scholarly journals Artificial Intelligence in Strategic Foresight – Current Practices and Future Application Potentials

2021 ◽  
Author(s):  
Patrick Brandtner ◽  
Marius Mates
Keyword(s):  
Artificial Intelligence ◽  
Future Application ◽  
Strategic Foresight ◽  
Current Practices

scholarly journals The future application of artificial intelligence to pathology diagnosis

2018 ◽  
Vol 29 ◽  
pp. vii1
Author(s):  
Junya Fukuoka ◽  
Kishio Kuroda ◽  
Tomoi Furukawa ◽  
Tomoya Oguri
Keyword(s):  
Artificial Intelligence ◽  
Future Application ◽  
The Future

Strategic Foresight Tools for Planning and Policy

2021 ◽  
pp. 775-797
Author(s):  
Barbara Jane Holland
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Good Predictor ◽  
Cognitive Biases ◽  
Strategic Thinking ◽  
Policy And Strategy ◽  
Strategic Foresight ◽  
Structured Approach ◽  
Blind Spots

Today, companies can no longer assume that the past will be a good predictor of the future; Those that fail to prepare for radically new possibilities may face sudden irrelevance. Strategic Foresight, aka, Futures thinking, provides a structured approach enabling people and organizations to overcome cognitive biases and think more realistically about change. It helps to uncover blind spots, imagine radically different futures, and improve decision-making. Climate disruption, artificial intelligence, and automation are quickly transforming the landscape for business and sustainability. This chapter will review the Strategic Foresight tools used to embed long-term strategic thinking and planning concerning policy and strategy.

Artificial Intelligence, Machine Learning, and Autonomous Technologies in Mining Industry

2022 ◽  
pp. 478-492
Author(s):  
Zeshan Hyder ◽  
Keng Siau ◽  
Fiona Nah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Continuous Production ◽  
Mining Industry ◽  
Economic Benefits ◽  
Current Status ◽  
Future Application ◽  
Successful Implementation ◽  
Social Challenges ◽  
Advanced Technologies

The implementation of artificial intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with autonomous trucks. Artificial intelligence, machine learning, and autonomous technologies provide many economic benefits for the mining industry through cost reduction, efficiency, and improving productivity, reducing exposure of workers to hazardous conditions, continuous production, and improved safety. However, the implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This article discusses the current status of AI, machine learning, and autonomous technologies implementation in the mining industry and highlights potential areas of future application. The article presents the results of interviews with some of the stakeholders in the industry and what their perceptions are about the threats, challenges, benefits, and potential impacts of these advanced technologies. The article also presents their views on the future of these technologies and what are some of the steps needed for successful implementation of these technologies in this sector.

Artificial Intelligence, Machine Learning, and Autonomous Technologies in Mining Industry

2019 ◽  
Vol 30 (2) ◽  
pp. 67-79 ◽  
Author(s):  
Zeshan Hyder ◽  
Keng Siau ◽  
Fiona Nah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Continuous Production ◽  
Mining Industry ◽  
Economic Benefits ◽  
Current Status ◽  
Future Application ◽  
Successful Implementation ◽  
Social Challenges ◽  
Reduction Efficiency

The implementation of artificial intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with autonomous trucks. Artificial intelligence, machine learning, and autonomous technologies provide many economic benefits for the mining industry through cost reduction, efficiency, and improving productivity, reducing exposure of workers to hazardous conditions, continuous production, and improved safety. However, the implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This article discusses the current status of AI, machine learning, and autonomous technologies implementation in the mining industry and highlights potential areas of future application. The article presents the results of interviews with some of the stakeholders in the industry and what their perceptions are about the threats, challenges, benefits, and potential impacts of these advanced technologies. The article also presents their views on the future of these technologies and what are some of the steps needed for successful implementation of these technologies in this sector.

