Second-wave AI and Afro-existential norms

2021 ◽  
Vol 9 (3) ◽  
pp. 49-64
Author(s):  
Abiola Azeez ◽  
Tosin Adeate
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Great Influence ◽  
Human Existence ◽  
Human Intelligence ◽  
Data Sets ◽  
Shared Experience ◽  
Set Up ◽  
Specialized Hardware ◽  
Second Wave

The idea of afro-existentialism connotes how Africans make sense of living and the meaning and meaninglessness attached to human existence. Different phenomena inform the way humans interpret existence, and one of such in the contemporary period, with great influence on Africans, is human involvement with non-human intelligence (AI), in its different eruptions. This paper focuses on the second-wave AI, which is a period of improved simulation of natural intelligence, whose singularity principle hypothesizes individualist motives. The paper asks, to what extent do Afroexistential norms accommodate second-wave AI? Partly in disagreement with the claim that AI is for everyone, we argue that second-wave artificial intelligence weakly adapts to Afro-existential practices, which is largely communal, emphasizing shared experience. We justify this claim by arguing that Western ethical patterns, which inform the features of the second-wave AI such as statistical patterns, smart algorithm, specialized hardware, and big data sets, emerge from individualist notions. This paper argues that second-wave AI trends do not reflect African norms of existence being factored into ordering algorithmic patterns that set up AI systems and programs. We infer that Afro-existential practices unsettles with the individualist principle which underlines second-wave AI and therefore, a conversation around the development and application of communal interpretation of AI is important.

Big Data and Machine Learning

2018 ◽  
pp. 225-239
Author(s):  
Fernando Enrique Lopez Martinez ◽  
Edward Rolando Núñez-Valdez
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Complete Solution ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Healthcare Organizations ◽  
High Level

IoT, big data, and artificial intelligence are currently three of the most relevant and trending pieces for innovation and predictive analysis in healthcare. Many healthcare organizations are already working on developing their own home-centric data collection networks and intelligent big data analytics systems based on machine-learning principles. The benefit of using IoT, big data, and artificial intelligence for community and population health is better health outcomes for the population and communities. The new generation of machine-learning algorithms can use large standardized data sets generated in healthcare to improve the effectiveness of public health interventions. A lot of these data come from sensors, devices, electronic health records (EHR), data generated by public health nurses, mobile data, social media, and the internet. This chapter shows a high-level implementation of a complete solution of IoT, big data, and machine learning implemented in the city of Cartagena, Colombia for hypertensive patients by using an eHealth sensor and Amazon Web Services components.

scholarly journals Artificial Intelligence and Health in Nepal

2020 ◽  
Vol 10 (3) ◽  
pp. 915-918
Author(s):  
Alexander Van Teijlingen ◽  
Tell Tuttle ◽  
Hamid Bouchachia ◽  
Brijesh Sathian ◽  
Edwin Van Teijlingen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
South Asian ◽  
Low Income ◽  
Short Communication ◽  
Data Sets ◽  
Low Income Country ◽  
Local Resources ◽  
Self Driving Cars ◽  
Key Terms

The growth in information technology and computer capacity has opened up opportunities to deal with much and much larger data sets than even a decade ago.  There has been a technological revolution of big data and Artificial Intelligence (AI).  Perhaps many readers would immediately think about robotic surgery or self-driving cars, but there is much more to AI.  This Short Communication starts with an overview of the key terms, including AI, machine learning, deep learning and Big Data.  This Short Communication highlights so developments of AI in health that could benefit a low-income country like Nepal and stresses the need for Nepal’s health and education systems to track such developments and apply them locally.  Moreover, Nepal needs to start growing its own AI expertise to help develop national or South Asian solutions.  This would require investing in local resources such as access to computer power/capacity as well as training young Nepali to work in AI. 

scholarly journals Tweaking Business Planning With Artificial Intelligence

2021 ◽  
Vol 2 (4) ◽  
pp. 1-22
Author(s):  
Jing Rui Chen ◽  
P. S. Joseph Ng
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analysis ◽  
Learning Experience ◽  
Compulsory Education ◽  
Large Data ◽  
Data Sets ◽  
Chinese Market ◽  
Artificial Intelligence Technology ◽  
Education Support

Griffith AI&BD is a technology company that uses big data platform and artificial intelligence technology to produce products for schools. The company focuses on primary and secondary school education support and data analysis assistance system and campus ARTIFICIAL intelligence products for the compulsory education stage in the Chinese market. Through big data, machine learning and data mining, scattered on campus and distributed systems enable anyone to sign up to join the huge data processing grid, and access learning support big data analysis and matching after helping students expand their knowledge in a variety of disciplines and learning and promotion. Improve the learning process based on large data sets of students, and combine ai technology to develop AI electronic devices. To provide schools with the best learning experience to survive in a competitive world.

scholarly journals Future trends: businness view and high tech

2020 ◽  
pp. 6
Author(s):  
José Rafael Marques da Silva ◽  
Manuela Correia
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Business Models ◽  
High Tech ◽  
Human Intelligence ◽  
Future Trends ◽  
Arable Crops ◽  
Food Traceability ◽  
Slide Presentation ◽  
The Future

This topic presents the macro-design of SPA that will surely appear in the coming years and also the future technological trends in SPA applied to viticulture and arable crops. A vision of the future of SPA is presented in three layers: i) human intelligence (related to soil, plants, climate, pests, diseases, environment, food production, fibre and energy) on top; ii) artificial intelligence (related to hardware, communications, data) in the middle; iii) and again human intelligence on the bottom (consumers, business models, transparency, food traceability). “Big Data” challenges are discussed regarding the specific needs of agriculture. The technological groups identified in a Foresight Analysis report are discussed and the future technological trends on arable crops and vineyards are presented. In this topic, materials include a slide presentation, a document text and the Foresight Analysis report.

