How artificial intelligence works: principles, algorithms, and methods

AI has long been a part of everyday life, but for many it still sounds like something complex and distant. In fact, there is no magic behind its “mind,” just algorithms, data, and the ability to learn.

In this article, together with our expert Yevhen Kasyanenko, we will analyze how artificial intelligence works, how it makes decisions, and why its approach to tasks is completely different from that of conventional programs.

What is artificial intelligence and how does it work?

Artificial intelligence is a computer technology that allows machines to independently analyze data and solve problems. Adaptability is the main distinguishing feature of AI, thanks to which it improves itself without direct programming.

“The difference from conventional programs is simple: they always act according to a pre-written script — press a button, get a result. AI can change its approach, come up with new solutions, and improve with experience,” says Yevhen  Kasyanenko.

The main areas of artificial intelligence

It is important to understand that AI is not a single magical technology; it encompasses a whole range of different approaches, each with its own role:

1. Machine learning (ML). The system is “fed” huge amounts of data, and its built-in algorithms look for patterns and use them in their answers or predictions: whether a customer will buy a product, whether to issue a loan, what the demand will be next week.
2. Deep learning (DL). This is an advanced version of machine learning, already with neural networks similar to the brain. They are what enable speech recognition, text generation, and image creation. ChatGPT, DALL·E, and Midjourney work this way.
3. Natural language processing (NLP). This allows AI to “understand” and create texts. Thanks to NLP, chatbots can hold conversations, and translators can accurately convert texts between languages.
4. Computer vision. Gives AI “eyes,” meaning it can see and recognize what is happening in photos and videos. This is used in medicine, on the roads, in manufacturing, and even in retail—from diagnosing images to tracking shoppers in stores.

Each of these areas is actively developing and will gradually change how companies work and the quality of our lives.

Key technologies that hold everything together

For all this to work, you need tools “under the hood” that turn algorithms into living AI systems:

• Neural networks. These are essentially mathematical models that mimic the work of the brain: they receive a signal, transmit it through “neurons,” and output a result. They are used almost everywhere, from photo editors to voice assistants.
• Machine learning algorithms. They find patterns in large amounts of data and learn to predict results. They work on the principle of “show, remember, predict.”
• Genetic algorithms. They search for the best solution using natural selection. They are useful when there are too many options and you need to find the optimal path (for example, for logistics).
• Reinforcement learning. AI learns from its own actions: it receives “rewards” for correct steps and “penalties” for mistakes. This approach is used, for example, in training game AI or in autonomous control systems.

Thanks to these technologies, AI does not simply execute commands, but learns through practice. It adapts to new data, draws conclusions, and over time works with increasing accuracy.

How artificial intelligence works

Before AI starts to “think” and make decisions, it goes through a process similar to human learning: first it learns, then it takes an exam, and only then does it start working in its field. This can be divided into five stages:

1. Data collection. AI cannot start “learning” without information. At this stage, data is collected—this can be numbers, texts, images, camera recordings, or even user behavior logs. The more and cleaner the data, the better.
2. Preparation and processing. Raw data is not the best teacher. It needs to be tidied up: duplicates removed, errors corrected, format standardized. Otherwise, the model will learn nonsense and make mistakes.
3. Training. Now the model “learns,” that is, it runs the data through algorithms, looks for patterns, and memorizes how causes and effects are related. It’s as if AI were reading a bunch of case studies and trying to figure out for itself how to act in similar situations.
4. Testing. The model is tested on new data that it has not seen before. It’s like giving a student an exam on topics they didn’t know about in advance to check whether they really understood the material or just memorized the answers. If the results are mediocre, the model is “tweaked”: it is retrained, its settings are adjusted, or it is completely rebuilt.
5. Implementation and adaptation. When the AI has passed all its “exams” with flying colors, it is sent out into the real world to work. Here, it selects products in online stores, helps doctors make diagnoses, analyzes video from cameras, and solves a host of other tasks.