Algorithms for immunochromatographic assay: review and impact on future application

The Analyst ◽  
2019 ◽  
Vol 144 (19) ◽  
pp. 5659-5676 ◽  
Author(s):  
Qi Qin ◽  
Kan Wang ◽  
Jinchuan Yang ◽  
Hao Xu ◽  
Bo Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Lateral Flow ◽  
Immunochromatographic Assay ◽  
Future Application ◽  
Lateral Flow Immunoassay

This review summarizes different models for the lateral flow immunoassay technology when combined with artificial intelligence and deep learning.

scholarly journals Future Vision of Interactive and Intelligent TV Systems using Edge AI

2020 ◽  
Vol 2020 (6) ◽  
pp. 51-55
Author(s):  
Álan Guedes ◽  
Antonio Busson ◽  
João Paulo Navarro ◽  
Sérgio Colcher
Keyword(s):  
Artificial Intelligence ◽  
Cost Reduction ◽  
Production Efficiency ◽  
Future Application ◽  
Strong Indication ◽  
Application Layer ◽  
Practical Applications ◽  
Low Level ◽  
Network Layers ◽  
Future Vision

Recently the Brazilian DTV system standards have been upgraded, called TV 2.5, in order to provide a better integration between broadcast and broadband services. The next Brazilian DTV system evolution, called TV 3.0, will address more deeply this convergence of TV systems not only at low-level network layers but also at the application layer. One of the new features to be addressed by this future application layer is the use of Artificial Intelligence technologies. Recently, there have been practical applications using Artificial Intelligence (AI) deployed to improve TV production efficiency and correlated cost reduction. The success in operationalize and evaluate these applications is a strong indication of the interest and relevance of AI in TV. This paper presents TeleMídia Lab’s future vision on interactive and intelligent TV Systems, with particular focus on edge AI. Edge AI means use in-device capabilities to run AI applications instead of running them in cloud.

Artificial intelligence to detect tympanic membrane perforations

2020 ◽  
Vol 134 (4) ◽  
pp. 311-315 ◽  
Author(s):  
A-R Habib ◽  
E Wong ◽  
R Sacks ◽  
N Singh
Keyword(s):  
Artificial Intelligence ◽  
Confidence Interval ◽  
Tympanic Membrane ◽  
Network Architecture ◽  
Area Under The Curve ◽  
Future Application ◽  
Proof Of Concept ◽  
Tympanic Membrane Perforations ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

AbstractObjectiveTo explore the feasibility of constructing a proof-of-concept artificial intelligence algorithm to detect tympanic membrane perforations, for future application in under-resourced rural settings.MethodsA retrospective review was conducted of otoscopic images analysed using transfer learning with Google's Inception-V3 convolutional neural network architecture. The ‘gold standard’ ‘ground truth’ was defined by otolaryngologists. Perforation size was categorised as less than one-third (small), one-third to two-thirds (medium), or more than two-thirds (large) of the total tympanic membrane diameter.ResultsA total of 233 tympanic membrane images were used (183 for training, 50 for testing). The algorithm correctly identified intact and perforated tympanic membranes (overall accuracy = 76.0 per cent, 95 per cent confidence interval = 62.1–86.0 per cent); the area under the curve was 0.867 (95 per cent confidence interval = 0.771–0.963).ConclusionA proof-of-concept image-classification artificial intelligence algorithm can be used to detect tympanic membrane perforations and, with further development, may prove to be a valuable tool for ear disease screening. Future endeavours are warranted to develop a point-of-care tool for healthcare workers in areas distant from otolaryngology.

Alexa, Is This a Historical Record?

2022 ◽  
Vol 15 (1) ◽  
pp. 1-20
Author(s):  
Santhilata Kuppili Venkata ◽  
Paul Young ◽  
Mark Bell ◽  
Alex Green
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Historical Record ◽  
Digital Transformation ◽  
National Archives ◽  
Learning Platforms ◽  
Current Practices

Digital transformation in government has brought an increase in the scale, variety, and complexity of records and greater levels of disorganised data. Current practices for selecting records for transfer to The National Archives (TNA) were developed to deal with paper records and are struggling to deal with this shift. This article examines the background to the problem and outlines a project that TNA undertook to research the feasibility of using commercially available artificial intelligence tools to aid selection. The project AI for Selection evaluated a range of commercial solutions varying from off-the-shelf products to cloud-hosted machine learning platforms, as well as a benchmarking tool developed in-house. Suitability of tools depended on several factors, including requirements and skills of transferring bodies as well as the tools’ usability and configurability. This article also explores questions around trust and explainability of decisions made when using AI for sensitive tasks such as selection.

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