorem. A Bayesian network relies on the assumption that one feature is unrelated to other features of the same class.
• Support vector machines (SVM): These algorithms plot each bit of data on a graph. The algorithm then classifies points by finding the optimal boundary hyperplane between each class in the dataset.

All of these algorithms require labelled data. That said, this doesn’t mean supervised algorithms can only make decisions with human input.

Over time, as an AI algorithm learns and draws patterns from datasets, it can begin to predict outcomes for new, unseen inputs autonomously.

With unsupervised learning, the algorithm takes in unlabelled, raw data and uses it to identify trends and correlations.

Unlike supervised learning, the algorithm receives no contextual clues from datasets — it discovers patterns entirely of its own accord. Common types of unsupervised learning algorithms include:

In this model, cluster algorithms group together unlabeled data points based on shared characteristics. This includes several different subtypes, such as:

• K-means clustering
• Hierarchical clustering
• Density-based clustering
• Spectral clustering
• Mean-shift clustering

This technique uses a rule-based machine learning method to discover links between parameters in large datasets. It comprises several subtypes:

• Apriori algorithm
• Eclat algorithm
• Frequent pattern growth algorithm

Dimensionality reduction involves reducing the features in a dataset while maintaining the essential details. For instance, refining 100 data points down to the two crucial data points to make data visualisation easier. It includes algorithms like:

• t-Distributed stochastic neighbour embedding (t-SNE)
• Principal component analysis (PCA)
• Linear discriminant analysis (LDA)
• Locally linear embedding (LLE)

It is important to note that unsupervised learning algorithms are growing in popularity due to advancements in generative AI technology. Unsupervised learning algorithms are commonly used for exploratory data analysis, where the algorithm's end goal isn’t as clearly defined.

In this dynamic, the ethos focuses on exploration as the algorithm learns and develops. With unsupervised learning algorithms, AI engineers work to ensure appropriate guardrails are in place to maximise positive outcomes and minimise any potential risks.

Reinforcement learning algorithms encourage AI technology to discover the best outcomes through a trial-and-error approach. They learn by receiving feedback in the form of positive or negative reinforcements.

Reinforcement algorithms consist of an agent and an environment. The environment sends a signal or query to the agent, prompting the agent to act.

Following this action, the environment provides the agent with a positive or negative reward so it can refine its understanding. This repeats indefinitely until the environment ends the cycle.

There are three broad subtypes of reinforcement learning:

• Model-based algorithms: In this instance, a programmer makes various environments with different dynamics. This allows the agent to adapt to and learn from several unique situations.
• Policy-based algorithms: This algorithm focuses on creating a policy that maps states to actions without using a specific value function.
• Value-based algorithms: This unique form of algorithm means the agent understands and works toward a long-term goal rather than just focusing on the next action and reward.

These algorithms aren’t as common as supervised learning algorithms, but they have their use cases. They’re often used in autonomous vehicle testing and robotics, a clear use case in which AI developers need to refine decision-making to encourage our desired behaviours.

Semi-supervised algorithms combine the strengths of labelled and unlabeled data and are commonly used when obtaining labelled data is challenging or resource-intensive.

Still, some are required to ensure the algorithm remains on course to reach a specific goal. In this sense, the labelled data can help the algorithm better understand the unlabeled information it encounters.

Once AI developers have decided which type of AI algorithm is best suited to their needs, they need to kickstart training with real-world data. There are several different techniques to use, each with its advantages:

In this training technique, developers feed the entire training dataset to the algorithm in one large batch at once (or at least in large chunks). The model then processes the data to identify patterns. It updates its parameters accordingly.

This training method is common in deep neural networks trained offline, such as image processing models, e.g., convolutional neural networks (CNNs) that require significant computing power and memory. These might analyse thousands of photos at a time and then refine their classification rules in one go.

Online training does not use a fixed dataset, as is the case with batch training. Instead, the algorithm learns continuously from new data points as they arrive.

This learning method is ideal for applications that need to adapt to changing environments, such as predictive maintenance for industrial machinery.

In the training process, each incoming sensor reading updates the model immediately, allowing it to predict equipment failures before they occur, ensuring timely interventions and minimising downtime.

With incremental training, developers update the model at regular intervals rather than allowing for real-time updates.

