This content has been automatically translated from Ukrainian.
Artificial intelligence is already helping to search for information, translate texts, create images, recognize faces in photographs, and plan routes. However, AI is not a single program or a digital mind that exists separately from computers. Behind this name lies a large group of technologies capable of performing tasks that previously required human involvement.
What is artificial intelligence
Artificial intelligence, or AI, is a field of computer science focused on creating systems that can analyze information, find patterns, make predictions, make certain decisions, or generate new content.
In English, artificial intelligence is called artificial intelligence, hence the common abbreviation AI.
AI technologies include:
- facial, object, and speech recognition;
- automatic translation;
- recommendation systems;
- weather, demand, or risk forecasting;
- generation of texts, images, music, and videos;
- control of robots and autonomous machines;
- detection of suspicious banking transactions;
- analysis of medical images.
Therefore, a voice assistant, the YouTube recommendation algorithm, and a chatbot based on a large language model are different systems, although they all use AI.
Most modern developments belong to what is called narrow artificial intelligence. It can perform one task or a specific set of tasks very well, but it does not possess universal human intelligence. Even the most powerful language model does not understand the world like a human, does not have its own life experience, and lacks consciousness.
How AI works
In a traditional program, the developer specifies concrete rules: if a certain event occurs, the system must perform a defined action. In machine learning, the approach is somewhat different. The algorithm is given a large number of examples so that it can find statistical patterns within them.
For example, a cat recognition system is trained on a large selection of images. During training, the model gradually determines which combinations of shapes, lines, colors, and textures are most often associated with cats. After this, it can assess whether a cat is present in a new photograph.
The result of such training is called a AI model. It is a mathematical system configured to perform specific tasks. At the same time, a model and a ready service are not the same. For example, ChatGPT is a product through which users interact with GPT models. Similarly, Gemini is both the name of a family of Google models and the name of a chatbot created based on them.
A separate direction is neural networks — systems whose structure vaguely resembles the interaction of neurons in the brain. When a neural network has many sequential layers, it is referred to as deep learning. This has become the foundation of most modern recognition and generation systems.
What is generative AI
Artificial intelligence became particularly noticeable with the spread of generative models. They not only classify or find information but also create new content: text, images, code, audio, or video.
Large language models, or LLM, are trained on vast amounts of text. Upon receiving a prompt, such a model predicts the most relevant continuation step by step. This is significantly more complex than regular autocomplete: the system takes into account context, style, sentence structure, and relationships between concepts.
However, a language model does not retrieve a ready answer from a hidden encyclopedia. It generates text based on learned patterns. This is why the response may sound convincing but contain errors or fabricated facts. This phenomenon is referred to as AI hallucination.
Modern models are increasingly multimodal. They can work not only with text but also with photographs, documents, tables, sound, and video. For example, such a system can read text from a photograph, explain a diagram, or answer questions about an depicted object.
How the history of artificial intelligence began
Ideas about creating a mechanical mind existed long before the advent of computers. However, transforming them into scientific tasks began only in the 20th century.
An important figure in the early history of AI was British mathematician Alan Turing. In a 1950 article, he posed the question: "Can machines think?" Instead of searching for an exact definition of thinking, Turing proposed a practical test, later called the Turing test. If during a text conversation a person cannot reliably distinguish a machine from another person, the behavior of the machine can be considered intelligent.
The term artificial intelligence itself appeared in 1956. It was proposed by American scientist John McCarthy for a summer research seminar at Dartmouth College. This meeting is often referred to as the symbolic beginning of AI as a separate scientific field.
In the 1950s and 1960s, researchers created programs that solved logical problems, proved theorems, and played simple games. It seemed that the advent of machine intelligence was just around the corner. However, early computers had too little computational power, and the manually created rules worked poorly outside narrow laboratory conditions.
Has a machine passed the Turing test?
For a long time, there was no definitive answer to this question, as the Turing test does not have a single official regulation or universally recognized passing score. Researchers conducted conversations of varying lengths, involved different numbers of judges, and evaluated results differently. Therefore, claims of "the first passing of the test" often sparked controversy.
