Inteligencia Artificial para la conservación y uso sostenible de la biodiversidad, una visión desde Colombia (Artificial Intelligence for conservation and sustainable use of biodiversity, a view from Colombia)
The rise of artificial intelligence (AI) and the aggravating biodiversity crisis have resulted in a research area where AI-based computational methods are being developed to act as allies in conservation, and the sustainable use and management of natural resources. While important general guidelines have been established globally regarding the opportunities and challenges that this interdisciplinary research offers, it is essential to generate local reflections from the specific contexts and realities of each region. Hence, this document aims to analyze the scope of this research area from a perspective focused on Colombia and the Neotropics. In this paper, we summarize the main experiences and debates that took place at the Humboldt Institute between 2023 and 2024 in Colombia. To illustrate the variety of promising opportunities, we present current uses such as automatic species identification from images and recordings, species modeling, and in silico bioprospecting, among others. From the experiences described above, we highlight limitations, challenges, and opportunities for in order to successfully implementate AI in conservation efforts and sustainable management of biological resources in the Neotropics. The result aims to be a guide for researchers, decision makers, and biodiversity managers, facilitating the understanding of how artificial intelligence can be effectively integrated into conservation and sustainable use strategies. Furthermore, it also seeks to open a space for dialogue on the development of policies that promote the responsible and ethical adoption of AI in local contexts, ensuring that its benefits are harnessed without compromising biodiversity or the cultural and ecosystemic values inherent in Colombia and the Neotropics.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementSimilar Papers 제목 키워드 기반
Estudio de la eficiencia en la escalabilidad de GPUs para el entrenamiento de Inteligencia Artificial
Training large-scale deep learning models has become a key challenge for the scientific community and industry. While the massive use of GPUs can significantly speed up training times, this approach has a negative impact…
FOLEY-VAE: Generación de efectos de audio para cine con inteligencia artificial
In this research, we present an interface based on Variational Autoencoders trained with a wide range of natural sounds for the innovative creation of Foley effects. The model can transfer new sound features to prerecord…
Inteligencia Artificial jurídica y el desafío de la veracidad: análisis de alucinaciones, optimización de RAG y principios para una integración responsable
This technical report analyzes the challenge of "hallucinations" (false information) in LLMs applied to law. It examines their causes, manifestations, and the effectiveness of the RAG mitigation strategy, highlighting it…
Design and Validation of a Responsible Artificial Intelligence-based System for the Referral of Diabetic Retinopathy Patients
Diabetic Retinopathy (DR) is a leading cause of vision loss in working-age individuals. Early detection of DR can reduce the risk of vision loss by up to 95%, but a shortage of retinologists and challenges in timely exam…
Inteligencia artificial para la multi-clasificación de fauna en fotografías automáticas utilizadas en investigación científica
The management of natural environments, whether for conservation or production, requires a deep understanding of wildlife. The number, location, and behavior of wild animals are among the main subjects of study in ecolog…
Management