Agricultural climate change and multilingual GIS database translation system based on embedded database and artificial intelligence

2021 ◽  
Vol 14 (11) ◽  
Author(s):  
Meiqin Lv
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Translation System ◽  
Gis Database ◽  
Embedded Database

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

scholarly journals Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

scholarly journals Using Artificial Intelligence to Visualize the Impacts of Climate Change

2021 ◽  
Vol 41 (1) ◽  
pp. 8-14
Author(s):  
Alexandra Luccioni ◽  
Victor Schmidt ◽  
Vahe Vardanyan ◽  
Yoshua Bengio ◽  
Theresa-Marie Rhyne
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Impacts Of Climate Change

scholarly journals Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

2021 ◽  
Author(s):  
Joseph Nyangon
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Carbon Budget ◽  
Energy Sector ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Legal Challenges ◽  
Nationally Determined Contributions ◽  
Implementation Level

The Paris Agreement on climate change requires nations to keep the global temperature within the 2°C carbon budget. Achieving this temperature target means stranding more than 80% of all proven fossil energy reserves as well as resulting in investments in such resources becoming stranded assets. At the implementation level, governments are experiencing technical, economic, and legal challenges in transitioning their economies to meet the 2°C temperature commitment through the nationally determined contributions (NDCs), let alone striving for the 1.5°C carbon budget, which translates into greenhouse gas emissions (GHG) gap. This chapter focuses on tackling the risks of stranded electricity assets using machine learning and artificial intelligence technologies. Stranded assets are not new in the energy sector; the physical impacts of climate change and the transition to a low-carbon economy have generally rendered redundant or obsolete electricity generation and storage assets. Low-carbon electricity systems, which come in variable and controllable forms, are essential to mitigating climate change. These systems present distinct opportunities for machine learning and artificial intelligence-powered techniques. This chapter considers the background to these issues. It discusses the asset stranding discourse and its implications to the energy sector and related infrastructure. The chapter concludes by outlining an interdisciplinary research agenda for mitigating the risks of stranded assets in electricity investments.

How to tackle Climate Change using Artificial Intelligence

2021 ◽  
Author(s):  
Rishikesh Bamdale ◽  
Saurabh Shelar ◽  
Varsha Khandekar
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Tackle Climate Change

This Sacred Life

2021 ◽  
Author(s):  
Norman Wirzba
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Environmental Degradation ◽  
Human Life ◽  
Genetic Enhancement ◽  
Social Injustice ◽  
God's Love ◽  
Bear Witness

In a time of climate change, environmental degradation, and social injustice, the question of the value and purpose of human life has become urgent. What are the grounds for hope in a wounded world? This Sacred Life gives a deep philosophical and religious articulation of humanity's identity and vocation by rooting people in a symbiotic, meshwork world that is saturated with sacred gifts. The benefits of artificial intelligence and genetic enhancement notwithstanding, Norman Wirzba shows how an account of humans as interdependent and vulnerable creatures orients people to be a creative, healing presence in a world punctuated by wounds. He argues that the commodification of places and creatures needs to be resisted so that all life can be cherished and celebrated. Humanity's fundamental vocation is to bear witness to God's love for creaturely life, and to commit to the construction of a hospitable and beautiful world.

scholarly journals Social Impact Returns. Filling the Finance Gap with Data Value

2021 ◽  
Author(s):  
Amparo Marin de la Barcena Grau
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Social Impact ◽  
Resource Scarcity ◽  
Digital Transformation ◽  
Growth Strategy ◽  
Financial Risks ◽  
Common Value ◽  
Use Of Data ◽  
Data Value

Sustainability, regulation and environmental issues such as climate change and resource scarcity are emerging as key trends with decisive impact on company’s Risk management, value creation and growth strategy. This combination represents one of the biggest opportunities to Society as a whole, including organizations, Governments and citizens. Typically, companies possess vast amounts of data, most of it unutilized. Many are now making investments in digital transformation, which generates even more data. The issue is how to generate social impact returns. The use of data and data analytics is centuries old, but with Artificial Intelligence (AI), Machine Learning (ML), jointly with other distributed ledger technologies (Blockchain, Cloud) that are advancing rapidly, there are major opportunities to capture value better, cheaper and faster. Speed is of the essence, and success depends on how fast organizations understand the need for non-financial risks management and respond to data-driven intelligence by reallocating resources to accomplish what needs to be done more efficiently. The reason for impact returns is understanding the benefit as a common value, not exclusive to companies, but it also has to distribute value among individuals, communities, and why not, to contribute to regenerate our planet based on a new economy.

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