scholarly journals Artificial Communication? The Production of Contingency by Algorithms

2017 ◽  
Vol 46 (4) ◽  
pp. 249-265 ◽  
Author(s):  
Elena Esposito
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Social Systems ◽  
Learning Algorithms ◽  
Artificial Reproduction ◽  
Process Information ◽  
Social Agents ◽  
Recent Developments ◽  
Self Learning

AbstractDiscourse about smart algorithms and digital social agents still refers primarily to the construction of artificial intelligence that reproduces the faculties of individuals. Recent developments, however, show that algorithms are more efficient when they abandon this goal and try instead to reproduce the ability to communicate. Algorithms that do not “think” like people can affect the ability to obtain and process information in society. Referring to the concept of communication in Niklas Luhmann’s theory of social systems, this paper critically reconstructs the debate on the computational turn of big data as the artificial reproduction not of intelligence but of communication. Self-learning algorithms parasitically take advantage – be it consciously or unaware – of the contribution of web users to a “virtual double contingency.” This provides society with information that is not part of the thoughts of anyone, but, nevertheless, enters the communication circuit and raises its complexity. The concept of communication should be reconsidered to take account of these developments, including (or not) the possibility of communicating with algorithms.

Reinforcement Learning for Cloud Computing Digital Library

2014 ◽  
Vol 571-572 ◽  
pp. 105-108
Author(s):  
Lin Xu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Digital Library ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Current Status ◽  
Self Learning ◽  
New Framework

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

scholarly journals Ethical Applications of Big Data-Driven AI on Social Systems: Literature Analysis and Example Deployment Use Case

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 235
Author(s):  
Paulo Garcia ◽  
Francine Darroch ◽  
Leah West ◽  
Lauren BrooksCleator
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Social Issues ◽  
Social Computing ◽  
Social Systems ◽  
Human Interaction ◽  
Data Driven ◽  
Vulnerable Groups ◽  
Use Case ◽  
Ongoing Research

The use of technological solutions to address the production of goods and offering of services is ubiquitous. Health and social issues, however, have only slowly been permeated by technological solutions. Whilst several advances have been made in health in recent years, the adoption of technology to combat social problems has lagged behind. In this paper, we explore Big Data-driven Artificial Intelligence (AI) applied to social systems; i.e., social computing, the concept of artificial intelligence as an enabler of novel social solutions. Through a critical analysis of the literature, we elaborate on the social and human interaction aspects of technology that must be in place to achieve such enabling and address the limitations of the current state of the art in this regard. We review cultural, political, and other societal impacts of social computing, impact on vulnerable groups, and ethically-aligned design of social computing systems. We show that this is not merely an engineering problem, but rather the intersection of engineering with health sciences, social sciences, psychology, policy, and law. We then illustrate the concept of ethically-designed social computing with a use case of our ongoing research, where social computing is used to support safety and security in home-sharing settings, in an attempt to simultaneously combat youth homelessness and address loneliness in seniors, identifying the risks and potential rewards of such a social computing application.

scholarly journals Artificial Intelligence in Education

Seminar.net ◽  
2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Xavier Giró Gràcia ◽  
Juana M. Sancho-Gil
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Digital Technology ◽  
Social Systems ◽  
Machine Learning Algorithms ◽  
Educational Systems ◽  
Negative Effects ◽  
Black Boxes ◽  
Artificial Intelligence In Education

Digital technology is constantly permeating and transforming all social systems, and education is not an exception. In the last decade, the unstoppable development of Artificial Intelligence, based on machine learning algorithms and fuelled by Big Data, has given a new push to the hope of improving learning-based machines, and providing educational systems with ‘effective’ solutions. Educators, educational researchers and policymakers, in general, lack the knowledge and expertise to understand the underlying logic of these new ‘black boxes’, and we do not have sufficient research-based evidence to understand the consequences that an excessive use of screens has in students’ development. This paper first discusses the notions behind what Big Data is and what it means in our current society; how data is the new currency that has driven the use of algorithms in all areas of our society, and specifically in the field of Artificial Intelligence; and the concept of ‘black boxes’, and its possible impact on education. Then, it discusses the underlying educational discourses, pointing out the need to analyse not only their contributions but also their possible negative effects. It finishes with considerations and a proposed agenda for further studying this phenomenon.

Introduction to Machine Learning and Its Implementation Techniques

2020 ◽  
pp. 334-358
Author(s):  
Arul Murugan R. ◽  
Sathiyamoorthi V.
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
High Speeds ◽  
Learning Techniques ◽  
Implementation Techniques ◽  
Gain Access

Machine learning (ML) is one of the exciting sub-fields of artificial intelligence (AI). The term machine learning is generally stated as the ability to learn without being explicitly programmed. In recent years, machine learning has become one of the thrust areas of research across various business verticals. The technical advancements in the field of big data have provided the ability to gain access over large volumes of diversified data at ease. This massive amount of data can be processed at high speeds in a reasonable amount of time with the help of emerging hardware capabilities. Hence the machine learning algorithms have been the most effective at leveraging all of big data to provide near real-time solutions even for the complex business problems. This chapter aims in giving a solid introduction to various widely adopted machine learning techniques and its applications categorized into supervised, unsupervised, and reinforcement and will serve a simplified guide for the aspiring data and machine learning enthusiasts.

