Artificial Intelligence (AI) is revolutionizing various industries, enhancing efficiency and solving problems that once required human intervention. While we haven’t reached the stage of Artificial General Intelligence (AGI), the AI models available today can tackle issues that were unimaginable just five years ago. This powerful technology is now accessible to anyone looking to integrate it into their operations.

AI has been a concept for a long time, and we’ve gradually incorporated it into our lives without even realizing it. The notion of what constitutes ‘intelligence’ in technology is constantly evolving. What was once considered smart, like ‘intelligent’ air conditioners in the 1980s that merely regulated room temperature, would not meet today’s standards. The rapid evolution of AI means that what was impressive a few years ago is now commonplace.

Rather than viewing AI as a Swiss Army knife for every problem, it’s more effective to start from the problem itself and determine if AI is the right solution. Falling in love with technology for its own sake can lead to misapplications, as seen with many current implementations of ChatGPT. It’s crucial to identify the problem first and then decide if AI is needed. To do this we first need to understand which problems each different type of AI solves.

Machine Learning

Types of Machine Learning

Machine Learning (ML) is a subset of AI in which many simple and straightforward (mathematically speaking) techniques are used. It can be divided into supervised and unsupervised learning. Supervised learning involves human intervention during the training process, while unsupervised learning does not. Common algorithms in ML include:

• K-Nearest Neighbors (KNN): Ideal for classification problems, KNN can categorize data based on various features. For instance, it can determine the genre of a movie based on its cast, director, and other factors.

Fig. 1 An example of how a KNN algorithm classifies information.

• Linear Regression:This is one of the most basic algorithms in AI or More specifically Machine learning. The idea behind it is to create a Polynomial that predicts a result or value of the given problem. And this is achieved through calibrating the coefficients in the polynomial until. This way the implementer ends up with a good enough prediction for a given set of data.

Fig. 2 Linear Regression

• Logistic Regression: This statistical model is used for binary predictions. For example, it can predict whether a patient has a heart condition based on medical data or if a user will make an online purchase. The idea behind is more or less similar to the Linear regression model, but it uses a Logistic function instead of a linear function to make the cut on whether something is in one group or another.

Fig. 3 Logistic Regression

Tools for Machine Learning

There are several libraries available for implementing these algorithms, such as Sci-Kit Learn, StatsModels, and TensorFlow. There is no need to reinvent the wheel in these matters, using these industry standards should be good enough for most of the problems we encounter day to day.

Decision Trees

Decision trees are a technique used in AI that models data in a tree-like structure. They are particularly useful for classification and regression tasks, providing clear visualizations of decision-making processes. This technique allows its users to get a given set of data and create a tree that will be able to classify all the elements in it, but most importantly it will be able to classify new elements that are not necessarily on the table.

One of the great advantages is that decision trees (as well as Linear Regressors, Logistic Regressors) are not a black box, and their users can figure out what is happening under the hood: one is able to figure out why a decision was made and what it was based on.

Fig. 3 Example of modeling information from a table into a ternary tree.

Deep Learning (Neural Networks)

Simple Neural Networks

Simple neural networks emerged in the 1980s and have since become increasingly prevalent in software development. These networks simulate human neurons mathematically and can perform complex tasks after being trained with large datasets. However, they often function as “black boxes,” making it difficult to understand how they arrive at specific decisions.

Fig. 4 Example of a dense neural network.

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are a specialized type of neural network designed to handle specific types of problems. While they share similarities with conventional neural networks, CNNs excel due to their unique processing capabilities, particularly in analyzing sequential data that occurs over time. This makes CNNs exceptionally useful for applications involving temporal data analysis. For instance, they are widely used in financial data analysis, where they can track variations in asset prices over time, and in event monitoring, where they can identify and analyze patterns and anomalies in sequences of events. These applications demonstrate the powerful ability of CNNs to process and interpret data that changes over time, providing valuable insights and predictions.

Retrieval-Augmented Generation (RAG)

They are a type of technology that allows the creation of ChatBots from supplied information. The information can be provided, for example, through PDF files. This information is then stored in a Vector Database. It is useful, for instance, in cases where the database is filled with a lot of information on a specific topic (e.g., football match results), and then searches are conducted based on this data (e.g., finding all the matches played between team A and team B, identifying the team with the most wins, etc.). It is somehow close to an expert system.

Transformers

Transformers are a specific type of architecture used in artificial intelligence introduced in 2017 through the paper “Attention is All You Need” by Vaswani, Shazeer, and Parmar. This is the technology used in underlying products such as ChatGPT and BERT. Transformers are neural networks that consist of several layers. The main idea in transformers is that each word of the user’s input is converted into an embedding, a mathematical representation of whatever the user has entered. Prior to this, the creators of the transformer must have trained the network with all the information they want to be available (typically very large datasets). Finally, the neural network searches for the information that is closest to the user’s input and generates the response.

Final thoughts

AI is not a one-size-fits-all solution. While ChatGPT is currently popular, it’s just one implementation among many AI technologies available today. The key is to use the right tool for the specific problem at hand. AI should be viewed as a powerful tool to enhance traditional software architectures, solving well-defined tasks under specific conditions.

At Zarego, we are at the forefront of this exciting technology. Our expertise can help you determine if AI is right for your business, identify the most suitable AI solutions, and implement them effectively. Let us guide you through the process of integrating AI to achieve your business goals.