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.
Six European cities – Helsinki, Amsterdam, Copenhagen, Paris, Stavanger, and Tallinn – join forces in a new project named AI4Cities. The project challenges enterprises, researchers and others to develop solutions utilising artificial intelligence (AI) to generate cuts to carbon dioxide emissions, said the City of Helsinki in a press release. Helsinki emphasises utilisation of data and AI in its digitalisation programme to achieve the city's climate goals. The participating cities' respective programmes to cut carbon dioxide emissions emphasise emissions from transport and housing. Consequently, the AI4Cities Project focuses on emissions generated from transport and traffic as well as the energy consumption by buildings.
Together with the UN Refugee Agency (UNHCR) 34 collaborators built several AI and machine learning based solutions to predict forced displacement, violent conflicts, and climate change in Somalia. In addition, an exploratory data analysis resulted in powerful insights regarding conflict types, areas, and reasons. The findings will help UNHCR to execute necessary support mechanism for people at need in a faster and more effective way. Millions of people in Somalia are forced to leave their current area of residence or community due to resource shortage and natural disasters like droughts and floods as well as violent conflicts. Our challenge partner, UNHCR, provides assistance and protection for those who are forcibly displaced inside of Somalia.
As the nations of Earth attempt to invent and implement solutions to the growing threat of climate change, just about every option is on the table. Investing in renewable sources of energy and dropping emissions around the globe are the dominant strategies, but utilizing artificial intelligence can help reduce the damage done by climate change. As reported by Live Mint, artificial intelligence algorithms can help conservationists limit deforestation, protect vulnerable species of animals from climate change, fight poaching, and monitor air pollution. The data science company Gramener has employed machine learning to help get estimates of the number of penguin colonies across Antarctica by analyzing images taken by camera traps. The size of penguin colonies in Antarctica has decreased dramatically over the course of the past decade, impacted by climate change.
To address the many pressing scientific questions and challenges facing our planet, we must increase global understanding of how human activity is affecting natural systems and create a community of change, driven by data and cutting-edge technology. Modern technologies, such as satellite imaging, bioacoustic monitoring, environmental DNA, and genomics, can capture data at a global scale, but also produce massive, complex data sets. Artificial intelligence (AI) and cloud computing can capitalize on the potential of such data, leading to faster and more meaningful insights and creating the opportunity for transformative solutions. The National Geographic Society and Microsoft's AI for Earth program are partnering to support novel projects that create and deploy AI tools to improve the way we monitor, model, understand, and ultimately manage Earth's natural resources for a more sustainable future. The grants given by the partnership will support projects that use cloud computing to create and deploy open-source models and algorithms that make key analytical processes more efficient in the field.
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.
Rising sea levels are not just predicted to change the landscape of the US, but it will also reshape where millions of people call home. Scientist used artificial intelligence to map where people will migrate once their coastal residence are under six-feet of water. The technology estimates nearly 13 million Americans will be forced to move by the end of the century, with many heading inland to land-locked cities such as Atlanta, Houston, Dallas, Denver and Las Vegas. The model also predicts suburban and rural areas in the Midwest will experience disproportionately large influx of people relative to their smaller local populations. The technology estimates nearly 13 million Americans will be forced to move by the end of the century, with many heading inland to land-locked cities such as Atlanta, Houston, Dallas, Denver and Las Vegas.
A team of University of Illinois researchers estimated the mortality costs associated with air pollution in the U.S. by developing and applying a novel machine learning-based method to estimate the life-years lost and cost associated with air pollution exposure. Scholars from the Gies College of Business at Illinois studied the causal effects of acute fine particulate matter exposure on mortality, health care use and medical costs among older Americans through Medicare data and a unique way of measuring air pollution via changes in local wind direction. The researchers - Tatyana Deryugina, Nolan Miller, David Molitor and Julian Reif - calculated that the reduction in particulate matter experienced between 1999-2013 resulted in elderly mortality reductions worth $24 billion annually by the end of that period. Garth Heutel of Georgia State University and the National Bureau of Economic Research was a co-author of the paper. "Our goal with this paper was to quantify the costs of air pollution on mortality in a particularly vulnerable population: the elderly," said Deryugina, a professor of finance who studies the health effects and distributional impact of air pollution.
Do you remember what Steve Jobs said about'making a dent in the universe?' Well, the way Artificial Intelligence and Big Data are improving lives it seems it would be much easier to do so with these technologies. Be it fraud prevention, automation, security, banking, and now forecasting climate change, AI and data-driven technologies are making rapid progress. Take the finance sector, for instance, AI has been serving it for years by automating and streamlining the customer experience. Additionally, AI-driven identity verification systems are detecting fraud, eliminating fraudsters, and helping banks through automation.
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.