In the original 1950 article, Turing did not set 30% as the official threshold for successful passing. He predicted that in about 50 years, computers would learn to play the imitation game so well that after five minutes of questioning, the average participant would correctly identify the machine no more than 70% of the time. Subsequently, this assumption began to be interpreted as a guideline: a machine must convince at least 30% of judges that it is a human.
One of the most famous attempts was the 2014 experiment conducted in London involving researchers associated with the University of Reading. The chatbot Eugene Goostman, which pretended to be a 13-year-old boy from Ukraine, convinced 33% of judges that they were talking to a human. The organizers called this the first passing of the Turing test, but many experts criticized this conclusion. The image of a young interlocutor, for whom English was supposedly not a native language, allowed for explanations of strange responses, language errors, and gaps in knowledge. Additionally, the discussion was sparked by the small number of judges and the short duration of conversations.
More convincing results were demonstrated by modern large language models. The research was initially published as a preprint in 2025 and later published in the scientific journal Proceedings of the National Academy of Sciences. In the standard three-party version of the test, a participant simultaneously communicated with a human and an AI system and then had to determine who was who.
GPT-4.5, which was given the prompt of a young, internet-culture-savvy interlocutor, was recognized as human in 73% of cases. This means that participants chose it over a real person more often than would be expected by random guessing. Llama 3.1 405B with the same setup achieved a result of 56% and, based on aggregate data, proved statistically indistinguishable from humans. Without a special prompt that set a human manner of communication, the results of both models were significantly worse.
The authors called this the first reliable experimental evidence that modern language models can pass the standard three-party version of the Turing test. However, a successful result does not prove that a machine has consciousness, understands its own words, or thinks like a human. It primarily demonstrates the system's ability to convincingly mimic human behavior during a short text conversation. Therefore, today the Turing test is increasingly viewed as a test of human-like communication rather than as definitive proof of the emergence of true machine intelligence.
AI winters and the return of interest
Inflated expectations have repeatedly turned into disappointment. Funding was cut, and the pace of research slowed. Such periods have been dubbed "AI winters".
In the 1980s, expert systems gained popularity. They used large sets of rules compiled by specialists and could assist in diagnostics, finance, or industry. However, maintaining such systems was expensive, and their capabilities remained limited.
The situation began to change due to three factors: the accumulation of digital data, the emergence of more powerful processors, and the improvement of machine learning algorithms.
In 1997, the IBM chess computer Deep Blue defeated world champion Garry Kasparov. In 2012, the neural network AlexNet showed significant progress in image recognition. And in 2016, Google DeepMind's AlphaGo defeated one of the world's strongest Go players — a game that had long been considered too complex for computers.
Another turning point was the emergence of the transformer architecture in 2017. It allowed for more efficient processing of large volumes of text and became the foundation for many modern language models.
At the end of 2022, OpenAI opened wide access to ChatGPT. Generative AI ceased to be predominantly a laboratory technology: it began to be used by schoolchildren, teachers, programmers, journalists, designers, and company employees.
Which companies develop artificial intelligence
Today, dozens of companies have their own research divisions and models. Some focus on universal assistants, while others concentrate on search, programming, robotics, medicine, video, or corporate solutions.
OpenAI
OpenAI is an American company behind ChatGPT. Its main developments belong to the GPT family — large multimodal models for working with text, images, files, and programming code.
The company also develops models and systems for creating images, videos, speech, and programming. Its products include ChatGPT, the video generator Sora, and Codex tools. The current catalog of OpenAI models is regularly updated, so specific versions gradually replace one another.
Google and Google DeepMind
Google has been researching AI for decades, and its division Google DeepMind is known for the AlphaGo, AlphaFold systems, and the family of models Gemini.
Gemini encompasses universal multimodal models of varying power: Pro is aimed at complex tasks, Flash — at fast and cheaper operations, and Flash-Lite — at mass execution of simpler requests. Separately, Google develops Imagen for images, Veo for videos, and specialized audio models. The company integrates AI into Google Search, Android, Gmail, Google Docs, and other services.
