Current Themes

To focus our inspiration and to try something a little different, we are running this particular round of Catalyst on specific themes, as outlined below. We are only funding proposals in these areas.

Current Themes

URBAN TRANSPORTATION

Today, 55% of the world's population lives in urban areas, a proportion that is expected to increase to 68% by 2050. With more and more people living closer and closer together, there is a need to find new ways to transport people and objects point A to point B efficiently in urban areas.

We'd love to hear your thoughts on how to improve urban transportation. Here are a few prompts for ideas:

  • What are solutions to alleviate long commutes - either by reducing their distance or by reducing their time?
  • What are ways to provide people with the benefits of commuting without actually commuting?
  • What are new solutions to improve middle-mile logistics? (e.g., the part of the supply chain that customers do not see, typically including the movement of goods between a port and the time at which they are dispatched to the final customer)
  • How do we solve last mile logistics challenges especially in urban, rural and exurban settings?
  • What are new propulsion mechanisms for vehicles in urban environments?
  • What are novel packaging solutions that could be developed to optimize urban transport?
  • Overall, what are new ways to better use urban space and answer urban transportation needs?
We believe that machine learning offers a key technology to bring these ideas to life. Please describe how you intend to incorporate this technology in your solution if appropriate.

COMPUTATIONAL SOCIAL SCIENCE

We're living in a society facing serious economic, social, and political divisions and are interested in big ideas drawing scholarly and entrepreneurial attention to social divides, polarization, and social conflict. We're seeking proposals combining methods from a diverse range of disciplines, including the humanities, statistics, social sciences, economics, sociology, anthropology, and history. We are also interested in fields which combine computational methods, like data science, with existing academic disciplines. Some examples include computational social science (CSS), digital history, and digital humanities.

Here are a few prompts for ideas:

  • What are some new means of encouraging democratic participation, or bridging gaps between people and government?
  • What are some innovative approaches to bridging the digital and physical worlds?
  • How do we preserve local culture, institutions, and businesses as cities develop into tech centers and global hubs?
  • How can we encourage more human face to face contact in an increasingly digital world?
  • What solutions can be developed to help people distinguish between facts and distorted or "fake" news?
  • How can we facilitate meaningful dialogue across socio-economic, cultural, religious, political, or geographic boundaries?
  • What are new solutions to address disparities in access to technology (the "digital divide")?
  • What novel algorithms, statistical models, or applied computational methods can help advance the social good, or the state of machine learning in general?
  • How might we address declines in dating, romantic relationships, and face to face social interaction throughout the developed world?
  • What novel methods can help us obtain deep insights about human behavior at scale?
  • How do we encourage civil discourse on the internet and in social media?
  • Overall, how can technology and social science combine to create a more integrated, just, and equitable society?
We believe that machine learning offers a key technology to bring these ideas to life. Please describe how you intend to incorporate this technology in your solution if appropriate.