πŸŽ‰ #Greenpeace gets hit with a $660M #bill for their little Dakota Access shenanigans 🀑. Turns out saving the planet might cost more than a few hugs and solar panels. Who knew eco-activism could be so expensive? πŸ’ΈπŸ’”
https://apnews.com/article/greenpeace-dakota-access-pipeline-lawsuit-verdict-5036944c1d2e7d3d7b704437e8110fbb #DakotaAccess #EcoActivism #EnvironmentalCost #ActivismFunding #HackerNews #ngated
Jury reaches verdict in trial of pipeline company's lawsuit against Greenpeace

A North Dakota jury has found Greenpeace liable for defamation and other claims in connection with protests against an oil pipeline's construction. The jury said Wednesday that the environmental advocacy group must pay more than $650 million in damages to Dallas-based Energy Transfer and its subsidiary Dakota Access. The companies had alleged defamation, trespass, nuisance, civil conspiracy and other claims against Greenpeace International, Greenpeace USA and Greenpeace Fund Inc. Attorneys for Greenpeace had denied the claims. The case reaches back to protests in 2016 and 2017 against the Dakota Access Pipeline and its Missouri River crossing upstream of the Standing Rock Sioux Tribe’s reservation.

AP News
looking for schemas, graphs, tables which clearly explain the #EnvironmentalCost of #AI tools - this stuff should be included in a 100% online learning module for undergraduate students. What would you use in this case?

How to estimate carbon footprint when training deep learning models? A guide and review
https://arxiv.org/abs/2306.08323

It is acknowledged that the development of these models has an environmental cost that has been analyzed in many studies ... we propose a comprehensive introduction & comparison of these tools for AI practitioners wishing to start estimating the environmental impact of their work.

#ML #NLP #EnvironmentalCost #GlobalWarming #CarbonFootprint #MacineLearning #GreenhouseGasEmissions #LLM

How to estimate carbon footprint when training deep learning models? A guide and review

Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowledge that the development of these models has an environmental cost that has been analyzed in many studies. Several online and software tools have been developed to track energy consumption while training machine learning models. In this paper, we propose a comprehensive introduction and comparison of these tools for AI practitioners wishing to start estimating the environmental impact of their work. We review the specific vocabulary, the technical requirements for each tool, and provide some advice on how and when to use these tools.

arXiv.org

Is it too late to halt #DeepSeaMining? Meet the activists trying to save the seabed

If #mining companies are given the go-ahead to exploit the ocean depths, the #EnvironmentalCost will be devastating. As the clock ticks down to a crucial deadline in July,
https://www.theguardian.com/environment/2023/may/21/is-it-too-late-to-halt-deep-sea-mining-the-activists-trying-to-save-the-seabed

Is it too late to halt deep-sea mining? Meet the activists trying to save the seabed

If mining companies are given the go-ahead to exploit the ocean depths, the environmental cost will be devastating. As the clock ticks down to a crucial deadline in July, Michael Segalov reports

The Guardian