Funding call: UNICEF set to invest up to Shs370m in startups
The United Nations International Children’s Emergency Fund (unicef) through its Innovation Fund is ready to invest up to US$100,000 (about Shs370 million) in startups that are creating solutions that can address the problems humanity is facing, through data science, machine learning, and Artificial Intelligence.
It is a zero-equity investment, according to unicef.
Applications for the Unicef Innovation Fund close on 28 February.
According to the UN agency, it is only applicants from Unicef’s programme countries that will be accepted for the program.
Secondly, for a startup to qualify, it must already have a functional, open source prototype — or one it is willing to make open source.
Since the launch of the Unicef Innovation Fund, 72 investments have been made in 42 countries and, in 2019, it wants to invest in 20 startups.
Other requirements needed, include having a solution that applies deep learning to analyze satellite imagery and automatically map infrastructures such as schools, health centers, roads or cell towers.
The project should be able to use digital footprints from sources like social media or mobility patterns to understand public spaces, it should be able to explore machine learning or AI techniques to help better understand the physical world and its rapidly changing environments.
Being able to use natural language processing or similar techniques to analyze large amounts of text and using machine learning and AI to understand content structures and knowledge, as well as learning relationships, are added advantages.
Other things your solution should be able to do, include:
Explore machine learning or AI techniques to help better understand the digital world and its rapidly dynamics as well as changing trends.
Use machine learning or AI to understand the relationships between different variables that impact development indicators.
Apply optimisation techniques to improve service delivery, resource allocation or content delivery.
Use predictive analysis to understand changes in the world such as job market trends or the supply and demand of skills.
Gather and combine existing data from different sources.
Generate new data through field data collection, crowdsourcing or social network platforms.
Use novel approaches to generate, as well as to validate large amounts of training data.