scholarly journals The Rise of Artificial Intelligence under the Lens of Sustainability

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 100 ◽  
Author(s):  
Jayden Khakurel ◽  
Birgit Penzenstadler ◽  
Jari Porras ◽  
Antti Knutas ◽  
Wenlu Zhang
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Vital Role ◽  
Future Research ◽  
Five Dimensions ◽  
Machine Learning Methods ◽  
Research Collaborations ◽  
Potential Benefits ◽  
Negative Impacts

Since the 1950s, artificial intelligence (AI) has been a recurring topic in research. However, this field has only recently gained significant momentum because of the advances in technology and algorithms, along with new AI techniques such as machine learning methods for structured data, modern deep learning, and natural language processing for unstructured data. Although companies are eager to join the fray of this new AI trend and take advantage of its potential benefits, it is unclear what implications AI will have on society now and in the long term. Using the five dimensions of sustainability to structure the analysis, we explore the impacts of AI on several domains. We find that there is a significant impact on all five dimensions, with positive and negative impacts, and that value, collaboration, sharing responsibilities; ethics will play a vital role in any future sustainable development of AI in society. Our exploration provides a foundation for in-depth discussions and future research collaborations.

scholarly journals Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 664
Author(s):  
Nikos Kanakaris ◽  
Nikolaos Giarelis ◽  
Ilias Siachos ◽  
Nikos Karacapilidis
Keyword(s):  
Language Processing ◽  
Scientific Knowledge ◽  
Link Prediction ◽  
Performance Metrics ◽  
Future Research ◽  
Knowledge Graph ◽  
Prediction Problem ◽  
Textual Information ◽  
Research Collaborations ◽  
Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

Automatic information retrievement for exporting services: First project findings from the development of an AI based export decision supporting instrument

2021 ◽  
pp. 2-11
Author(s):  
David Aufreiter ◽  
Doris Ehrlinger ◽  
Christian Stadlmann ◽  
Margarethe Uberwimmer ◽  
Anna Biedersberger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Research Process ◽  
Training Data ◽  
Future Research ◽  
Manufacturing Companies ◽  
Market Information ◽  
Annotation Scheme ◽  
Export Decisions ◽  
Decision Supporting

On the servitization journey, manufacturing companies complement their offerings with new industrial and knowledge-based services, which causes challenges of uncertainty and risk. In addition to the required adjustment of internal factors, the international selling of services is a major challenge. This paper presents the initial results of an international research project aimed at assisting advanced manufacturers in making decisions about exporting their service offerings to foreign markets. In the frame of this project, a tool is developed to support managers in their service export decisions through the automated generation of market information based on Natural Language Processing and Machine Learning. The paper presents a roadmap for progressing towards an Artificial Intelligence-based market information solution. It describes the research process steps of analyzing problem statements of relevant industry partners, selecting target countries and markets, defining parameters for the scope of the tool, classifying different service offerings and their components into categories and developing annotation scheme for generating reliable and focused training data for the Artificial Intelligence solution. This paper demonstrates good practices in essential steps and highlights common pitfalls to avoid for researcher and managers working on future research projects supported by Artificial Intelligence. In the end, the paper aims at contributing to support and motivate researcher and manager to discover AI application and research opportunities within the servitization field.

scholarly journals Merging Artificial Intelligence and Blockchain Technologies to Solve Academic Qualification Forgery Issues

2020 ◽  
Author(s):  
Manju Jose
Keyword(s):  
Artificial Intelligence ◽  
Industrial Revolution ◽  
Emerging Technologies ◽  
Integrated System ◽  
Future Research ◽  
Group Discussions ◽  
Academic Qualification ◽  
Negative Impacts ◽  
Economy And Education ◽  
Academic Qualifications

This paper emphasizes the possibility of merging artificial intelligence and Blockchain technologies to solve academic qualifications forgery issues in the educational sectors. Empirical data is collected through interviews with specialists and technical people who are interested in the emerging technologies of the Fourth Industrial Revolution and focus group discussions in the field, as well as from reports in the reviewed literary articles. Scientific journals have also been accessed to analyse the paper goals and objectives. The findings suggest that emerging technologies can be integrated to become more efficient and effective in detecting fraud and forgery before it occurs. Considerable attention should be given to reducing and combating these issues because they have significant negative impacts on the economy and education. Accordingly, the study makes recommendations based on the results and areas of future research, considering the establishment of a unified and integrated system. Initially it will be applied as a pilot in Sultanate of Oman, then gradually it will be extended to the Gulf Cooperation Council States (GCC) and internationally particularly the affiliated and the recognized educational institutions to avoid the phenomena that affects the reputation and quality of education institutions and academic qualifications. The conclusion considers the impacts of the proposed system in the education and economy as well in general.

