AI & Environment Resource HubGoogle · Publication date unknown
AI Quests is a game-based learning experience designed to teach middle school students (ages 11-14) about AI. Through a series of interactive, code-free quests, students will learn what it takes to responsibly build AI applications that address real-world societal challenges. The quests are inspired by actual AI projects led by Google Research. In 2025, we'll launch our first three AI Quests: Predicting Floods, Preventing Sight Loss, Mapping the Human Brain.
Google. Date unknown. AI Quests. https://research.google/ai-quests/intl/en_gb (AI & Environment Resource Hub; record atlas-ec140fcfc867; collection snapshot 2026-09-15).
Included in the original Course collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Source sheet: Course · Row 43 · Original ID: Not supplied.
Availability, fees, and enrollment dates may have changed since collection. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
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Shared topics: Governance and society; Research tools and geospatial methods; Weather, hazards, and adaptation
A free interactive tool that rates the AI resilience of over 1,597 U.S. occupations, classifying each as 'Changing Fast' (0–30%), 'Evolving' (30–70%), or 'Stable' (70–100%). Scores are generated by combining data from the Bureau of Labor Statistics, Anthropic, Microsoft, and Will Robots Take My Job. Users can search any occupation to understand how AI may affect their career trajectory.
Shared topics: Governance and society; Research tools and geospatial methods; Weather, hazards, and adaptation
The paper introduces Global MetNet, a global machine learning model for real-time precipitation nowcasting that predicts rainfall up to 12 hours ahead using satellite and global weather data rather than radar. Operating at high spatial (∼5 km) and temporal (15-minute) resolution, it significantly outperforms traditional numerical weather prediction models—especially in data-sparse regions of the Global South—offering rapid, accurate, and equitable forecasts already deployed to millions of users via Google Search.
Shared topics: Governance and society; Research tools and geospatial methods; Weather, hazards, and adaptation
Cathy Richards and Izni Zahidi on the democratisation of map-making, and the ethical risks arising from the use of GIS data.
Shared topics: Governance and society; Research tools and geospatial methods; Weather, hazards, and adaptation
This study uses machine learning to conduct a large-scale systematic mapping of carbon dioxide removal (CDR) research, identifying nearly 29,000 relevant studies 3–4 times more than previously estimated. It reveals that CDR research is highly concentrated in specific options like biochar, dominated by technology-focused experimental studies, and disproportionately located in China and OECD countries. The study provides an open-access database to support climate assessments and policy decisions, including future IPCC reports.