Datacenters meet resistance over environmental concerns as AI boom spreads in Latin America

The Guardian — Artificial IntelligenceMonday, November 10, 2025 at 11:58:06 PM
Datacenters meet resistance over environmental concerns as AI boom spreads in Latin America
As the artificial intelligence boom expands in Latin America, communities in some of the region's driest areas are pushing back against the construction of datacenters due to environmental concerns. These facilities, which are crucial for powering AI technologies, are facing demands for greater transparency from local populations who are wary of the impact on their environment. The situation highlights the tension between rapid technological advancement and the need for sustainable practices, as governments seek foreign investment while communities advocate for their rights and environmental protection.
— via World Pulse Now AI Editorial System

Was this article worth reading? Share it

Recommended Readings
MADiff: Motion-Aware Mamba Diffusion Models for Hand Trajectory Prediction on Egocentric Videos
PositiveArtificial Intelligence
The article presents MADiff, a novel method for predicting hand trajectories in egocentric videos using diffusion models. This approach aims to enhance the understanding of human intentions and actions, which is crucial for advancements in embodied artificial intelligence. The challenges of capturing high-level human intentions and the effects of camera egomotion interference are addressed, making this method significant for applications in extended reality and robot manipulation.
Toward Gaze Target Detection of Young Autistic Children
PositiveArtificial Intelligence
The paper discusses the automatic detection of gaze targets in young autistic children using artificial intelligence. This technology aims to enhance the quality of life for children who may not have sufficient access to professionals. A new Autism Gaze Target (AGT) dataset has been created to support this research, and a novel Socially Aware Coarse-to-Fine (SACF) framework is proposed to improve gaze detection by considering social contexts, addressing the common issue of class imbalance in autism datasets.
SimuFreeMark: A Noise-Simulation-Free Robust Watermarking Against Image Editing
PositiveArtificial Intelligence
SimuFreeMark is a proposed watermarking framework designed to enhance image security against editing attacks, particularly in the context of artificial intelligence-generated content (AIGC). Unlike existing methods that depend on noise simulation, SimuFreeMark directly embeds watermarks into the low-frequency components of images, which have shown significant robustness against various attacks. This innovation aims to address the growing need for reliable watermarking solutions in an era of advanced image manipulation techniques.
SemanticNN: Compressive and Error-Resilient Semantic Offloading for Extremely Weak Devices
PositiveArtificial Intelligence
The article presents SemanticNN, a novel semantic codec designed for extremely weak embedded devices in the Internet of Things (IoT). It addresses the challenges of integrating artificial intelligence (AI) on such devices, which often face resource limitations and unreliable network conditions. SemanticNN focuses on achieving semantic-level correctness despite bit-level errors, utilizing a Bit Error Rate (BER)-aware decoder and a Soft Quantization (SQ)-based encoder to enhance collaborative inference offloading.
Do AI Voices Learn Social Nuances? A Case of Politeness and Speech Rate
PositiveArtificial Intelligence
A recent study published on arXiv investigates whether advanced text-to-speech systems can learn social nuances, specifically the human tendency to slow speech for politeness. Researchers tested 22 synthetic voices from AI Studio and OpenAI under polite and casual conditions, finding that the polite prompts resulted in significantly slower speech across both platforms. This suggests that AI can internalize and replicate subtle psychological cues in human communication.
Forecasters at the US National Hurricane Center are increasingly leaning on Google's new DeepMind prediction model, though questions about its methods remain (Eric Holthaus/The Guardian)
NeutralArtificial Intelligence
Forecasters at the US National Hurricane Center are increasingly utilizing Google's new DeepMind prediction model, which is designed to provide faster and more accurate hurricane forecasts. Despite its advantages, questions regarding the model's methods and reliability persist. The model is noted for being less expensive and time-consuming, potentially aiding in saving lives and property during hurricane events.
How do 'AI detection' tools actually work? And are they effective?
NeutralArtificial Intelligence
As nearly half of all Australians report having recently used artificial intelligence (AI) tools, understanding the mechanisms and effectiveness of AI detection tools is increasingly important. The rise in AI usage raises questions about the reliability of these detection tools, which are designed to identify AI-generated content. This growing reliance on AI prompts discussions about the implications for various sectors, including education and content creation, as stakeholders seek to navigate the evolving landscape of AI technology.