Environmental conservation organizations routinely monitor news content on conservation in protected areas to maintain situational awareness of developments that can have an environmental impact. Existing automated media monitoring systems require large amounts of data labeled by domain experts, which is only feasible at scale for high-resource languages like English. However, such tools are most needed in the global south where news of interest is mainly in local low-resource languages, and far fewer experts are available to annotate datasets sustainably. In this paper, we propose NewsSerow, a method to automatically recognize environmental conservation content in low-resource languages. NewsSerow is a pipeline of summarization, in-context few-shot classification, and self-reflection using large language models (LLMs). Using at most 10 demonstration example news articles in Nepali, NewsSerow significantly outperforms other few-shot methods and achieves comparable performance with models fully fine-tuned using thousands of examples. The World Wide Fund for Nature (WWF) has deployed NewsSerow for media monitoring in Nepal, significantly reducing their operational burden, and ensuring that AI tools for conservation actually reach the communities that need them the most. NewsSerow has also been deployed for countries with other languages like Colombia.

Welcome to our September 2022 monthly digest, where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. This month, amongst other things, we find out more about environmental conservation, synthesizing new medicines, the efficiency of large language models, and the RoboCup Humanoid League. A key part of this work focusses on how to strategically allocate limited resources. Her primary application area is poaching prevention, helping rangers in protected areas around the world plan patrols and identify poaching hotspots. In this blog post, Christopher Franz and Kevin Schewior write about how they applied a well-known algorithm for solving two-player games to the problem of synthesizing new molecules.

In the theoretical ecology world, for example, what they've been doing for several decades now, planning and what they call adaptive management, looks at Markov decision processes to model how environments change over time If an animal population has 100 individuals, and then a year passes and there's like no hunting, then the population will increase to some amount. But then if there's hunting or if there's a drought or something like that, then the population would decrease by this amount. Those are probabilistic systems that you can model. Markov decision processes have been used in computer science for several decades, and it's awesome that this model has been useful in ecology as well. But then in the past10 or so years, there's been a lot of new advances in computer science for planning using reinforcement learning enabling us to model these systems more effectively, account for uncertainty, do robust planning.

Tens of thousands of dead fish have been found floating in a canal on New York's Long Island this week and authorities are investigating what caused it. Countless bunker fish were seen floating near the water's edge on the Shinnecock Canal in Hampton Bays on Monday morning in what scientists are calling unprecedented. Locals have branded it the'fishpocalypse'. Drone footage taken by Hampton Watercraft shows the unbelievable extent of the stranded fish, all stacked on top of each other on the surface of the water, with the canal looking like it is covered in ice from afar. Regional Department of Environmental Conservation spokesman Bill Fonda told Newsday that the die-off was most likely caused by suffocation and not chemicals or pollutants and urged concerned locals not to worry.