Hej, I’m Cynthia.

Finalist in the challenge - Health promotion The idea is to strategically place the beacons around camp and use them to send push notifications about health events and tips to soldiers' mobiles, as well as use the soldiers' collective Fitbit activity statistics to inform decisions and potentially provide customised training based on individual fitness.

Team “Autimood” built a really cool application using the Narrative api and the Microsoft emotion detection project to help autistic kids understand human emotions in a better and fun way. They use the images from the narrative database and analyse faces for emotions. At the end of the day autistic kids can sit down with their parents and play a fun game of identifying the emotions of the people they meet during the day.

We designed a kitchen bot that helps make cooking an easier task. With the kitchen bot, we want to reduce food wastage and help people to cook better food with available ingredients in their fridge.

The world is in an ever changing state and it is often hard to grasp the severity of its changes. By utilizing data driven methods, we have managed to associate sounds to the environmental changes of the earth. The multimodal experience creates for an awareness about where the future of the world is heading. We set out to use deep learning to make computers make songs out of certain geolocational areas. But what we found, after iterating the process at several time steps, was that it was possible to get a feel of how the earth is affected by everything that is happening in our society. Since we wanted to have a pipeline with as little manipulation by humans as possible, we set up a rule set and let the computers, based on data, create the sonifications related to each place. The tools we have managed to produce are possible to use for a wide range of changes in the environment, everything from natural disasters to malicious attacks can be tracked and sonified. To accomplish our goal, we used deep learning, TensorFlow, spectral analysis, multivariate statistics as well as other data driven methods to classify areas and extract, through image recognition, features from satellite imagery (provided by the SNSA) that the AI could use to create the sonifications. To turn the extracted data into sounds, we set up a framework for the AI to use in the musical coding language called "ChucK" (http://chuck.cs.princeton.edu/).