“The most interesting thing is that with each new task, AI becomes smarter because it continues to learn on the fly, in real-world conditions,” emphasizes our expert.

Basic artificial intelligence algorithms

Algorithms are the engine of AI. They allow the system to learn, find connections in data, and make decisions. Different approaches are used depending on the task, but the main ones are classical machine learning and complex neural networks. More on these below.

Machine learning algorithms

Machine learning is a set of algorithms that can find connections in data and make predictions based on them. Here are the most popular methods:

• Decision trees. Imagine a series of questions like “if yes, then go there; if no, then go here.” That’s how decision trees think. They create a logical structure that leads step by step to the desired result. They are often used in recommendation systems, medicine, and decision automation.
• Random Forest. When one tree is not enough, they make a whole forest. This method combines many decision trees, each of which votes for the result. The result is more accurate and stable predictions. It works well in credit scoring, diagnostics, and demand forecasting tasks.
• Logistic regression. Despite the name, this is not about logic and not entirely about regression. This algorithm predicts the probability of an event, such as whether a user will click a button or not. It is often used in marketing, HR analytics, and customer classification.
• Support vector machine (SVM). If you need to clearly separate “friends from foes,” “risk from no risk,” or “spam from non-spam,” this algorithm is ideal. It searches for the boundary that most accurately divides the data into categories. It works well even on small but high-quality samples.
• Gradient boosting (XGBoost, CatBoost, and others). One of the most powerful tools in the ML arsenal. It collects a bunch of “weak” models (most often just trees) and teaches them to complement each other. The result is a super-accurate system that is used in fintech, medicine, and predictive analytics systems.

“A good algorithm is half the battle. But it’s important not just to choose a trendy method, but to understand what works best for your specific task. Sometimes simple logistic regression will be more useful than a neural network on steroids,”adds Yevhen  Kasyanenko.

Neural networks

If artificial intelligence has a heart, it is neural networks. They process huge amounts of data, and their work is modeled on the human brain.

Inside a neural network live dozens, hundreds, and sometimes millions of “neurons” — small computing nodes. Each such “neuron” receives data, processes it, and passes it on. Gradually, the network learns to distinguish important signals from noise, find patterns, and draw conclusions.

The most commonly used types of neural networks today are:

• Convolutional neural networks (CNN) – masters at working with images. They recognize faces, find tumors on MRIs, analyze surveillance camera footage, and sort photos by theme. They work like smart filters: they pick out the essentials and cut out the excess.
• Recurrent neural networks (RNN) are specialists in sequences. They are used in speech recognition, text translation, and time series analysis—from customer behavior to price fluctuations. They “remember” previous steps and use this to predict the next one.
• Generative neural networks (GAN and similar) are true creators. They paint pictures, invent photos of people who never existed, write music, and even poetry.

Thanks to neural networks, AI is no longer just a tool on demand, but almost a full-fledged interlocutor or assistant that can understand, create, and adapt to humans.

Methods of training artificial intelligence

In order for AI to begin to “understand” how to act in different situations, as we already know, it needs to be trained. And here it all depends on what data we have and how much we are willing to “prompt” the system at the start. There are three main approaches, each with its own advantages and tasks.

Supervised learning

It’s like a textbook with the right answers. We give the model examples where the correct result is already known: for example, photos of cats and dogs with captions. The model learns to find differences, memorizes patterns, and then predicts who is in the new photo.

This method is most often used where a clear “yes/no” or prediction is needed: from spam filtering to credit risk assessment.

Unsupervised Learning

Here, the model learns on its own, without prompts. It is simply given a bunch of data and tries to bring order to it: find similar objects, group them, and highlight unusual cases.

This approach works well in marketing (customer segmentation), security (anomaly detection), and big data analytics.

Reinforcement Learning

It’s like a game: do it right and get a reward, make a mistake and get a penalty. The model interacts with the environment, tries different actions, and learns from its mistakes.

This is how AI that plays chess, controls robots, or drives driverless cars is trained. Over time, the model becomes “smarter” because it remembers what worked and what didn’t.

