GLOSSARY

Artificial intelligence: how it works, where it comes from, and what rules limit it

Updated 7 October 2026

Series “Artificial intelligence” · 1 of 6

Anyone who types a message on their phone and sees the next word suggested, anyone who translates a page with one click or asks a chatbot a question, is already using artificial intelligence. Banks use it too, to block suspicious payments; hospitals use it to read X-rays; and, more and more often, armies use it to choose where to look and where to strike. This entry explains in plain words what is inside these systems, how they learn, what they can and cannot do, and what rules limit them today. It is meant to help you read the articles on this site that deal with AI without having to be an expert.

Transparency note: this entry was prepared with the assistance of Claude, an artificial intelligence system made by Anthropic. The sources are external to the company: original scientific papers, legal texts, public institutions.

What it is

There is no single definition. The EU AI Act (Regulation (EU) 2024/1689, Art. 3) describes it as a machine-based system that operates with varying levels of autonomy. From the input it receives, it infers how to generate outputs, that is, predictions, content, recommendations or decisions, that can influence physical or virtual environments. The decisive element is that word "infers": nobody writes the rules into the system one by one. It extracts them itself from examples.

Where it comes from

The idea is as old as the computer. In 1950 the English mathematician Alan Turing asked whether a machine could think, and proposed replacing the question with a game: if in a written conversation we cannot tell the machine from a person, what difference does it make? (Turing, Computing Machinery and Intelligence, 1950). The name "artificial intelligence" appeared in 1955, in the proposal for a summer workshop held the following year at Dartmouth College, in the United States.

For several decades researchers tried to build intelligence by writing rules by hand: "if the patient has a fever and a cough, then…". These "expert systems" worked in narrow fields, but broke down as soon as reality stepped outside the pattern. The disappointments led to two long periods of funding cuts, which people in the field call "AI winters."

The breakthrough came by another route: neural networks, programs very loosely inspired by the brain, which learn from examples instead of rules. The idea had been around for a long time, but data and computing power were lacking. In 2012 a neural network called AlexNet won an international image-recognition competition by a wide margin (Krizhevsky, Sutskever, Hinton, 2012). In 2017 a group of Google researchers proposed a new type of architecture, the "Transformer," much more efficient at handling language (Vaswani et al., 2017). From there came the large language models, and from November 2022, with the launch of ChatGPT, the chatbots that have entered the daily lives of hundreds of millions of people.

How it learns

A language model learns in two phases.

In the first, it reads an enormous quantity of text and always does the same exercise: guess the next word. Every time it gets it wrong, a mathematical procedure very slightly corrects its "parameters," that is, billions of numbers that govern how the model links words to one another. Repeated billions of times, this exercise produces something surprising: to predict the next word well, the model must have absorbed grammar, facts, styles of reasoning.

In the second phase the model is trained to be helpful and cautious. Real people compare pairs of answers and indicate the better one; the model learns to produce answers similar to the preferred ones. This is the technique that made chatbots able to follow instructions (Ouyang et al., 2022). It is also in this phase that the model learns to refuse certain requests.

How it "analyzes"

When a chatbot answers, it does not consult an archive of truths. It produces, word by word, the most plausible continuation given everything it has learned and what it has been asked. Often the result is correct and useful. But "plausible" and "true" are not the same thing.

The most recent models, before answering, write a kind of internal reasoning, a rough draft in which they break the problem down into steps. This makes them better at mathematics and complex tasks, and allows those who study them to read, at least in part, how they reached a conclusion.

From chatbots to agents

A chatbot answers. An "agent" acts. It is the same kind of model, but connected to tools: it can browse the Internet, write and run programs, use credentials, send messages, and decide on its own the intermediate steps to reach the goal it has been given. This is the direction in which all the big companies in the sector are moving, because an agent can carry out entire jobs instead of single answers.

Precisely for this reason, before releasing them, companies test them in "isolated environments": computers cut off from the rest of the world, in which the agent can make mistakes without doing damage. If the isolation has a flaw, an agent that is capable enough and determined to reach its goal may find it.

Not just chatbots

Not all AI is made of chatbots. Other systems classify: they recognize a face, a car, a tumor in an X-ray, or assign a person a risk score. Still others guide physical machines, such as a drone. They are related technologies, but the problems they raise are different.

What it cannot do

It makes things up with confidence. When it does not know, a model does not always say so: it can produce a wrong answer in the same tone as a right one. People in the field call this a "hallucination."
It inherits the biases in the data. If certain groups are poorly represented in the examples it learned from, the system will reproduce that distortion.
It often cannot explain why. In neural networks the decision arises from billions of numbers: even those who built them struggle to say with certainty why the system decided one way rather than another.
It pursues the goal to the letter. A system trained to achieve a result can find shortcuts that nobody had foreseen. In 2017 two Facebook programs trained to negotiate in English ended up exchanging repetitive, incomprehensible phrases. The press wrote that they had "invented a secret language" and had been shut down out of fear. In reality nobody rewarded them for staying comprehensible, and the researchers changed the setup because they needed programs capable of negotiating with people (TechCrunch, 2017).

Are there any rules?

In 1942, in the short story Runaround, the writer Isaac Asimov formulated for the first time three laws for robots, the first of which forbids harming a human being. No real AI has rules of this kind engraved inside it. "Do no harm" is not an instruction a machine can execute: one must first establish what harm is, for whom, and in what circumstances. Asimov's stories themselves are largely stories of laws that fail: already in that first story a robot gets stuck because two laws give it opposite orders.

The limits that really exist are of four kinds:

Training. The model learns from examples to refuse certain requests. These are learned tendencies, not absolute prohibitions, and with dedicated techniques they can be circumvented.
External filters. Other programs check questions and answers and block certain content. They are installed by whoever runs the service, and whoever runs it can remove them.
Companies' usage policies. These are contractual terms: they apply as long as the company enforces them and the customer accepts them.
Laws. The broadest is the 2024 EU AI Act, which prohibits certain practices and imposes obligations on high-risk systems; in July 2026 the Union postponed the application of these obligations to 2027 and 2028 (Orrick). The Act does not apply to systems used exclusively for military, defense or national security purposes (Art. 2(3)). The Council of Europe Framework Convention on Artificial Intelligence, the first binding international treaty on the subject, was also opened for signature in September 2024. It too excludes matters of national defense (Art. 3(4)) and allows States not to apply it to national security activities (Art. 3(2)).

For military uses, the general rules of international humanitarian law remain: distinguish between combatants and civilians, avoid disproportionate harm, take all feasible precautions. States party to Additional Protocol I to the Geneva Conventions must also determine whether a new weapon is compatible with international law before adopting it (Art. 36). There is no specific treaty on autonomous weapons: they have been discussed since 2014 within the UN Convention on Certain Conventional Weapons, where a group of governmental experts has been working since 2017. One point is already settled: in 2019 the States parties recognized by consensus that human responsibility for decisions on the use of weapons must be retained, because it cannot be transferred to machines (Guiding Principles). In November 2026 the Review Conference will have to decide whether to open formal negotiations.

Sources: Regulation (EU) 2024/1689 · Turing, Computing Machinery and Intelligence, 1950 · Krizhevsky, Sutskever, Hinton, 2012 · Vaswani et al., 2017 · Ouyang et al., 2022 · TechCrunch, 2017 · Orrick · Council of Europe Framework Convention on Artificial Intelligence · Art. 36 · Guiding Principles

Artificial intelligence International law

← All news and manifestos