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AI to help organizations cut greenhouse gas emissions by 16%

#artificialintelligence

The potential positive impact of Artificial Intelligence (AI) is significant and organizations can expect to cut GHG emissions by 16% in the next three to five years through AI-driven climate action projects according to a research report by Capgemini Research Institute. Despite the considerable potential of AI for climate action, adoption remains low. More than eight in ten organizations spend less than 5% of climate change investment on AI and data tracking; 54% have fewer than 5% of employees with the skills to take up data and AI-driven roles; and more than a third (37%) of sustainability executives have decelerated their climate goals in light of COVID-19, with the highest deceleration in the energy and utilities industry. Only 13% of organizations have aligned their climate vision and strategy with their AI capabilities – these are who Capgemini defines as climate AI champions. Two-fifths of these come from Europe, followed by the Americas and APAC.


It's Time to Prioritize Energy Efficient Green Artificial Intelligence

#artificialintelligence

Rapid developments in AI have triggered digital advancements in almost every industry. The technology is capable of construing data contextually to provide requested information, supply analysis, and push events based on findings. Simultaneously, businesses need to meet social, investor and regulatory requirements regarding how they use advanced technologies like AI. Significantly, it is also crucial that organizations must commit to using the technology with a purpose, which leads to the way of sustainable development. In its recent study, the Allen Institute for AI argued the prioritization of "Green AI" efforts that focus on the energy efficiency of AI systems. The study was based on many high-profile advances in AI that have wavered carbon footprints.


It's Time to Prioritize Energy Efficient Green Artificial Intelligence

#artificialintelligence

Rapid developments in AI have triggered digital advancements in almost every industry. The technology is capable of construing data contextually to provide requested information, supply analysis, and push events based on findings. Simultaneously, businesses need to meet social, investor and regulatory requirements regarding how they use advanced technologies like AI. Significantly, it is also crucial that organizations must commit to using the technology with a purpose, which leads to the way of sustainable development. In its recent study, the Allen Institute for AI argued the prioritization of "Green AI" efforts that focus on the energy efficiency of AI systems. The study was based on many high-profile advances in AI that have wavered carbon footprints.


Council Post: In EU's Climate Change Fight, The 2 Trillion Euros Was The Easy Part

#artificialintelligence

Bureaucrats -- particularly those from the European Union (EU) -- rarely get the praise they deserve. By their nature, they are reserved, so they do not draw attention to themselves when things go well. When things go poorly, though, they make for a convenient target. So when the EU does something bold, we should give it its due. The EU's boldness in addressing a host of environmental problems head-on is unmatched.


A novel artificial intelligence system that predicts air pollution levels

#artificialintelligence

Imagine being scared to breathe the air around you. An unusual concept for us here in the UK, but it is a genuine concern for communities all over the world with air pollution killing an estimated seven million people every year. A team of Loughborough University computer scientists are hoping to help eradicate this fear with a new artificial intelligence (AI) system they have developed that can predict air pollution levels hours in advance. The technology is novel for a number of reasons, one being that it has the potential to provide new insight into the environmental factors that have significant impacts on air pollution levels. Professor Qinggang Meng and Dr. Baihua Li are leading the project which is focused on using AI to predict PM2.5--particulate matter of less than 2.5 microns (10-6 m) in diameter--that is often characterized as reduced visibility in cities and hazy-looking air when levels are high.


BMI: A Behavior Measurement Indicator for Fuel Poverty Using Aggregated Load Readings from Smart Meters