How artificial intelligence is used

AI is already working in many areas, taking on routine tasks, helping to make decisions faster, and saving companies a lot of time and money. We encounter it much more often than we think:

• Business. AI knows what customers like and will tell you when it’s time to start a sale. It can write text for a newsletter or social media post, and a chatbot will answer questions in such a way that you won’t notice the difference from a live operator. Marketplaces already tailor their storefronts to each buyer, and sometimes it seems like they can read your mind.
• Finance. Algorithms evaluate loan applications in seconds, catch suspicious transactions, and calculate risks while a regular employee is still opening a spreadsheet. This helps banks make decisions almost instantly and prevent fraud at the outset.
• Medicine. Neural networks see things on MRIs and X-rays that may escape the doctor’s eye: early signs of tumors, microcracks, or abnormalities in tests. AI does not replace doctors, but it gives them a powerful tool to make more accurate diagnoses.
• Transportation. Driverless cars are already being tested in cities, and smart navigators with AI predict traffic jams half an hour before they occur and suggest detours. Logistics services optimize routes so that cargo arrives faster and cheaper.
• Cybersecurity. AI monitors networks around the clock, notices strange behavior, and blocks threats before they can cause damage.

In essence, AI has already become our invisible assistant: it advises, prompts, filters, and protects, even when we are not thinking about it. And, to be honest, this is just the beginning.

Risks and ethical aspects of AI

AI opens up a host of new opportunities, but with them come some serious risks that cannot be ignored. The smarter machines become, the more questions arise: Are they doing everything honestly? Where is our data going? Who will be left without a job? To be more specific:

• Privacy is at risk. AI works with millions of lines of personal data, from website behavior to medical images. And if we don’t take care of protection, leaks are inevitable. Companies are required to implement reliable security systems and strictly follow data laws.
• Algorithms can also be biased. If AI is “fed” inaccurate or one-sided data, it will repeat the same mistakes. This will result in unfair decisions in hiring, lending, or even in the justice system. Transparency is important here: model verification, data cleansing, and control over results.
• Workplace automation is a reality. AI already handles many routine tasks, and yes, some professions are disappearing. But at the same time, new ones are emerging: we need people who can work with data, configure AI, and analyze results. It is not the strongest who survive, but the most adaptable.

“AI is a tool, not a judge. It should not replace humans in making critical decisions. Therefore, the main thing here is to maintain a balance between technology and responsibility,” emphasizes Yevhen  Kasyanenko.

The future of artificial intelligence

AI is not just developing, it is racing ahead at full speed, changing the world faster than we can get used to it. And we are really only at the beginning of the transformation of the world. In the coming years, artificial intelligence will become smarter, more independent, and will penetrate even more areas of life, from factories to home assistants.

Let’s highlight a few important aspects in this regard:

• More autonomy. AI is learning to learn on its own. There are already systems that adapt to new data on the fly, without the intervention of a programmer. In the future, such models will be able to make decisions in real time, predict events more accurately, and react instantly to changes.
• Maximum automation. Factories without people, warehouses with robots, stores without cashiers—all of this is already being tested or implemented. AI will manage logistics, procurement, production, and services. The result will be lower costs, fewer errors, and higher speed.
• Ethics and control. The smarter AI becomes, the more important it is that it plays by the rules. There is already a growing demand for “transparent” algorithms – ones whose work can be explained and verified.

“AI is indeed predicted to play an infrastructural role in the future. How we deal with it today will determine what our tomorrow will be like,” notes our expert.

Conclusion

Artificial intelligence is becoming an integral part of our reality, transforming routines and giving businesses a boost to growth. Those who are the first to harness the technology will conquer the market. But for AI to really work for you, it is important to understand how it works and where it is most beneficial.

At KISS, we create AI solutions that not only sound good but actually work: they automate, simplify, and strengthen your business. Want to figure out how to implement artificial intelligence for your tasks? Contact us right now—we’ll advise, demonstrate, and configure it for you.