From the beginning, AI has been perceived as something almost magical, with definitions primarily highlighting its capabilities rather than the underlying components or operational principles. When attempting to define artificial intelligence, our focus often centres on its features and functions. This has generated a demand for a straightforward definition rooted in the technical foundations of AI. Only in this way can we make it more accessible in everyday conversations.
AI definitions based on its functions
One of the existing definitions argues that AI is about machines, particularly computer systems, mimicking human intelligence processes. It has various practical applications, such as expert systems, natural language processing, speech recognition, and machine vision.
Another definition sustains that AI is about “computers and machines that mimic the problem-solving and decision-making capabilities of the human mind”. Britannica calls it the capacity of a computer or a computer-controlled robot to perform tasks typically executed by humans, tasks that demand human intelligence and judgment, that can rival humans in specific tasks.
Hence, the great wave of enchantment for AI, because it is not fully understood. AI is perceived as a miracle because most definitions highlight its complexity, designed to simulate human thinking and action. Yet, this aspect does not make it magical, and certainly not better than the human brain. But let us dive deeper into the meanings and functions of AI to understand it better.
What is AI at a technical level?
Most of the existing studies identify AI systems with complex systems of neural networks, similar to the human neuronal networks. But by drawing constant parallels between the brain and AI, although we try to offer an easy-to-understand explanation, we can often complicate things, especially when knowledge about the brain is limited, and we do not understand completely how the human brain works either. So, the best way to understand AI is by looking at the processes that build and design the AI networks for learning. Let’s see what AI is at a technical level and what neural circuits look like.
How does AI work?
A neural network comprises artificial neurons, often known as perceptrons, which serve as computational units for data classification and analysis. Starting from this, here is a general description of how perceptrons work for data classification and analysis:
- Data is input into the initial layer of the neural network.
- Each perceptron from the first layer makes decisions and passes this information to multiple nodes in the subsequent layer.
- If a decision has not been made, or if the answer is not right, it uses another layer of nodes to analyse the data, and so on until the expected or required answer is provided (the level of layers the AI uses also depends on the type of AI and the programming of the neural networks).
It is important to know that there are models with more than three layers that are commonly termed ‘deep neural networks’ or ‘deep learning,’ and they can possess hundreds or even thousands of layers. The ultimate output from the final perceptrons is what enables the neural network to accomplish its designated task, such as object classification or data pattern recognition.
This explanation of the functioning of AI covers the technical intricacies of programming and analysis. Nevertheless, in most discussions about how AI operates, experts in the field prefer to describe it by categorizing the types of AI and their respective functions.
Sources:
Craig Lev, 2024. What is AI (Artificial Intelligence)? Definition, Types, Examples & Use Cases. Available at: https://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence
Stryker Cole, 2024. What is artificial intelligence (AI)? Available at: https://www.ibm.com/think/topics/artificial-intelligence
Copeland B.J., 2025. Artificial intelligence. Available at: https://www.britannica.com/technology/artificial-intelligence