scholarly journals Modelling of Traffic Flows and Supply Chains Based on Geospatial Knowledge

2021 ◽  
Vol 2068 (1) ◽  
pp. 012042
Author(s):  
A Kolesnikov ◽  
P Kikin ◽  
E Panidi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supply Chains ◽  
Inventory Management ◽  
Traffic Flows ◽  
Machine Learning Methods ◽  
Customer Interaction ◽  
Long Term Impact ◽  
Artificial Intelligence Systems

Abstract The field of logistics and transport operates with large amounts of data. The transformation of such arrays into knowledge and processing using machine learning methods will help to find additional reserves for optimizing transport and logistics processes and supply chains. This article analyses the possibilities and prospects for the application of machine learning and geospatial knowledge in the field of logistics and transport using specific examples. The long-term impact of geospatial-based artificial intelligence systems on such processes as procurement, delivery, inventory management, maintenance, customer interaction is considered.

scholarly journals Artificial Intelligence in Global Health —A Framework and Strategy for Adoption and Sustainability

2020 ◽  
Vol 9 (1) ◽  
pp. 121-127 ◽  
Author(s):  
Trevor D. Hadley ◽  
Rowland W. Pettit ◽  
Tahir Malik ◽  
Amelia A. Khoei ◽  
Hamisu M. Salihu
Keyword(s):  
Artificial Intelligence ◽  
Global Health ◽  
Language Processing ◽  
Communicable Disease ◽  
Birth Asphyxia ◽  
Disease Outbreaks ◽  
Healing Time ◽  
Future Research ◽  
Medical Specialties ◽  
Community Healthcare

Artificial Intelligence (AI) applications in medicine have grown considerably in recent years. AI in the forms of Machine Learning, Natural Language Processing, Expert Systems, Planning and Logistics methods, and Image Processing networks provide great analytical aptitude. While AI methods were first conceptualized for radiology, investigations today are established across all medical specialties. The necessity for proper infrastructure, skilled labor, and access to large, well-organized data sets has kept the majority of medical AI applications in higher-income countries. However, critical technological improvements, such as cloud computing and the near-ubiquity of smartphones, have paved the way for use of medical AI applications in resource-poor areas. Global health initiatives (GHI) have already begun to explore ways to leverage medical AI technologies to detect and mitigate public health inequities. For example, AI tools can help optimize vaccine delivery and community healthcare worker routes, thus enabling limited resources to have a maximal impact. Other promising AI tools have demonstrated an ability to: predict burn healing time from smartphone photos; track regions of socioeconomic disparity combined with environmental trends to predict communicable disease outbreaks; and accurately predict pregnancy complications such as birth asphyxia in low resource settings with limited patient clinical data. In this commentary, we discuss the current state of AI-driven GHI and explore relevant lessons from past technology-centered GHI. Additionally, we propose a conceptual framework to guide the development of sustainable strategies for AI-driven GHI, and we outline areas for future research. Keywords: • Artificial Intelligence • AI Framework • Global Health • Implementation • Sustainability • AI Strategy   Copyright © 2020 Hadley et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Dams: Effects of Hydrological Infrastructure on Development

2019 ◽  
Vol 11 (1) ◽  
pp. 125-148
Author(s):  
Andrew Dillon ◽  
Ram Fishman
Keyword(s):  
Economic Development ◽  
Flood Control ◽  
The Body ◽  
Future Research ◽  
Emerging Issues ◽  
Potential Benefits ◽  
The Impact ◽  
Benefits And Costs ◽  
Distributional Impacts

