AI & Environment Resource HubRobinson, David; Miron, Marius; Hagiwara, Masato; Weck, Benno; Keen, Sara; Alizadeh, Milad; Narula, Gagan; Geist, Matthieu; Pietquin, Olivier · 2024-11-11
NatureLM-audio, a language-audio model for bioacoustics, advances species detection and conservation by setting new benchmarks in animal vocalization analysis.
Robinson, David; Miron, Marius; Hagiwara, Masato; Weck, Benno; Keen, Sara; Alizadeh, Milad; Narula, Gagan; Geist, Matthieu; Pietquin, Olivier. 2024-11-11. NatureLM-Audio: an Audio-Language Foundation Model for Bioacoustics. https://arxiv.org/abs/2411.07186 (AI & Environment Resource Hub; record paper-034; collection snapshot 2026-09-15).
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Source sheet: Scientific Paper · Row 38 · Original ID: paper-034.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Biodiversity and ecosystems
Learn about how AI can be used to protect biodiversity, fight climate change, and just better understand our planet through 5-minute explainers covering academic papers on AI for the Planet. AI is not just chatbots! Grace Lindsay is a professor of Data Science & Psychology. She teaches a course on Machine Learning for Climate Change.
Shared topics: Biodiversity and ecosystems
Trust is a cornerstone and enabler of human civilization, determining the very nature of how people interact with each other. The swift integration of artificial intelligence (AI) into daily life poses grand societal challenges and necessitates a reevaluation of trust. Our bibliometric literature review calls for scientists and stakeholders to cross traditional academic boundaries to address emerging and evolving societal challenges arising from AI. We propose a transdisciplinary research framework to understand and bolster trust in AI and address grand challenges in domains as diverse and urgent as misinformation, discrimination, and warfare.
Shared topics: Biodiversity and ecosystems
The abstract argues that policy debates about “sustainable AI” are stuck in an overly simplistic binary, AI as both an environmental threat and a tool for ecological transition, which leads to shallow regulatory approaches focused mostly on energy reporting. Drawing on law-and-technology scholarship, the authors show how technological determinism, exceptionalism, and regulatory solutionism distort the debate, and they propose reframing regulation around the socio-economic drivers and power dynamics of AI development rather than the technology itself.
Shared topics: Biodiversity and ecosystems
Improved machine learning techniques enhance bird call detection in passive acoustic monitoring, boosting precision and biodiversity tracking in climate research.