Here are the grantees of the Google AI Impact Challenge
As part of Google’s AI for Social Good program, we launched the Google AI Impact Challenge, based on our strong belief that emerging technologies will help us address big social, humanitarian and environmental problems. We were blown away by the number of thoughtful proposals we received: 2,602 applications from 119 countries, nearly two thirds of the world’s countries.
Forty percent of the applications came from organizations with no previous experience with artificial intelligence, which is still a developing concept in the social impact field. Our job, as we thoroughly vetted the applications, was to choose the best projects based on feasibility, potential for impact, scalability and the responsible use of AI.
Today, at I/O, we are announcing 20 organizations that will share $25 million in grants from Google.org, credit and consulting from Google Cloud, mentoring from Google AI experts and the opportunity to join a customized accelerator program from Google Developers Launchpad. The selected projects address issues in the areas of health, economic opportunity and empowerment, environmental protection and conservation, education, misinformation and crisis and emergency response. Here’s the full list of grantees.
American University of Beirut (Lebanon): Applying machine learning to weather and agricultural data to improve irrigation for resource-strapped farmers in Africa and the Middle East.
Colegio Mayor de Nuestra Señora del Rosario (Colombia): Using satellite imagery to detect illegal mines, enabling communities and the government to protect people and natural resources.
Crisis Text Line, Inc. (USA): Using natural language processing to optimize assignment of texters in crisis to counselors, reducing wait times and maintaining effective communication.
Eastern Health (Australia): Analyzing clinical records from ambulances to uncover trends and potential points of intervention to inform policy and public health responses around suicide.
Fondation MSF (France): Detecting patterns in antimicrobial imagery to help medical staff in low-resource areas prescribe the right antibiotics for bacterial infections.
Full Fact (UK): Developing trend monitoring and clustering tools to aid fact checkers’ analysis, so they can help contextualize the news and enable informed decisions.
Gringgo Indonesia Foundation (Indonesia): Building an image recognition tool to improve plastic recycling rates, reduce ocean plastic pollution and strengthen waste management in under-resourced communities.
Hand Talk (Brazil) Using AI to translate Portuguese into Brazilian Sign Language through a digital avatar, enabling digital communication for Brazilians who are deaf and and hard-of-hearing.
HURIDOCS (Switzerland): Using natural language processing and ML to extract and connect relevant information in case-related documents, allowing human rights lawyers to effectively research and defend their cases.
Makerere University (Uganda): Tracking and predicting air pollution patterns via low-cost sensors in Kampala, Uganda, improving air quality forecasting and intervention.
New York University (USA): Partnering with the New York City Fire Department’s analytics team to optimize response to its yearly 1.7 million emergencies, accounting for factors like weather, traffic and location.
Nexleaf Analytics (USA): Building data models to predict vaccine viability throughout the cold vaccine supply chain and ensure effective delivery.
The Pennsylvania State University (USA): Using deep learning tools to better predict locations and times at risk for landslides, creating a warning system to minimize the impact of natural disasters.
Quill.org (USA): Using deep learning to provide low-income students with immediate feedback on their writing, enabling students to revise their work and quickly improve their skills.
Rainforest Connection (USA): Using deep learning for bioacoustic monitoring and commonplace mobile technology to track rainforest health and detect threats.
Skilllab BV (Netherlands): Helping refugees translate their skills to the European labor market and recommend relevant career pathways to explore.
TalkingPoints (USA): Using AI to enable two-way translated parent/teacher engagement and coaching when language represents a barrier to communication.
The Trevor Project (USA): Using natural language processing and sentiment analysis to determine a LGBTQ youth’s suicide risk level to better tailor services for individuals seeking help.
Wadhwani AI (India): Using image recognition to track and analyze pest control efforts, enabling timely and localized intervention to stabilize crop production and reduce pesticide usage.
WattTime Corporation (USA): Using image processing algorithms and satellite networks to replace on-site power plant emissions monitors with open-source monitoring platforms.