Artificial Intelligence (AI) refers to computer systems that perform tasks that imitate human thinking, such as learning, reasoning, problem-solving, or communication. Developers aim to create algorithms that can perceive their environment, make decisions, and act or improve independently based on those decisions.

Main categories of AI by capability (intelligence level)

• Artificial Narrow Intelligence (ANI). This is the most common form of AI today. It is designed for a specific task, such as facial recognition, natural language processing, or running recommendation systems. The key point is that ANI often performs its task faster or more accurately than humans, but it cannot go beyond its specialized area. It does not think “generally” and cannot adapt to unfamiliar, novel situations as humans can.
• Artificial General Intelligence (AGI). A higher level of AI that currently exists only in theory. The aim is to build machines that could think and learn at a human level — in any domain. Such systems would not only solve a single task but could also reason, plan, and solve problems like a human being. Although no AGI exists yet, many researchers are actively working on it.
• Artificial Superintelligence (ASI). This concept borders on science fiction, yet it is seriously discussed. ASI would surpass human intelligence in all domains — it would think faster, be more creative, and solve problems more effectively than humans. While exciting, it also raises significant ethical and safety concerns. For example, who would control such a system, and how could we ensure it is used for good purposes?

Categories of AI by mode of operation

1) Predictive AI—Forecasting. This type of AI uses past data to predict what is likely to happen in the future.

Typical applications:

• Weather forecasting
• Financial trend prediction (e.g., stock markets)
• Customer behavior prediction
• Predictive maintenance in industry

Keyword: Probability. Predictive AI does not make specific predictions but provides probabilities based on past data.

2) Generative AI—Content Creation. It produces new text, images, music, or video based on learned patterns.

Examples:

• Text generation (e.g., ChatGPT)
• Image generation (e.g., DALL·E, MidJourney)
• Music composition
• Video animations

Keyword: Creativity. Generative AI functions like a diligent artist: it learns extensively and then creates new content in its own style.

3) Classification and recognition AI—Pattern identification. It recognizes and categorizes input data, such as images, sounds, and movements.

Examples:

• Facial recognition (e.g., in security systems)
• Speech recognition (e.g., voice assistants)
• Medical image analysis (e.g., X-ray interpretation)
• Autonomous vehicles (object and lane detection)

Keyword: Pattern. This type does not interpret meaning but excels at rapid and accurate recognition.

Machine Learning

Machine Learning (ML) is one of the most important fields of AI. Its essence is that computer systems learn patterns from data and examples, enabling them to generalize. Instead of following pre-programmed rules, ML algorithms learn from experience, processing datasets to solve tasks. This allows an app to recognize faces, recommend movies, or translate texts. In other words, AI is the broader goal (human-like intelligence), while ML is the “engine” that enables systems to adapt and improve.

Three main approaches to machine learning:

• Supervised learning: Algorithms are trained with labeled data (e.g., images labeled “cat” or “dog”), and later the system can identify new examples correctly.
• Unsupervised learning: There are no labels; the algorithm must find patterns, clusters, and similarities independently (e.g., identifying customer segments).
• Reinforcement learning: The system learns by trial and error, receiving rewards for correct actions and penalties for mistakes — similar to learning to play a game by practice.

Large Language Models (LLMs)

Large Language Models are AI systems trained on vast text corpora to recognize linguistic patterns and generate new text. Examples include GPT (OpenAI), BERT (Google), and Claude (Anthropic).

Applications:

• Text writing and summarization
• Translation
• Question–answer systems
• Customer service chatbots
• Research assistance
• Programming support

How do LLMs learn?

LLMs are based on machine learning, particularly deep learning. They are trained on massive text datasets (books, articles, web pages) and learn statistical relationships between words and expressions, such as which word is likely to follow another in context. This does not mean accurate “understanding” but pattern recognition and probability estimation.

Training often occurs in two phases: pre-training, which involves learning general linguistic patterns, and fine–tuning, which involves adapting to specific tasks or domains (e.g., medical texts, customer service dialogues).

Opportunities and limitations of LLMs

Opportunities:

• Processing and summarizing large amounts of information.
• Rapid text generation, idea creation, and translation.
• Effective in structured tasks (e.g., code generation, creating document outlines).
• Flexible integration into different applications.

Limitations:

• Do not possess real knowledge or understanding – operate probabilistically.
• May produce incorrect, imprecise, or outdated information.
• Sensitive to data quality: biased data leads to biased results.
• High computational and energy demands raise sustainability concerns.

The phenomenon of “hallucination”

One common problem of LLMs is “hallucination,” which is producing plausible but factually incorrect or entirely fabricated information. This happens because the model does not “know” reality – it simply continues linguistic patterns. It may “invent” quotes, sources, or facts that do not exist.

Why should AI-generated content always be verified?

LLM outputs are often well-formulated, making them appear convincing. They may present conclusions that sound logical but are unsupported by facts. Critical source-checking is essential, especially in research, education, or professional work. AI-generated text is not a source; it must always be compared against reliable literature and verified data.