Anthropic
Anthropic created the family of large language models Claude. It is divided into several main lines: Haiku is primarily designed for fast and relatively inexpensive operations, Sonnet offers a balance of speed and capabilities, while Opus is aimed at more complex tasks. In new generations, the company is also developing models for long multi-step processes.
Claude can analyze documents and images, write texts and code, work with tools, and perform sequences of actions.
Meta
The company Meta, which owns Facebook, Instagram, and WhatsApp, develops the family of models Llama. Unlike completely closed commercial systems, Llama models are distributed with accessible weights under Meta's license. This allows companies and researchers to run them on their own hardware, further train them, and adapt them to specific tasks.
Meta also uses AI in its social networks: for recommendations, advertising systems, moderation, and the operation of the Meta AI assistant.
Microsoft
Microsoft is not only a major investor and partner of OpenAI but also an independent AI developer. The company creates compact models from the Phi family and integrates artificial intelligence into Windows, Microsoft 365, GitHub, and the Azure cloud platform.
The name Copilot encompasses a range of Microsoft assistants. For example, GitHub Copilot helps with programming, while Microsoft 365 Copilot works with documents, spreadsheets, presentations, and email. Different Copilot products may use different models, including OpenAI's developments.
Amazon
Amazon actively applies AI in e-commerce, logistics, cloud services, and the voice assistant Alexa. Through the Amazon Bedrock platform, the company offers businesses access to models from various developers.
Amazon's own family of models is called Nova. It includes systems for working with text and multimedia data, as well as separate tools for generating images and videos.
xAI
The company xAI was founded by Elon Musk. Its main product is the family of models and chatbot Grok, closely integrated with the X platform. Grok works with text, programming code, and images, and new versions receive tools for searching and performing more complex tasks.
Mistral AI
The French company Mistral AI develops universal and specialized models. Its main families include Mistral Large, Mistral Small, and compact models Ministral. Separately, the company offers Codestral for programming, Pixtral for working with images, and Voxtral for audio.
Some Mistral models are available with open weights, making the company one of the most prominent European AI developers.
Chinese developers
Chinese technology companies also play a significant role in the development of AI. Alibaba creates the Qwen family, Baidu — ERNIE, while DeepSeek has become known for general-purpose language models and models optimized for complex reasoning.
In this segment, models with open weights are developing particularly rapidly. They can be run locally or used as a basis for their own services, although licensing conditions vary in each case.
How AI models differ
The presence of dozens of names does not mean that each model is a completely separate technology. Often, a company creates an entire family: the most powerful version for complex tasks, a faster and cheaper one for everyday operations, and a compact model for smartphones or local devices.
Models can differ in:
- types of data they work with;
- speed and cost of use;
- amount of context they can process;
- quality of programming and logical reasoning;
- ability to run on their own hardware;
- access to the internet and external tools;
- licensing conditions.
One model writes programming code better, another analyzes short queries faster, and a third specializes in images or speech. Therefore, the question "which AI is the best?" does not have a universal answer: it all depends on the specific task.
Where we already encounter AI
Artificial intelligence entered everyday life much earlier than generative chatbots became popular. It sorts spam in emails, recommends music and movies, enhances smartphone photos, corrects errors in texts, and helps banks detect fraud.
In medicine, AI is used for analyzing images and searching for promising molecules. In industry — for quality control and predicting failures. In transportation — for route planning and driver assistance systems. In science, models help process large datasets that a human could not verify manually.
At the same time, AI does not guarantee correct results. A model may replicate biases from training data, misinterpret a query, or confidently provide fabricated information. Caution should be exercised regarding responses related to medicine, finance, law, and security.
Artificial intelligence is neither an all-knowing digital interlocutor nor a regular search engine. It is a powerful tool for working with information, the capabilities of which depend on the model, data, settings, and the task at hand. The technology is evolving extremely quickly, but fact-checking, assessing consequences, and accountability for decisions still remain with humans.