For example, an email categorisation filter can learn from user interactions, such as moving emails into folders or marking them as important. This allows the system to continuously improve its understanding of what matters most to the user and organise incoming emails in a way that better aligns with their preferences, helping them stay organised and prioritise more effectively

This type is like an AI algorithm cheat code. Developers take a model already trained on a large, general dataset. For example, an AI system designed for image recognition that they know works. They’ll then fine-tune it on a smaller, specific dataset. This saves time and resources, enabling machines to learn from existing knowledge and a working model instead of starting from scratch.

Developing a useful, intelligent system is about more than choosing the right algorithm and letting it fly. How you train that algorithm matters, too. The desired output is reliant upon this process. Here are some guiding principles to bear in mind:

Make sure the training data set is accurate, diverse, and representative. The quality of the input data directly shapes the output of an AI algorithm. By using well-curated data, you can help ensure fair and balanced outcomes. For example, a well-designed hiring algorithm that uses representative data can make more inclusive and equitable decisions.

Online training does not use a fixed dataset, as is the case with batch training. Instead, the algorithm learns continuously from new data points as they arrive.

This learning method is ideal for applications that need to adapt to changing environments, such as predictive maintenance for industrial machinery.

In the training process, each incoming sensor reading updates the model immediately, allowing it to predict equipment failures before they occur, ensuring timely interventions and minimising downtime.

With incremental training, developers update the model at regular intervals rather than allowing for real-time updates.

For example, an email categorisation filter can learn from user interactions, such as moving emails into folders or marking them as important. This allows the system to continuously improve its understanding of what matters most to the user and organise incoming emails in a way that better aligns with their preferences, helping them stay organised and prioritise more effectively

This type is like an AI algorithm cheat code. Developers take a model already trained on a large, general dataset. For example, an AI system designed for image recognition that they know works. They’ll then fine-tune it on a smaller, specific dataset. This saves time and resources, enabling machines to learn from existing knowledge and a working model instead of starting from scratch.

Your training is never a one-and-done thing. Keep an eye on performance. If you find the performance skews, then this could be the result of shifting conditions, for example, a new market trend. In the case of a new market trend, you may need to refine the AI algorithm to maintain its accuracy and usefulness.

AI algorithms are driving innovations and improving customer experiences across nearly every industry. Let’s spotlight a few examples from Australia showing how businesses are leveraging the latest developments of AI algorithms:

Many companies are using AI algorithms to offer personalised product recommendations. Think of ecommerce stores or streaming platforms as the obvious examples. The premise is to use customer data to offer more bespoke recommendations, giving visitors a customised ‘just-for-them’ experience that resonates.

Australian retailer Big W, for instance, employs machine learning models to analyse data points like customer purchase history, enabling machines to learn more about consumer preferences. In turn, these insights can be used to power better product suggestions and targeted promotions.

Many organisations also tap into Salesforce’s Artificial Intelligence for predictive insights and marketing analytics to help with their marketing efforts, helping them segment audiences or refine email campaigns.

Medical professionals and AI developers are teaming up to create AI technologies that enhance disease diagnosis and drug discovery. More recently, AI has been used to offer more personalised medicine and care plans, filling in where doctors may not be needed or are overworked.

Healthcare is one of the most exciting fields for AI innovation since there is a clear incentive to explore better patient outcomes. For example, advanced deep neural networks can detect early-stage cancers from medical images with high accuracy.

CSIRO’s data61 collaborates on AI-driven health initiatives in Australia, using computer vision to s pot anomalies in scans that might otherwise go unnoticed.

They are working with Australian companies like Eyes of AI, which specifically focuses on dentistry. They use AI tools to detect potential signs of jaw cancer.

In the financial sector, algorithms support real-time anomaly detection to combat fraud and maintain credit scoring. Commonwealth Bank, for example, uses data-driven AI tools to identify suspicious transactions and improve risk assessment, helping protect customers and the institution from losses.

As long as AI algorithms are trained well, they can outperform the human brain’s capacity to spot and protect against threats. Utilising them is a clear boost to the service that banks can provide their customers.

The key point here is that customers recognise these tools enhance their experience, provided they still have the option to speak to a human when a personal touch is needed for their needs or more complex inquiries.

Across these industries and others, AI algorithms allow organisations to make more informed decisions, automate repetitive tasks, and uncover insights that drive competitive advantage.

More importantly, though, as these examples have shown, they are driving better experiences for customers.

AI algorithms form the backbone of artificial intelligence, empowering machines to learn, adapt, and make decisions with minimal human input. In the process, they are outstripping human intelligence and capabilities in many ways.

Whether you’re working with supervised learning, unsupervised approaches, or reinforcement methods, the key is training your model effectively, from high-quality data collection to continuous performance monitoring.

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