It’s fun to design educational material with creative constraints.
Therefore when trying to create learning materials, I often pose myself a create constraint by “forcing” myself to *NOT* do the most obvious thing.
So when asking myself how can I explain – to people with ZERO programming background – how a neural network works? I decided to give myself the constraints that it must be done entirely intuitively and without explaining it to them. Further, it should use no or very limited digital aids (so no showing how one actually works and messing around with training data and paramters!).
I’ve made a human-like board game to explain statements and sequences using only images (the MyBot game – available here) based off the RoboRally board game which basically does exactly that, I deciced I wanted to try and develop a game around the central concepts of input layer, output layer, neurons and weights. Perhaps heavily inspired by one of my favorite board games SET, I decided to create a card game for this.
The game has to be played by 5 people. One person is the “neural network” and has to classify (output layer) an object (in this case, a fruit) using only information from their neurons. Each neuron (the other 4 players) get a card which visually encodes part of the input layer (the fruit). The neurons (the four card-receiving players) then explain their “input unit” to the player pretending to be a neural network.
Below is an example of a set of four cards used to exemplify a fruit.
As you might have already noticed, the cards together show the “features or attributes of a pineapple” (color, shape, stem, seeds) in the same way that the SET game has different attributes. However, each neuron (each player) isn’t aware of what they are looking at. The guessing player might proceed to classify the entire input layer (all 4 cards) as a pineapple putting “weight” on the stem.
For the banana SET, I can tell you the neuron with the shape is the one that will be weighted.
Players can take turn being the “guesser” and the neurons. After playing the game, you can ask the players how they decided what to classify each object as? When did they become certain (enough)? Did they classify all of them correctly?
After playing the game, I would give a presentation of how the concepts that the players now (hopefully) intuitively understand relate to the mathematical model behind a lot of generative artificial intelligence (CNNs).
After the game, you can also have students play around with Googles Teachable Machine to see how it fairs in classifying candy (licorice vs winegums) and talk to them about what happens if the training data doesn’t contain any licorice eggs.
To play the game, you need my deck of cards which you can print from the file listed below. Each set of four cards need to be marked on the back to make up at complete “input layer” of cards (A, B, C, D and E).