Hydrological investments, particularly irrigation dams, have multiple potential benefits for economic development. Dams also have financial, environmental, and distributional impacts that can affect their benefits and costs. This article reviews the evidence on the impact of dams on economic development, focusing on the levels and variability of agricultural productivity, and its effect on poverty, health, electricity generation, and flood control. We also review the evidence on irrigation efficiency and collective action of dam maintenance. Throughout the discussion, we highlight the empirical challenges that restrict the body of causally interpretable impact estimates and areas in which the evidence is particularly thin. We conclude with a discussion of emerging issues pertaining to the long-term sustainability of dams’ impacts and suggest directions for future research.

scholarly journals Psychedelics and potential benefits in “healthy normals”: A review of the literature

2019 ◽  
Vol 3 (3) ◽  
pp. 280-287 ◽  
Author(s):  
Sam Gandy
Keyword(s):  
Psychological Functioning ◽  
Well Being ◽  
Future Research ◽  
Life Meaning ◽  
Potential Development ◽  
Potential Benefits ◽  
High Doses ◽  
And Behavior

We are in the midst of a psychedelic research renaissance. With research examining the efficacy of psychedelics as a treatment for a range of mental health indications still in its early stages, there is an increasing body of research to show that careful use of psychedelics can yield a variety of benefits in “healthy normals” and so lead to “the betterment of well people.” Psychedelics have been found to modulate neuroplasticity, and usage in a supportive setting can result in enduring increases in traits such as well-being, life satisfaction, life meaning, mindfulness, and a variety of measures associated with prosocial behaviors and healthy psychological functioning. The effect of psychedelic experience on measures of personality trait openness and is potential implications is examined, and the potential role of awe as a mediator of the benefits of the psychedelic experience is discussed. Special attention is given to the capacity of psychedelics to increase measures of nature relatedness in an enduring sense, which is being correlated with a broad range of measures of psychological well-being as well as a key predictor of pro-environmental awareness and behavior. The effects of particular classical psychedelic compounds on healthy people are discussed, with special attention given to the mystical-type experiences occasioned by high doses of psychedelics, which appear to be an important mediator of long-term benefits and psychotherapeutic gains. Research looking at the potential benefits of psychedelic microdosing is discussed. Potential future research avenues are explored, focusing on the potential development of psychedelics as agents of ecotherapy.

scholarly journals Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

2020 ◽  
Vol 78 (4) ◽  
pp. 1547-1574
Author(s):  
Sofia de la Fuente Garcia ◽  
Craig W. Ritchie ◽  
Saturnino Luz
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Language Processing ◽  
Future Research ◽  
Original Research ◽  
Speech And Language ◽  
Eligibility Criteria ◽  
Speech And Language Processing

Background: Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. Objective: Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. Methods: Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. Results: From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). Conclusion: Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity

2019 ◽  
Vol 30 (1) ◽  
pp. 61-79 ◽  
Author(s):  
Weiyu Wang ◽  
Keng Siau
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Future Research ◽  
Way Of Life ◽  
Negative Effects ◽  
Future Research Directions ◽  
Negative Impacts ◽  
The Impact ◽  
The Way

The exponential advancement in artificial intelligence (AI), machine learning, robotics, and automation are rapidly transforming industries and societies across the world. The way we work, the way we live, and the way we interact with others are expected to be transformed at a speed and scale beyond anything we have observed in human history. This new industrial revolution is expected, on one hand, to enhance and improve our lives and societies. On the other hand, it has the potential to cause major upheavals in our way of life and our societal norms. The window of opportunity to understand the impact of these technologies and to preempt their negative effects is closing rapidly. Humanity needs to be proactive, rather than reactive, in managing this new industrial revolution. This article looks at the promises, challenges, and future research directions of these transformative technologies. Not only are the technological aspects investigated, but behavioral, societal, policy, and governance issues are reviewed as well. This research contributes to the ongoing discussions and debates about AI, automation, machine learning, and robotics. It is hoped that this article will heighten awareness of the importance of understanding these disruptive technologies as a basis for formulating policies and regulations that can maximize the benefits of these advancements for humanity and, at the same time, curtail potential dangers and negative impacts.

scholarly journals Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

2020 ◽  
Author(s):  
Thanh Thi Nguyen
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Medical Image ◽  
Current Status ◽  
Future Research ◽  
Research Directions ◽  
Daily Lives ◽  
Related Data ◽  
Future Research Directions ◽  
The Internet Of Things

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

Sign in / Sign up

Export Citation Format

Share Document