What Is SI?
Author: Domeny.ForSale editorial team Published
In brief: SI is the Polish abbreviation for “sztuczna inteligencja”, meaning “artificial intelligence” — a term for computer systems that perform tasks such as recognising images, processing language or making predictions based on data. Its English equivalent, AI, means the same thing. AI does not necessarily think or understand the world as a human does, and its results can be wrong. Outside the technology context, SI also stands for the International System of Units, which includes the metre, kilogram and second, among others.
How does artificial intelligence differ from an ordinary program?
A traditional program follows instructions written by a programmer, for example applying a discount when a shopping basket exceeds a specified value. An AI system, by contrast, can analyse features of data and use them to recognise patterns, classify information or suggest an answer. The distinction is not always clear-cut: a single application often combines ordinary rules with artificial intelligence models.
Imagine an email filter. A simple rule sends every message containing a specified word to spam. An AI-based filter can consider many features at once, such as the content, the structure of the message and its similarity to previous spam. This allows it to detect an unwanted message without that particular word, but it may also mistakenly block an important email.
Not all automation is artificial intelligence. Sending a reminder about an invoice due date does not require AI. Extracting data from a photograph of that invoice may use it.
How does AI work, and what does training a model mean?
Many modern AI systems use machine learning, a set of methods that allow a system’s behaviour to be adjusted based on data rather than by manually specifying every rule. The model produced by this process is a mathematical mechanism that transforms input data into an output, for example turning a photo into the name of the object shown.
During training, the model analyses examples, and its parameters are adjusted to improve results according to a chosen criterion. It then uses the relationships it has learned to work with new data. The quality of its answers depends on factors including the training data, the model’s design and whether the task resembles the situations it was prepared for.
Deep learning is a branch of machine learning that uses multilayer neural networks. Despite its name, a neural network is not a digital human brain. It is a computational structure that processes information through successive layers.
Simply using a tool does not mean that the model learns continuously from every conversation. Whether the provider stores the data you enter and uses it for further training depends on the service, settings and account terms. You should check these rules in the provider’s official documentation and privacy policy.
What is AI used for besides talking to a chatbot?
A chatbot is just one way to use artificial intelligence. AI can work in the background of an application without having any conversation with the user.
| Application | What the system does | What to watch out for |
|---|---|---|
| Speech recognition | Converts a recording into text | May misinterpret surnames, numbers and specialist terms |
| Recommendations | Selects products or content based on predicted preferences | Suggestions are not necessarily the best choice for the recipient |
| Anomaly detection | Flags transactions or behaviour that deviate from a pattern | An unusual event does not always indicate fraud |
| Document analysis | Classifies content and extracts information | May miss a caveat or misread a table |
Generative AI creates new content, such as text, images, audio or code. A large language model, often abbreviated to LLM, generates text by predicting successive portions of it based on context. It can help with drafting messages or explaining concepts, but the generation mechanism itself does not guarantee that an answer is true.
Why can AI be convincingly wrong?
Fluent language is not evidence of sound reasoning or access to a reliable source. A model may generate a nonexistent quotation, an incorrect date or a description of a feature that a particular service does not have. These seemingly credible but factually inaccurate outputs are often called hallucinations.
A system may also reproduce biases present in its data or perform less well on examples that were poorly represented during training. Good performance on one task does not imply similar performance on every other task. A tool that skilfully improves writing style may not interpret a contract correctly.
Not every chatbot has internet access, and access to a search engine does not eliminate errors. Current prices, regulations and service specifications need to be confirmed using primary sources. For medical, legal or financial decisions, an AI answer can serve as supporting material, but it does not replace an assessment by an appropriate professional who takes the individual’s circumstances into account.
How can you start using AI sensibly?
For your first attempt, choose a task whose result you can assess yourself. Instead of asking for a complete business strategy, ask it to shorten your own product description without changing the facts.
- Define the goal and constraints. For example: “Shorten the description to 100 words. Do not add product features that are not in the text.”
- Remove confidential data. Do not paste passwords, customer data or company documents without checking the rules for using the tool.
- Compare the result with the original. Pay particular attention to numbers, names, promises and omitted caveats.
- Approve the result before using it. Do not publish or send it automatically just because it sounds professional.
Start with one short text and check whether the time saved on editing outweighs the time needed to review the answer.
See also: What happens after a domain expires?
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