Arthur C. Clarke: “Any sufficiently advanced technology is indistinguishable from magic”
By Galina Chernikova
Technology has advanced so much that our lives now resemble a fairy tale – not a Disney one, but a practical Russian folk tale.
Modern navigation systems are like Ivan Tsarevich’s magical ball of yarn. They quickly calculate routes, warn about traffic jams, and even help avoid obstacles on the road. Of course, in the fairy tale, the ball of yarn was enchanted, but our GPS systems are no less impressive they work anywhere, from dense forests to urban jungles.
In fairy tales, the magical tablecloth would instantly present a feast, but modern technology turns this process into pure enjoyment – after all, what could be more magical than having your favourite meal delivered right to your doorstep?
Technology surrounds us everywhere, and while it may not be entirely magical, its capabilities remind us that fairy tales might be closer than they seem.
The rise of Artificial Intelligence (AI) has radically transformed the way we live and interact with the world. The concept of AI emerged in the 1950s alongside the first computers. The term was introduced by scientist John McCarthy at a Dartmouth College conference to describe research aimed at developing machines capable of solving tasks that require intelligence. The core idea was that intelligence is a set of computational processes that can be replicated using computers.
Many people find the word “intelligence” intimidating because it creates the impression that computers engage in a thought process similar to the human mind. But that’s not quite the case.
Let’s break down what the concept of AI includes. There are various methods, which can be roughly divided into two types: traditional approaches and machine learning. We are more interested in the latter, so let’s quickly go over the first.
Traditional artificial intelligence methods involve solving problems using predefined rules and logical principles. If a computer is assisting in diagnosing diseases, it might rely on a set of rules to determine a possible illness based on symptoms – just like a doctor uses their knowledge and experience.
Other methods help computers find solutions in complex situations. For instance, when determining the fastest route for a journey, a computer can evaluate different options to find the optimal path. Or, in a game like chess, an AI program can analyse potential moves to choose the best one.
There are also methods that allow computers to “think” using strict logical rules. These approaches are often used in language processing or action planning. Sometimes, problems are solved quickly using simple rules that provide a good approximation rather than a perfect solution.
Since these methods rely on clear instructions and logic, they are useful for solving problems in many different fields.
Machine learning, on the other hand, is a method where computers learn to solve problems by analysing data rather than following predefined rules. Instead of explicitly programming instructions, we provide the computer with many examples, and it identifies patterns or relationships in the data to make predictions or draw conclusions.
Imagine we want to teach a computer to recognize paintings created in different artistic styles. To do this, we would show it many examples of paintings by various artists, and the computer would analyse the differences – such as the colours each artist uses or the way they draw shapes. After training, when presented with a new painting, the computer would be able to classify its style because it has learned to recognize these distinctive features.
However, it is important to understand that the computer does not actually “see” paintings the way humans do. Instead, it processes a set of numbers that, to a person, carry no meaningful information. Every image is a grid of pixels, each represented by red (R), green (G), and blue (B) values. A pure white pixel is (255, 255, 255); black is (0, 0, 0).
When we train a computer to recognize paintings, we provide it with images as numerical data and label them with the corresponding artist. For example, one image is labelled as Savrasov, another as Rembrandt. The machine analyses this data, identifies patterns, and learns to distinguish artistic styles. This process is based on complex mathematical analysis and linear algebra techniques.
That all makes sense, you might say, but how does AI create new paintings? Has it really learned to invent something new on its own? Will humans soon become unnecessary? Generative artificial intelligence, such as models for generating images and text, works by studying vast amounts of data and identifying patterns within them. When it creates images, it doesn’t copy existing ones but generates new ones by combining colours, shapes, and textures based on the examples it has been trained on. When it writes text, it doesn’t think – it simply predicts the most likely word or phrase in a given context, drawing from millions of texts it has processed.
I work as a Data Scientist in a healthcare company, where my job revolves around machine learning and data analysis. Our team tackles challenges that span the entire business. We analyse advertising effectiveness, develop models that help answer consumer questions – ranging from choosing new product flavours to addressing sensitive issues about medication dosages. We also predict flu outbreaks to ensure pharmacies are stocked with the necessary medicines in advance. In manufacturing, we work with data streams from factories, helping to automate quality control and optimize production. My job isn’t just about crunching numbers – it’s about finding solutions that make healthcare more accessible and efficient. Technology is transforming healthcare, and being part of this change is truly exciting.
There’s no need to fear AI taking jobs. Yes, it is changing our lives, and it’s difficult to predict where technological progress will lead us. But it’s important to remember that technology expands our capabilities rather than taking them away. Once upon a time, the loom replaced hand weaving, but that didn’t eliminate the profession of textile production – it actually fuelled the rise of fashion as an art form. Freed from tedious manual labour, people were able to focus on creativity and the search for new materials and meanings.
The same is true for AI: it takes over repetitive, monotonous tasks, leaving humans to do what requires imagination, critical thinking, and emotional intelligence. Instead of fearing change, we should learn to use new tools to our advantage. After all, technology is simply an extension of our magic – meaning there are still many incredible discoveries ahead.
