Research

Mental illnesses have reached an epidemic level over the last few years, with 1 in 4 people suspected to have a diagnosable mental illness. Mental illnesses are also the biggest contributors to the loss of life satisfaction and subjective well-being, even more so than physical illnesses. The issue is more significant in high-income countries without social welfare, where the costs of treatment marginalize a significant portion of those affected. On the other hand, the issue is also magnified in low and middle-income countries, where a significant percentage (often up to 90 %) of the affected
people do get the necessary help. To overcome this, we propose to set up a platform, which connects interested users with mental health professionals in near real-time. We can try a host of AI and software to scale the project including, but not limited to: use of chatbots or use of smartphone and wearables data to monitor user mental and emotional states, and predict relapses. The project aims to empower people to seek out mental health services at their fingertips. Additionally, the goal is to democratise access to mental healthcare, irrespective of people’s financial standings.

This project aims to develop a better understanding of online identities through multiple approaches: Confrontation, Explanation and Solution. The concrete application aims to provide a web-service that can generate a detailed, yet brief report about the most unique facts retrieved from the internet. This confrontation includes all of the basic information (gender, language, job, location) and more personalized information (friends, shopping preferences, language use). As there are already services in place to provide this information from separate social media sites (Facebook, Twitter etc), the implementation focuses on the connection of this information and on its visualization. Furthermore, the retrieved data should be explained to the user (how did the algorithm find the data) and finally offer ways to hide or delete the data.