scholarly journals Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
E. A. Huerta ◽  
Asad Khan ◽  
Edward Davis ◽  
Colleen Bushell ◽  
William D. Gropp ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Social Patterns ◽  
Computing Paradigm ◽  
Recent Developments ◽  
Optimization Schemes ◽  
Performance Computing

Abstract Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry.

scholarly journals Artificial Intelligence Based Body Sensor Network Framework—Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ashwin A. Phatak ◽  
Franz-Georg Wieland ◽  
Kartik Vempala ◽  
Frederik Volkmar ◽  
Daniel Memmert
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Collection ◽  
Real Time ◽  
Sensor Network ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Body Sensor Network

AbstractWith the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality ‘big data’ in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

scholarly journals State-of-the-Art of Artificial Intelligence

2021 ◽  
Author(s):  
Ramjee Prasad ◽  
Purva Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
State Of The Art ◽  
Turing Test ◽  
Rapid Progress ◽  
Current State ◽  
Recent Developments ◽  
Historical Developments ◽  
Insight Into ◽  
Made In

Artificial Intelligence (AI) as a technology has existed for less than a century. In spite of this, it has managed to achieve great strides. The rapid progress made in this field has aroused the curiosity of many technologists around the globe and many companies across various domains are curious to explore its potential. For a field that has achieved so much in such a short duration, it is imperative that people who aim to work in Artificial Intelligence, study its origins, recent developments, and future possibilities of expansion to gain a better insight into the field. This paper encapsulates the notable progress made in Artificial Intelligence starting from its conceptualization to its current state and future possibilities, in various fields. It covers concepts like a Turing machine, Turing test, historical developments in Artificial Intelligence, expert systems, big data, robotics, current developments in Artificial Intelligence across various fields, and future possibilities of exploration.

Artificial Intelligence and Machine Learning Algorithms

2020 ◽  
pp. 188-219
Author(s):  
Amit Kumar Tyagi ◽  
Poonam Chahal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Techniques

With the recent development in technologies and integration of millions of internet of things devices, a lot of data is being generated every day (known as Big Data). This is required to improve the growth of several organizations or in applications like e-healthcare, etc. Also, we are entering into an era of smart world, where robotics is going to take place in most of the applications (to solve the world's problems). Implementing robotics in applications like medical, automobile, etc. is an aim/goal of computer vision. Computer vision (CV) is fulfilled by several components like artificial intelligence (AI), machine learning (ML), and deep learning (DL). Here, machine learning and deep learning techniques/algorithms are used to analyze Big Data. Today's various organizations like Google, Facebook, etc. are using ML techniques to search particular data or recommend any post. Hence, the requirement of a computer vision is fulfilled through these three terms: AI, ML, and DL.

An Experimental Healthcare System

2021 ◽  
pp. 301-308
Author(s):  
Miroslav M. Bojović ◽  
Veljko Milutinović ◽  
Dragan Bojić ◽  
Nenad Korolija
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Knowledge Management ◽  
Deep Learning ◽  
Electronic Health Records ◽  
Clinical Guidelines ◽  
Learning Algorithms ◽  
Healthcare Systems ◽  
Starting Point ◽  
Data Architecture

Contemporary healthcare systems face growing demand for their services, rising costs, and a workforce. Artificial intelligence has the potential to transform how care is delivered and to help meet the challenges. Recent healthcare systems have been focused on using knowledge management and AI. The proposed solution is to reach explainable and causal AI by combining the benefits of the accuracy of deep-learning algorithms with visibility on the factors that are important to the algorithm's conclusion in a way that is accessible and understandable to physicians. Therefore, the authors propose AI approach in which the encoded clinical guidelines and protocols provide a starting point augmented by models that learn from data. The new structure of electronic health records that connects data from wearables and genomics data and innovative extensible big data architecture appropriate for this AI concept is proposed. Consequently, the proposed technology may drastically decrease the need for expensive software and hopefully eliminates the need to do diagnostics in expensive institutions.

scholarly journals Media jak z Matrixa. Niewidoczne maszyny w komunikacji społecznej w ujęciu teorii systemów

2021 ◽  
Vol 13 ◽  
pp. 13-33
Author(s):  
Dorota Płuchowska
Keyword(s):  
Artificial Intelligence ◽  
Systems Theory ◽  
Social Communication ◽  
Technical Communication ◽  
Sociological Theory ◽  
Social Systems ◽  
Effective Form ◽  
The Matrix ◽  
Reflective Development ◽  
Self Learning

With reference to the assumptions of the sociological theory of communication of social systems, its understanding of society and its media, the article deals with the issue of how the change of social communication dictated by the digitalization of media changes society. Society accepts the presence of digitization but looks for fields to criticize the non-reflective development of its routine. An example is the movie The Matrix, to which the analysis relates. The examples show what the technology of technical communication and self-learning (artificial intelligence) is already able to do today. The article summar-izes that the introduction of media 4.0 to communication — with their example of invisible machines — is consent to their “participation” (in an automated but effective form) in the creation of society. This influences the autopoietic notion of society developed by systems theory.

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