Svenska

Interactive simulations and hands-on labs for learning about Artificial Intelligence.

AI search and problem solving

Search algorithms in mazes

Explore BFS, DFS, Greedy best-first and A* and see how they find their way through a maze step by step. Ch. 2 · Fundamental search algorithms(pp. 51–65)

Search algorithms in trees

Visualize fundamental search algorithms in a search tree and follow the search step by step. Ch. 2 · Fundamental search algorithms(pp. 51–65)

Five in a row in a partially observable environment

Play five in a row without seeing all the squares to experience partial observability. Ch. 2 · Environments(pp. 43–45)

Minimax with alpha‑beta pruning

Try the Minimax algorithm on a search tree. Follow the updates to the alpha-beta values during the search and see how alpha-beta pruning makes the search more efficient. Ch. 2 · Decision algorithms for games(pp. 66–74)

Machine learning

The Naïve Bayes lab

Train a Naive Bayes classifier to tell spam from ordinary email and see the mathematics behind the classification. Ch. 3 · Naive Bayes classifier(pp. 107–110)

The neural networks lab

Build, train and explore a neural network step by step. Follow the computations in each node, then let the network make predictions based on the input. Ch. 3 · Neural networks(pp. 112–117)

Visualizing decision boundaries

Place data points and see the difference between how linear models and neural networks draw boundaries between classes, and discover why neural networks are often needed. Ch. 3 · Neural networks(pp. 112–117)

Q‑learning simulation

Train an agent to navigate a grid using reinforcement learning. Follow how the agent learns the best path by trying different actions and updating its Q-values. Ch. 3 · Reinforcement learning(pp. 124–130)

NIM with reinforcement learning

Watch an AI system learn to play the game of NIM from scratch – train it over different numbers of games, see how the AI's strategy improves using a Q-table, and then try playing against it. Ch. 3 · Reinforcement learning(pp. 124–130)

The language model lab

Build your own small language model from any text and see how it picks the next word – word by word. Ch. 4 · How a language model works(pp. 141–142)

AI and society

The bias lab

Train a recruitment AI on skewed historical data – discover how discrimination arises and how hard it is to get rid of. Ch. 1 · Bias(pp. 31–32)

The AI timeline – When was that?

Test your knowledge of AI history! Place events on a timeline and score points. Choose to practice solo or challenge a friend in two-player mode. Ch. 1 · The history of AI(pp. 14–19)

Implement AI algorithms in code

Here you can try out how some AI algorithms work by running ready-made code blocks in Google Colab. You don't need to know how to program – it's enough to follow the instructions, make small changes and see how the result is affected. Note: these notebooks are in Swedish.

BFS and DFS in trees

Google Colab Swedish only Ch. 2 · Fundamental search algorithms(pp. 51–65)

Linear regression with gradient descent

Google Colab Swedish only Ch. 3 · Linear regression and gradient descent(pp. 96–101)

k‑Nearest Neighbors

Google Colab Swedish only Ch. 3 · k‑nearest neighbors (kNN)(pp. 103–105)

k‑Means clustering

Google Colab Swedish only Ch. 3 · The k‑means algorithm(pp. 120–122)
Book cover: AI – Teknik, samhälle och etik
The textbook

AI – Teknik, samhälle och etik

David Hedengren · Liber

All the simulations on this site were created as a digital companion to the textbook AI – Teknik, samhälle och etik (a Swedish AI textbook).

Explore the book at Liber