Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications [Working Title]

2021 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Novel Applications

scholarly journals Special Issue on AISAT 2009 - Selected Papers

2010 ◽  
Vol 14 (6) ◽  
pp. 683-683
Author(s):  
Michael Negnevitsky
Keyword(s):  
Artificial Intelligence ◽  
International Workshop ◽  
Science And Technology ◽  
Special Issue ◽  
Practical Applications ◽  
Technical Content ◽  
The Us ◽  
Scientists And Engineers ◽  
Novel Applications ◽  
Review Criteria

Artificial Intelligence (AI) is the key to success in novel applications ranging from robotics to speech- and image-recognition systems, and from stock forecast investment systems to informal communication between robots and humans. The last two decades have seen AIfs focus shift from philosophical arguments to practical applications in both science and technology. AI researchers and educators have simultaneously recognized mutual commonality in their work and have initiated much-needed multidisciplinary approaches to AI. Guest Editor, Michael Negnevitsky, organized the 3rd International Workshop on Artificial Intelligence in Science and Technology (AISAT 2009) in Hobart, Tasmania, Australia last year, bringing together a globally diverse group of scientists and engineers to discuss issues related to practical AI applications in science and technology. The workshop featured presentations from the US, Japan, Malaysia, Spain, Kuwait, and Australia. All papers were peer reviewed by two experts for technical content, contribution, and originality to ensure high presentation quality. Fewer than 30% of submissions were accepted for presentation at the Workshop. Authors of the most outstanding presentations at AISAT 2009 were encouraged to submit manuscripts to this special issue, whose papers present innovative approaches and promising practical applications for AI. Submissions were reviewed for relevance, originality, significance, and presentation based on JACIII review criteria. We are sure that readers will find these papers both interesting and inspiring. We hope also that they will motivate researchers to expand their studies on AI applications in science and technology.

Agora

2021 ◽  
Vol 49 (4) ◽  
pp. 6-11
Author(s):  
Jonas Traub ◽  
Zoi Kaoudi ◽  
Jorge-Arnulfo Quiané-Ruiz ◽  
Volker Markl
Keyword(s):  
Artificial Intelligence ◽  
Data Management ◽  
Domain Knowledge ◽  
Data Science ◽  
Dynamic Environment ◽  
Fine Grained ◽  
Computational Resources ◽  
Diverse Data ◽  
Novel Applications ◽  
Lock In

Data science and artificial intelligence are driven by a plethora of diverse data-related assets, including datasets, data streams, algorithms, processing software, compute resources, and domain knowledge. As providing all these assets requires a huge investment, data science and artificial intelligence technologies are currently dominated by a small number of providers who can afford these investments. This leads to lock-in effects and hinders features that require a flexible exchange of assets among users. In this paper, we introduce Agora, our vision towards a unified ecosystem that brings together data, algorithms, models, and computational resources and provides them to a broad audience. Agora (i) treats assets as first-class citizens and leverages a fine-grained exchange of assets, (ii) allows for combining assets to novel applications, and (iii) flexibly executes such applications on available resources. As a result, it enables easy creation and composition of data science pipelines as well as their scalable execution. In contrast to existing data management systems, Agora operates in a heavily decentralized and dynamic environment: Data, algorithms, and even compute resources are dynamically created, modified, and removed by different stakeholders. Agora presents novel research directions for the data management community as a whole: It requires to combine our traditional expertise in scalable data processing and management with infrastructure provisioning as well as economic and application aspects of data, algorithms, and infrastructure.

scholarly journals Deep Learning: A Review

2018 ◽  
Author(s):  
Rocio Vargas ◽  
Amir Mosavi ◽  
Ramon Ruiz
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
State Of The Art ◽  
The Novel ◽  
Nonlinear Transformations ◽  
Large Databases ◽  
The Common ◽  
Novel Concept ◽  
High Level ◽  
Novel Applications

Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level in large databases. The recent advancements in deep learning architec- tures within numerous fields have already provided significant contributions in artificial intelligence. This article presents a state of the art survey on the contri- butions and the novel applications of deep learning. The following review chron- ologically presents how and in what major applications deep learning algorithms have been utilized. Furthermore, the superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applica- tions. The state of the art survey further provides a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.

Doing More With Less

2019 ◽  
pp. 1-17
Author(s):  
Carlos Hernán Fajardo-Toro ◽  
Andrés Aguilera-Castillo ◽  
Mauricio Guerrero-Cabarcas
Keyword(s):  
Artificial Intelligence ◽  
Business Models ◽  
Industrial Revolution ◽  
Industrial Robots ◽  
The Internet ◽  
Fourth Industrial Revolution ◽  
Technological Advances ◽  
Personal Robotics ◽  
Novel Applications ◽  
The Internet Of Things

Technological advances and novel applications in areas such as industrial robots (eventually personal robotics), artificial intelligence, big data, 3D printing, the internet of things, biotechnology, blockchain, and others have revived the debate on how the development and implementation of technological innovations may displace labor. These technologies are allowing the innovation of products, services, and business models at unprecedented speed, in the same way they are putting at risk both qualified and unqualified jobs and occupations. Most of the specialized literature dealing with the issue of technology and labor comes from the economics discipline, but it is pertinent to discuss how this translates into the managerial, organizational, and strategic principles framed for the fourth industrial revolution.

Advances in ultramicrotomy for physical sciences applications

1993 ◽  
Vol 51 ◽  
pp. 710-711
Author(s):  
G. McMahon ◽  
T. Malis
Keyword(s):  
Particle Surface ◽  
Zeolite Catalysts ◽  
Physical Sciences ◽  
Pull Out ◽  
Large Particles ◽  
Novel Applications ◽  
Metal Wires ◽  
Frequent Problem ◽  
Specific Orientation ◽  
Diamond Knife

As with all techniques which are relatively new and therefore underutilized, diamond knife sectioning in the physical sciences continues to see both developments of the technique and novel applications.Technique Developments Development of specific orientation/embedding procedures for small pieces of awkward shape is exemplified by the work of Bradley et al on large, rather fragile particles of nuclear waste glass. At the same time, the frequent problem of pullout with large particles can be reduced by roughening of the particle surface, and a proven methodology using a commercial coupling agent developed for glasses has been utilized with good results on large zeolite catalysts. The same principle (using acid etches) should work for ceramic fibres or metal wires which may only partially pull out but result in unacceptably thick sections. Researchers from the life sciences continue to develop aspects of embedding media which may be applicable to certain cases in the physical sciences.

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