scholarly journals Interpreting, analyzing and distributing information: A big data framework for competitive intelligence

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Eduardo Luis Casarotto ◽  
Guilherme Cunha Malafaia ◽  
Marta Pagán Martínez ◽  
Erlaine Binotto
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Technological Innovation ◽  
New Technologies ◽  
Decision Makers ◽  
Strategic Decision ◽  
Competitive Intelligence ◽  
Technological Innovations ◽  
Data Sets ◽  
Data Framework

This paper aimed to develop a data-based technological innovation frameworkfocused on the competitive intelligence process. Technological innovations increasinglytransform the behavior of societies, affecting all sectors. Solutions such as cloud computing, theInternet of Things, and artificial intelligence provide and benefit from a vast generation of data:large data sets called Big Data. The use of new technologies in all sectors increases in the faceof such innovation and technological mechanisms of management. We advocated that the use ofBig Data and the competitive intelligence process could help generate or maintain a competitiveadvantage for organizations. We based the proposition of our framework on the concepts of BigData and competitive intelligence. Our proposal is a theoretical framework for use in thecollection, treatment, and distribution of information directed to strategic decision-makers. Itssystematized architecture allows the integration of processes that generate information fordecision making.

Artificial Intelligence and Big Data Analytics in Support of Cyber Defense

2021 ◽  
pp. 1738-1753
Author(s):  
Louise Leenen ◽  
Thomas Meyer
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Data Sets ◽  
Data Types ◽  
Cyber Defense ◽  
Intelligent Decision Making ◽  
Big Data Technologies

Cybersecurity analysts rely on vast volumes of security event data to predict, identify, characterize, and deal with security threats. These analysts must understand and make sense of these huge datasets in order to discover patterns which lead to intelligent decision making and advance warnings of possible threats, and this ability requires automation. Big data analytics and artificial intelligence can improve cyber defense. Big data analytics methods are applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends, and other useful information. Artificial intelligence provides algorithms that can reason or learn and improve their behavior, and includes semantic technologies. A large number of automated systems are currently based on syntactic rules which are generally not sophisticated enough to deal with the level of complexity in this domain. An overview of artificial intelligence and big data technologies in cyber defense is provided, and important areas for future research are identified and discussed.

Copyright Exceptions Reform and AI Data Analysis in ChinaA Modest Proposal

2021 ◽  
pp. 196-220
Author(s):  
Tianxiang He
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Incomplete Data ◽  
Copyright Law ◽  
Data Sets ◽  
Copyright Exception ◽  
Using Data

The development of artificial intelligence (AI) technology is firmly connected to the availability of big data. However, using data sets involving copyrighted works for AI analysis or data mining without authorization will incur risks of copyright infringement. Considering the fact that incomplete data collection may lead to data bias, and since it is impossible for the user of AI technology to obtain a copyright licence from each and every right owner of the copyrighted works used, a mechanism that can free the data from copyright restrictions under certain conditions is needed. In the case of China, it is crucial to check whether China’s current copyright exception model can take on the role and offer that kind of function. This chapter suggests that a special AI analysis and data mining copyright exception that follows a semi-open style should be added to the current exceptions list under the Copyright Law of China.

Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions

2022 ◽  
pp. 30-57
Author(s):  
Richard S. Segall
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Recurrent Neural Networks ◽  
Big Data Analytics ◽  
Data Sets ◽  
Open Source Data ◽  
Source Data

The purpose of this chapter is to illustrate how artificial intelligence (AI) technologies have been used for COVID-19 detection and analysis. Specifically, the use of neural networks (NN) and machine learning (ML) are described along with which countries are creating these techniques and how these are being used for COVID-19 diagnosis and detection. Illustrations of multi-layer convolutional neural networks (CNN), recurrent neural networks (RNN), and deep neural networks (DNN) are provided to show how these are used for COVID-19 detection and prediction. A summary of big data analytics for COVID-19 and some available COVID-19 open-source data sets and repositories and their characteristics for research and analysis are also provided. An example is also shown for artificial intelligence (AI) and neural network (NN) applications using real-time COVID-19 data.

scholarly journals Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4727
Author(s):  
Maysam Abbod ◽  
Jiann-Shing Shieh
Keyword(s):  
Artificial Intelligence ◽  
Signal Processing ◽  
Big Data ◽  
Deep Learning ◽  
Health Monitoring ◽  
Intelligent Systems ◽  
Measurement Techniques ◽  
Mechanical Systems ◽  
Data Sets ◽  
Special Issue

Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.

Innovative Devops for Artificial Intelligence

2019 ◽  
Vol 19 (1) ◽  
pp. 58-63 ◽  
Author(s):  
R. Ciucu ◽  
F.C. Adochiei ◽  
Ioana-Raluca Adochiei ◽  
F. Argatu ◽  
G.C. Seriţan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Performance Optimization ◽  
High Performance ◽  
Computational Models ◽  
Data Sets ◽  
Production Environments ◽  
Model Training ◽  
And Performance ◽  
Set Up ◽  
Computational Resources

AbstractDeveloping Artificial Intelligence is a labor intensive task. It implies both storage and computational resources. In this paper, we present a state-of-the-art service based infrastructure for deploying, managing and serving computational models alongside their respective data-sets and virtual environments. Our architecture uses key-based values to store specific graphs and datasets into memory for fast deployment and model training, furthermore leveraging the need for manual data reduction in the drafting and retraining stages. To develop the platform, we used clustering and orchestration to set up services and containers that allow deployment within seconds. In this article, we cover high performance computing concepts such as swarming, GPU resource management for model implementation in production environments with emphasis on standardized development to reduce integration tasks and performance optimization.

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