Rooms

The ten rooms of the Museum of Artificial Intelligence as stable, documented pages.

Greek precursors

The idea of a machine that moves, calculates or “thinks” on its own was not born in twentieth-century laboratories. Ancient Greeks imagined it in myth, examined it in philosophy and, in some cases, built remarkable mechanisms.

The beginnings

Before powerful computers existed, there was a question. Mathematicians and engineers asked whether thought could be described by rules. If so, perhaps a machine could follow them.

The AI winters

Early researchers made bold promises on short timelines. When results lagged behind expectations, funding was cut. These periods became known as “AI winters”. Between them, one approach appeared commercially useful: encoding expert knowledge as rules.

How a machine learns

In machine learning, we do not write every rule. We give the machine examples and let it find patterns. Whatever is present in those examples, useful or distorted, can pass into the model.

Neural networks

An artificial neuron does something simple: it weighs its inputs and produces a decision. The power comes from connecting thousands or billions of such units in layers and adjusting their weights through training.

Language and the first LLMs

At its core, a language model does something simple: it predicts what comes next. The idea is old. What changed is scale: how much text the model learns from and how much context it can use.

How an LLM is made today

A modern language model is not written line by line by programmers. It passes through a sequence of stages, more like a production line, involving data, compute, training, evaluation and deployment. Each stage adds capabilities and introduces trade-offs.