On Friday, September 27th, several universities and research centers across Europe hosted outreach activities for the European Researchers’ Night. In Rome, nearby the buildings of the Math and Geology department of the University of Roma Tre, one of the events that welcomed visitors was organized by Pangea Formazione: “It’s raining cats and dogs“.
To visitors of the stand, mainly targeted at kids, it was given the chance to get in touch with some of the core ideas that advanced machine learning solutions are based upon, through a pair of board games.
One of the activities dealt with the basics of convolutional neural networks and image classification via deep learning. Kids were divided in teams and assigned one (sketchy) drawings each, with the goal to help the other team to guess their image through a series of subsequent elements. At each round, the host was presenting a new ‘feature’ (a particular curve line, a corner, or some other shape) that members of each team had to search inside their images. If such a feature was present, they shall draw it on a thin sheet of paper. Through addition of multiple features, a more and more complete picture was composed and it was easier for the opponent team to guess the subject of the drawings, but scoring progressively fewer points.
This procedure mimics quite closely the inner functionality of a trained CNN classifier that first learns a series of abstract patterns (through the different filters that get trained in the sequence of layers of the neural network), which in our game were represented by the lines and patterns proposed by the host of the game, and then searches for them in any new picture that is fed for classification.
The second activity consisted of a card game about updating probability estimates, based on different levels of information. During subsequent rounds of a game, one or two players want to guess the current presence of a specific weather conditions (rainy, cloudy, sunny, windy, etc.), while being unable to directly obtain this information e.g. because they cannot just look outside the window or use a weather forecast app.
Hence, they can decide to guess blindly about it (having a certain low probability to guess correctly) or to play additional information cards in order to gain further evidences in support to their guess. Examples of information cards are: the current season, which can increase chance to guess right the weather for some conditions and decrease it for other conditions, the city in which the player’s character currently is (e.g. Palermo, Rome, Milan, etc.), or the fact that the player’s character is a person who has spent part of her life in a chosen city, which increases the chance of a correct guess if the same city card has been played as well.
Players therefore take turns either by trying to guess their answer, or by adding an information card to their advantage (if such cards turn odds in their favor) or by putting obstacles on the opponent’s guess (if the cards give negative points for the weather condition that the other player is trying to guess).
This mechanism about accounting for every available information before evaluating the probability of the event shall remind you about the description of the subjective probability given in a previous blog post. Along the same lines, the game helped to convey the idea that in real situations we must be flexible enough to update our belief in presence of new evidences.
Several components of the Pangea Formazione work team participated to the event, helping visitors to grasp the rules of the games and illustrating the underlying principles that really made the games close to the actual machine learning algorithms we often see in action in everyday life. When we see a mobile phone capable to recognize our faces as a security mechanism, or when translation apps can identify and translate texts that the camera focus on, we seldom have the knowledge needed to understand how such complex tasks are accomplished. Even if a casual observer could believe some magic is involved, in fact it is just the (complex) combination of simpler elements, whose understanding luckily does not need particular studies.
Kids (and their parents and grandparents as well, in fact) were very curious and wanted to have a glimpse of the actual ideas that lie behind common applications of machine learning.
At the same time, the ludic aspects of the games were really appreciated by the kids who stopped by our stand, spanning ages from 6 to 12 years, and they really wanted to remain as long as possible with us.
For adults, a series of posters summarized some of the different technical aspects that are involved both in CNN algorithms for image classification and in defining a flexible definition of probability, like the subjective one, that can go beyond the simple examples with coins and dice we learn at school.
The only downside of an otherwise great evening was the fact that ‘our’ ESR Daria could not attend the events, because she is currently spending her time at University of Edinburgh for her secondment period. But we will welcome her in the outreach group next year for sure!
Insights ITN 