environmental law


AI system can predict air pollution before it happens

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Air pollution kills an estimated seven million people every year and cities around the world are being forced to take action to do what they can to lower the risk to inhabitants. A team of Loughborough University computer scientists believe their AI system has the potential to provide new insight into the environmental factors that have significant impacts on air pollution levels. In particular it focuses on the amount of'PM2.5' In 2013, a study involving 312,944 people in nine European countries revealed that there was no safe level of particulates. PM2.5 particulates were found to be particularly deadly, blamed for a 36 per cent increase in lung cancer per 10 μg/m3 as they can penetrate deep into the lungs.


AI Analysis Shows Improvement in Conservation of Endangered Species

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Researchers using artificial intelligence to grade decades of conservation efforts have determined we're getting better at reintroducing once-endangered species to the wild. In their study published Thursday in the journal Patterns, the researchers analyzed the abstracts of more than 4,000 studies of species reintroduction across four decades and found that we're generally improving in our conservation efforts. The authors hope that machine learning could be used in this field, as well as others, to discover the best techniques and solutions from the ever-growing plethora of scientific research. "We wanted to learn some lessons from the vast body of conservation biology literature on reintroduction programs that we could use here in California as we try to put sea otters back into places they haven't roamed for decades," said senior author Kyle Van Houtan, chief scientist at Monterey Bay Aquarium in California. "But what sat in front of us was millions of words and thousands of manuscripts. We wondered how we could extract data from them that we could actually analyze, and so we turned to natural language processing."


Four Quick Facts About How AI Is Changing The World

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Artificial intelligence technology has continued to grow in recent years, stunning the world with its latest innovations. But, some are admittedly growing weary about AI and its continuous growth. With talk of robots one day replacing humans for labor, concerns of an increasingly tech dependent world grow stronger. A report from Oxford researchers stated that 47% of American jobs will be at risk by 2030 because of automation. However, AI is truly changing the world - providing innovation that can change how we approach healthcare, the environment, and the day to day act of living.


A novel artificial intelligence system that predicts air pollution levels

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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.


Four Quick Facts About How AI Is Changing The World

#artificialintelligence

Artificial intelligence technology has continued to grow in recent years, stunning the world with its latest innovations. But, some are admittedly growing weary about AI and its continuous growth. With talk of robots one day replacing humans for labor, concerns of an increasingly tech dependent world grow stronger. A report from Oxford researchers stated that 47% of American jobs will be at risk by 2030 because of automation. However, AI is truly changing the world - providing innovation that can change how we approach healthcare, the environment, and the day to day act of living.


Multi-resolution Multi-task Gaussian Processes

Neural Information Processing Systems

We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels. We offer a multi-resolution multi-task (MRGP) framework that allows for both inter-task and intra-task multi-resolution and multi-fidelity. We develop shallow Gaussian Process (GP) mixtures that approximate the difficult to estimate joint likelihood with a composite one and deep GP constructions that naturally handle biases. In doing so, we generalize existing approaches and offer information-theoretic corrections and efficient variational approximations. We demonstrate the competitiveness of MRGPs on synthetic settings and on the challenging problem of hyper-local estimation of air pollution levels across London from multiple sensing modalities operating at disparate spatio-temporal resolutions.


AI is helping protect endangered species: Microsoft Federal News Network

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In fact, the idea is really two fold. We take all that data that was collected in 2016 and they had already put all that time in to labeling. And now hopefully we can train the model and then do two things. One is the next flight. When they come back with lots and lots of images, hopefully we can require much less human time to annotate all of those.


Sign the Petition

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We are deeply concerned about how much flight traffic is caused by us - machine learners and, more generally, (data) scientists who should understand the dangers of climate change. Although we acknowledge that scientific exchange is difficult without traveling, we believe that video conferences - if set up properly - could become an increasingly important replacement. By streaming talks, some conferences already offer the opportunity to follow remotely. However, usually it is strictly required that authors present their work via physical attendance. Especially in machine learning, where conferences play an important role in scientific communication and careers, young scientists cannot realistically choose not to publish at the main venues, "just" because they are too far away.


Analytical Equations based Prediction Approach for PM2.5 using Artificial Neural Network

arXiv.org Machine Learning

Particulate matter pollution is one of the deadliest types of air pollution worldwide due to its significant impacts on the global environment and human health. Particulate Matter (PM2.5) is one of the important particulate pollutants to measure the Air Quality Index (AQI). The conventional instruments used by the air quality monitoring stations to monitor PM2.5 are costly, bulkier, time-consuming, and power-hungry. Furthermore, due to limited data availability and non-scalability, these stations cannot provide high spatial and temporal resolution in real-time. To overcome the disadvantages of existing methodology this article presents analytical equations based prediction approach for PM2.5 using an Artificial Neural Network (ANN). Since the derived analytical equations for the prediction can be computed using a Wireless Sensor Node (WSN) or low-cost processing tool, it demonstrates the usefulness of the proposed approach. Moreover, the study related to correlation among the PM2.5 and other pollutants is performed to select the appropriate predictors. The large authenticate data set of Central Pollution Control Board (CPCB) online station, India is used for the proposed approach. The RMSE and coefficient of determination (R2) obtained for the proposed prediction approach using eight predictors are 1.7973 ug/m3 and 0.9986 respectively. While the proposed approach results show RMSE of 7.5372 ug/m3 and R2 of 0.9708 using three predictors. Therefore, the results demonstrate that the proposed approach is one of the promising approaches for monitoring PM2.5 without power-hungry gas sensors and bulkier analyzers.


Artificial Intelligence in Indian Agriculture - CII Blog

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The agriculture and allied sectors are considered the bedrock of India's economy. With farming employing almost half of India's workforce, Agri Gross Domestic Product (GDP) can be considered the engine of growth for the economy. The global need to produce 50% more food by 2050 cannot be accomplished if only 4% of the land is under cultivation.The vulnerabilities arising from climate change, coupled with the risk of increased dependency on unsustainable agriculture practices, can lead to agricultural distress. Artificial Intelligence (AI), along with other digital technologies, will play a key role in modernizing agricultural activities and realising the goal of doubling the farmer's income by 2022. The global'AI in agriculture' market size is expected to be worth USD 2.6 billion by 2025.