Google today awarded $25 million in grants to a range of organizations to help them apply machine learning to fight some of the world’s biggest challenges.
Recipients range from New York City’ fire department, who wants to find ways to reduce emergency call response time, to an experiment to track air quality with sensors attached to mopeds in Uganda, information that may shape public policy.
The program is also an extension of Google’s AI for Social Good program, which provides flood forecasting to communities in India and is researching how to provide speech recognition for more people with disabilities.
More than 2,600 applications were received since the contest was announced in October from 119 countries around the world, Google.org president Jacquelline Fuller told VentureBeat in a phone interview.
The news was announced today onstage at the Google I/O developer conference by CEO Sundar Pichai and AI head Jeff Dean.
“We want to empower everyone to use AI to apply to problems they see in their communities,” Dean said onstage.
Each recipient organization will receive roughly $500,000 to $2 million.
As part of the challenge, Google AI staff from its labs around the world as well as Googlers who focus on matters of privacy and security will be assigned to consult and advise the award winners. 40 percent of winners from Google.org is also part of the initiative have no prior experience with machine learning.
Winners from 12 nations around the world include:
– The Trevor Project in the United States speaks with LGBTQ youth in crisis and will apply natural language processing and sentiment analysis in an attempt to understand the suicide risk level of people who call or text.
– Wadhwani AI from India will use AI in an attempt to reduce pesticide use, a major source of food waste.
– New York University and the Fire Department of New York (FDNY) will team up to reduce response time for the 1.7 million calls.
– American University of Beirut in Lebanon will use machine learning in an attempt to help farmers save water used for crop irrigation and food production.
– Colegio Mayor de Nuestra Señora del Rosario in Colombia will use computer vision and satellite imagery to detect illegal mining operations known to contaminate local drinking water.
– Crisis Text Line, a service that uses natural language understanding to predict when a person is sad or depressed, will use the Global AI Impact Challenge funding to reduce wait times and more quickly serve users.
– La Foundation Medecins Sans Frontieres in France wants to create a smartphone app that non-technical staff can use to analyze anti-microbial images in an effort to reduce the cost of treatment of some ailments and use of antibiotics.
– Human Rights Information and Documentation Systems (HURIDOCS) in Switzerland will explore ways machine learning can make connections between laws, victim testimony, and other documents.
– Rainforest Connection in the United States will use sensors and audio analysis to detect the sounds of illegal logging.
– Penn State University in the United States will use deep learning to attempt to better predict landslides.
The inaugural Global AI Impact Challenge follows a model that’s been carried out in more than a dozen similar projects designed to deliver maximum impact, Fuller said.
Applicants were chosen in part based not just on the potential impact of their proposal but also perceived feasibility, alignment with Google AI principles, and potential to scale.
“What we’ve seen with our impact challenge model is that you will typically have a set of them who their hypotheses just doesn’t pan out or it’s not achieving what they thought it was going to achieve. You’ll have a group that’s doing really well, but it seems like the solution is going to be more appropriate for just them, but then you’ll have several who really hit it out of the park. And we are absolutely open and willing and looking for doubling down on those and helping to make sure that if there’s scalability potential there that we are helping them to do that.”
Google will also seek ways to spread the code of promising projects through open source sharing of code, Fuller said.