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.

scholarly journals Future-proof: preparing young Canadians for the future of work

2021 ◽  
Author(s):  
Creig Lamb ◽  
Sarah Doyle
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Economic Growth ◽  
Labour Force ◽  
Resource Scarcity ◽  
Technology And Innovation ◽  
Future Of Work ◽  
The Future ◽  
Demographic Shifts ◽  
And Robotics

There are a number of major trends that have the potential to shape the future of work, from climate change and resource scarcity to demographic shifts resulting from an aging population and immigration. This report focuses on the need to prepare Canada’s youth for a future where a great number of jobs will be rapidly created, altered or made obsolete by technology. Successive waves of technological advancements have rocked global economies for centuries, reconfiguring the labour force and giving rise to new economic opportunities with each wave. Modern advances, including artificial intelligence and robotics, once again have the potential to transform the economy, perhaps more rapidly and more dramatically than ever before. As past pillars of Canada’s economic growth become less reliable, harnessing technology and innovation will become increasingly important in driving productivity and growth.

scholarly journals Future-proof: preparing young Canadians for the future of work

2021 ◽  
Author(s):  
Creig Lamb ◽  
Sarah Doyle
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Economic Growth ◽  
Labour Force ◽  
Resource Scarcity ◽  
Technology And Innovation ◽  
Future Of Work ◽  
The Future ◽  
Demographic Shifts ◽  
And Robotics

There are a number of major trends that have the potential to shape the future of work, from climate change and resource scarcity to demographic shifts resulting from an aging population and immigration. This report focuses on the need to prepare Canada’s youth for a future where a great number of jobs will be rapidly created, altered or made obsolete by technology. Successive waves of technological advancements have rocked global economies for centuries, reconfiguring the labour force and giving rise to new economic opportunities with each wave. Modern advances, including artificial intelligence and robotics, once again have the potential to transform the economy, perhaps more rapidly and more dramatically than ever before. As past pillars of Canada’s economic growth become less reliable, harnessing technology and innovation will become increasingly important in driving productivity and growth.

The Sustainable Falcon

2020 ◽  
pp. 1-16 ◽  
Author(s):  
Heather Webb ◽  
Shubo Liu
Keyword(s):  
Climate Change ◽  
Sustainable Development ◽  
Social Impact ◽  
Sustainable Consumption ◽  
Growth Strategy ◽  
Low Carbon ◽  
Green Growth ◽  
Sustainable City ◽  
Sustainable Consumption And Production ◽  
The City

It has become vital to understand the economic, environmental, and social impact “going green” has on the region as well as on the interlinked relationship between sustainable consumption and production. This chapter focuses on Dubai's green growth strategy and the process for anticipating success while comparing its policy and initiatives to other major cities. In addition, the chapter reviews current regulations along with low carbon initiatives as part of Dubai's green, sustainable development. As Dubai prepares for Expo 2020, the city is focusing on generating sustainable, green innovations. Indeed, climate change has shaped the need for cities and countries to be more aware of their surroundings, and Dubai is no exception in developing a fully, sustainable city to become a green, economic leader.

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.

About digital transformation of the transport industry in Russia

2020 ◽  
Vol 19 (3) ◽  
pp. 95-105
Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Data Science ◽  
Digital Transformation ◽  
Digital Culture ◽  
Blockchain Technology ◽  
Community Approach ◽  
Non Linear ◽  
Transport Industry ◽  
Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

Financial risks in the face of digital transformation

Public Law ◽  
2020 ◽  
pp. 43-49
Author(s):  
A. Kovalchuk ◽  
S. Stetsenko
Keyword(s):  
Digital Transformation ◽  
Financial Risks ◽  
The Face

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 The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

2021 ◽  
Author(s):  
Francesco Piccialli ◽  
Vincenzo Schiano di Cola ◽  
Fabio Giampaolo ◽  
Salvatore Cuomo
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Digital Transformation ◽  
Daily Activities ◽  
Commercial Vehicles ◽  
Human Skills ◽  
To Come ◽  
Complex Scenario ◽  
The Impact

AbstractThe first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

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