6 pages, Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centered around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good.
8 pages, Reducing food waste is widely recognized as critical for improving resource efficiency and meeting the nutritional demand of a growing human population. Here we explore whether the sharing economy can provide meaningful assistance to reducing food waste in a relatively low-impact and environmentally-sound way. Analyzing 170,000 postings on a popular peer-to-peer food-sharing app, we find that over 19 months, 90t of food waste with an equivalent retail value of £0.7 million were collected by secondary consumers and diverted from disposal. An environmental analysis focused on Greater London reveals that these exchanges were responsible for avoiding emission of 87–156t of CO2eq. Our results indicate that most exchanges were among users associated with lower income yet higher levels of education. These findings, together with the high collection rates (60% on average) suggest that the sharing economy may offer powerful means for improving resource efficiency and reducing food waste.
Baranowski, Dariusz B. (author), Flatau, Maria K. (author), Flatau, Piotr J. (author), Karnawati, Dwikorita (author), Barabasz, Katarzyna (author), Lubaz, Michal (author), Latos, Beata (author), Schmidt, Jerome M. (author), Paski, Jaka A.I. (author), and Marzuki (author)
Format:
Journal article
Publication Date:
2020-05-19
Published:
UK: Nature Portfolio
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 207 Document Number: D13091
10 pages, Floods are a major contributor to natural disasters in Sumatra. However, atmospheric conditions leading to floods are not well understood due, among other factors, to the lack of a complete record of floods. Here, the 5 year flood record for Sumatra derived from governmental reports, as well as from crowd-sourcing data, based on Twitter messages and local newspapers’ reports, is created and used to analyze atmospheric phenomena responsible for floods. It is shown, that for the majority of analyzed floods, convectively coupled Kelvin waves, large scale precipitation systems propagating at ∼12 m/s along the equator, play the critical role. While seasonal and intraseasonal variability can also create conditions favorable for flooding, the enhanced precipitation related to Kelvin waves was found in over 90% of flood events. In 30% of these events precipitation anomalies were attributed to Kelvin waves only. These results indicate the potential for increased predictability of flood risk.
