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Environmental Law


How Can AI Aid in Predicting and Fighting Global Climate Change?

#artificialintelligence

Planet Earth is rapidly growing warmer, and scientists are looking for different ways to predict the tipping points in climate change. The phenomenon of climate change is chaotic. For years, researchers and scientists have looked for successful methods of batting global climate change but were unable to find a solution as effective as AI. The integration of AI-powered systems has given a chance to environmentalists to address key issues, including threats to sustainability, food and water shortages, loss of biodiversity, climate change, and other environmental problems. AI paired with data sciences and machine learning can help find rigorous patterns to reduce and eradicate carbon footprints.


Using Spatial Information to Detect Lead Pipes

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For centuries, cities in the United States used an inexpensive, malleable, and leak-resistant material for constructing their water pipes: lead. Today, the health risks posed by lead pipes are well-known. Drinking lead-contaminated water can stunt children's development and cause heart and kidney problems among adults.¹ The Environmental Protection Agency (EPA) banned the use of lead pipes for new construction in 1986. Yet, today, lead services lines (the pipes that take water from city lines into individual homes) are still prevalent across the country.


Opinion: Artificial Intelligence Could Save Earth

#artificialintelligence

With Earth being in the middle of climate crisis, there have been talks about the ways to save our planet over the past few years. Here a technology like Artificial Intelligence (AI) has the potential to achieve this. AI was valued at USD $62.3 billion in 2020, growing with a CAGR of 40.2 per cent it is expected to value USD $997.77 billion by 2028. This machine learning driven platform aids in identifying patterns using huge chunks of data. The respondents involved in AI projects state that in the next 3 to 5 years AI enabled medical devices are predicted to reduce average global emissions by 18.3 per cent.


Calculating the future environmental impacts of the metaverse - Verdict

#artificialintelligence

The metaverse looks likely to reach into every corner of our lives. Apple, Disney, Nvidia, Microsoft, and Meta (formerly Facebook) have all stated their intentions to get involved, but the environmental costs from AI workloads that will arise from running the metaverse on a large scale will be huge. However, recent technological innovations in data centers will help. Furthermore, the metaverse may offset emissions by changing the very ways we interact with each other. The metaverse is a virtual world where users can share experiences and interact in real time within simulated scenarios.


This soft, dragonfly-shaped robot could help resolve environmental issues

#artificialintelligence

The researchers believe these types of measurements could play an important part in an environmental robotic sensor in the future. Responsiveness to pH can detect freshwater acidification, which is a serious environmental problem affecting several geologically-sensitive regions. The ability to soak up oils makes such long-distance skimming robots an ideal candidate for early detection of oil spills. Changing colors due to temperatures could help spot signs of red tide and the bleaching of coral reefs, which leads to decline in the population of aquatic life.


Unraveling the hidden environmental impacts of AI solutions for environment

arXiv.org Artificial Intelligence

In the past ten years artificial intelligence has encountered such dramatic progress that it is seen now as a tool of choice to solve environmental issues and in the first place greenhouse gas emissions (GHG). At the same time the deep learning community began to realize that training models with more and more parameters required a lot of energy and as a consequence GHG emissions. To our knowledge, questioning the complete environmental impacts of AI methods for environment ("AI for green"), and not only GHG, has never been addressed directly. In this article we propose to study the possible negative impact of "AI for green" 1) by reviewing first the different types of AI impacts 2) by presenting the different methodologies used to assess those impacts, in particular life cycle assessment and 3) by discussing how to assess the environmental usefulness of a general AI service.


Energy consumption of AI poses environmental problems – TechTarget – SearchEnterpriseAI

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Between storing data in large-scale data centers and then using that data to train a machine learning or deep learning model, AI energy consumption is high.


Energy consumption of AI poses environmental problems

#artificialintelligence

Take some of the most popular language models, for example. OpenAI trained its GPT-3 model on 45 terabytes of data. To train the final version of MegatronLM, a language model similar to but smaller than GPT-3, Nvidia ran 512 V100 GPUs over nine days. A single V100 GPU can consume between 250 and 300 watts. If we assume 250 watts, then 512 V100 GPUS consumes 128,000 watts, or 128 kilowatts (kW).


On the Opportunities and Risks of Foundation Models

arXiv.org Artificial Intelligence

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.


QUT researchers ramp up koala conservation efforts with AI hub

ZDNet

Researchers from the Queensland University of Technology (QUT) have established an AI hub to expand the use of drones and infrared imaging as part of efforts to ramp up conversation work around protecting endangered animals, such as koalas. Last year, QUT researchers recognised that using AI-enabled infrared drones could help accurately identify koala populations located in bushfire affected areas and dense bushland. Research lead Grant Hamilton said establishing the AI Hub would now allow the team to build out the system and work with local conversation groups and organisations, such as Landcare, that can assist with using the drones and thermal imaging detection to survey bushfire affected areas for koalas, before transmitting the raw data back to the QUT hub for analysing. "This system will allow Landcare groups, conservation groups, organisations working on protecting and monitoring species to survey large areas in their regions, anywhere in Australia, with the use of drones and thermal imaging detection, and send the data back to us where we can process it. It's citizen science on a much bigger scale," he said, describing it as a "game changer".