# How AI Works

*Eleven concepts, from neurons to Da Vinci · 8 October 2026 · Zachariah Roydel Robertson*

Eleven concepts that power modern language models, in the order you need them: from a single artificial neuron to how Da Vinci learns. Each builds on the one before.

## 01. Neurons and weights

A neuron takes in numbers, multiplies each by a weight (a strength), adds them up and sends out a number. Weights are what gets adjusted during training. They are the model's memory.

## 02. Backpropagation

After the model makes a guess, it measures how wrong it was. Then it traces backward through every neuron, asking how much each contributed to that error. Each weight is nudged a tiny amount to be less wrong.

## 03. Tokens

Words and parts of words are broken into small pieces called tokens. Each token is mapped to a number, like a postcode for a chunk of language. The model works with these numbers, not letters.

## 04. Next-word prediction

The model takes everything said so far and predicts which token comes next, with a probability for each possibility. It picks one, and that new token becomes context for predicting the next.

## 05. Transformers and attention

The Transformer is the engine inside modern AI. Its key idea is attention: each word looks at every other word in the context and weighs how important it is for understanding. This is how the model tracks relationships across long passages.

## 06. Pretraining

Before a model ever talks to anyone, it is trained on huge amounts of text: books, websites, articles. It learns the patterns of language by guessing the next word billions of times, and absorbs knowledge of the world along the way.

## 07. Fine-tuning

After pretraining, the model is adapted to a specific task using smaller, curated datasets. A customer-support model sees examples of good support conversations. Fine-tuning adjusts the weights slightly to specialise the general knowledge.

## 08. Reinforcement learning

After the model generates a response, a person or an automated system scores it. The model learns to maximise that score by adjusting its weights toward what worked and away from what did not.

## 09. Test-time thinking

A model can spend extra computation working through a hard problem step by step, instead of answering immediately. More thinking gives more accurate answers on complex questions such as mathematics and code.

## 10. Open and closed weights

With open weights, a model's parameters are public: anyone can run and modify it. With closed weights, the model is kept private and reached through an API. Open is free and customisable; closed is usually more refined and supported.

## 11. Da Vinci: causal learning

Da Vinci, the assistant inside e1-4, combines three ideas: stochastic training (learning from randomness), causal reasoning (understanding what causes what) and self-improvement (learning from its own mistakes). It is built on open-weight foundations, trained on its own compute, and improves by thinking.