arXiv.org Machine Learning

Fuel poverty affects between 50 and 125 million households in Europe and is a significant issue for both developed and developing countries globally. This means that fuel poor residents are unable to adequately warm their home and run the necessary energy services needed for lighting, cooking, hot water, and electrical appliances. The problem is complex but is typically caused by three factors; low income, high energy costs, and energy inefficient homes. In the United Kingdom (UK), 4 million families are currently living in fuel poverty. Those in series financial difficulty are either forced to self-disconnect or have their services terminated by energy providers. Fuel poverty contributed to 10,000 reported deaths in England in the winter of 2016-2107 due to homes being cold. While it is recognized by governments as a social, public health and environmental policy issue, the European Union (EU) has failed to provide a common definition of fuel poverty or a conventional set of indicators to measure it. This chapter discusses current fuel poverty strategies across the EU and proposes a new and foundational behavior measurement indicator designed to directly assess and monitor fuel poverty risks in households using smart meters, Consumer Access Device (CAD) data and machine learning. By detecting Activities of Daily Living (ADLS) through household appliance usage, it is possible to spot the early signs of financial difficulty and identify when support packages are required.


Google aims AI at whales, words and well-being - France 24

#artificialintelligence

Google on Tuesday provided a look at efforts to put artificial intelligence to use for good, from protecting whales to breaking language barriers. The internet giant unveiled projects on AI work teams a week after Google chief executive Sundar Pichai urged a "proportional approach" to regulating the technology. Among demonstrations on Tuesday was a "bioacoustics" project using AI to help scientists, governments and nonprofit groups track endangered species. Two years ago, Google partnered with the US National Oceanic and Atmospheric Adminstration to track humpback whales by using AI recognize the sound of whales in audio captured by underwater microphones. Google on Tuesday announced an alliance with environmental groups to track critically endangered killer whales in the Salish Sea using AI.


Should Artificial Intelligence Governance be Centralised? Design Lessons from History

arXiv.org Artificial Intelligence

Can effective international governance for artificial intelligence remain fragmented, or is there a need for a centralised international organisation for AI? We draw on the history of other international regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak in favour of centralisation. Conversely, the risk of creating a slow and brittle institution speaks against it, as does the difficulty in securing participation while creating stringent rules. Other considerations depend on the specific design of a centralised institution. A well-designed body may be able to deter forum shopping and ensure policy coordination. However, forum shopping can be beneficial and a fragmented landscape of institutions can be self-organising. Centralisation entails trade-offs and the details matter. We conclude with two core recommendations. First, the outcome will depend on the exact design of a central institution. A well-designed centralised regime covering a set of coherent issues could be beneficial. But locking-in an inadequate structure may pose a fate worse than fragmentation. Second, for now fragmentation will likely persist. This should be closely monitored to see if it is self-organising or simply inadequate.


Smell Pittsburgh: Engaging Community Citizen Science for Air Quality

arXiv.org Artificial Intelligence

Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools. Taking this into account, we developed Smell Pittsburgh, a system that enables community members to report odors and track where these odors are frequently concentrated. All smell report data are publicly accessible online. These reports are also sent to the local health department and visualized on a map along with air quality data from monitoring stations. This visualization provides a comprehensive overview of the local pollution landscape. Additionally, with these reports and air quality data, we developed a model to predict upcoming smell events and send push notifications to inform communities. We also applied regression analysis to identify statistically significant effects of push notifications on user engagement. Our evaluation of this system demonstrates that engaging residents in documenting their experiences with pollution odors can help identify local air pollution patterns, and can empower communities to advocate for better air quality. All citizen-contributed smell data are publicly accessible and can be downloaded from https://smellpgh.org.


Drones show how Greenland Ice Sheet fractures causing dramatic waterfall and rising sea levels

Daily Mail - Science & tech

Captivating images capture by custom-built drones have revealed the damage to the Greenland Ice Sheet that is being caused by rising global temperatures. The images, which have been taken as part of an EU-funded project to track changes in the world's second-largest ice sheet, are the first drone-based observations of how fractures form and expand under meltwater lakes. The expanding fractures cause catastrophic lake drainages, during which huge quantities of water are transferred to below the surface of the ice. Changes in ice flow occur on a much shorter timescales than were previously considered possible, said the research team, which was led by the University of Cambridge. 'It's possible we've under-estimated the effects of these glaciers on the overall instability of the Greenland Ice Sheet,' said drone pilot Tom Chudley, a PhD student at the University of Cambridge's Scott Polar Research Institute.