Inspiration: Currently with around 700 thousand infected people worldwide, more than half of the jobs in the developed countries and even more in the less developed ones may be at stake. So millions of people will lose their job and without finding an alternative their future will be unpredictable. Our team, coming from different countries: Germany, Hungary, India, Mexico, and Switzerland, wanted to tackle this urgent issue and decided to provide some feasible job alternatives for people who lost their work. So we created Morphosis.
What it does: Morphosis is a web or mobile-based free app where users can indicate their previous job and the app will offer them several feasible job alternatives that they can pursue during and after social distancing.
How we built it: The app relies on a user-based recommended system and machine learning to generate the most viable alternative based on previous learnings from the current and other users.
Challenges we ran into: We had two key challenges: 1. Understand which technical solution (a type of machine learning) can be used and implemented to recommend the most feasible alternative job options. 2. Collect hundreds of current jobs and potential future jobs and order them into a structured database that the application can use.
Accomplishments that we're proud of is that even though it was a diverse team with different backgrounds and timezones we were able to keep our focus on the common goal, to develop an easy to use simple app which can deliver a social benefit to broader society, independently from location and nationality.
What's next for Morphosis: The machine learning system behind Morphosis will be more accurate if more users are experimenting with it. Therefore the key is that we generate users for the platform. To deliver a bigger impact with the app, we want to create a platform that connects the supply and demand, so directly linking it with available temporary job opportunities and include a reference and feedback system to make the app more reliable.
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absed, flutter, learning, machine, material, material-design, recommender, system, user