The Art of Artificial Intelligence. 1. Themes and Case Studies of Knowledge Engineering

1977 ◽  
Author(s):  
Edward A. Feigenbaum
Keyword(s):  
Artificial Intelligence ◽  
Case Studies ◽  
Knowledge Engineering

Humanism and Music

2020 ◽  
Author(s):  
Christopher M. Driscoll
Keyword(s):  
Artificial Intelligence ◽  
Popular Culture ◽  
Popular Music ◽  
Case Studies ◽  
Contemporary Music ◽  
Theoretical Relationship ◽  
Rock Band ◽  
Historical Developments ◽  
The Relationship

This chapter explores the relationship between humanism and music, giving attention to important theoretical and historical developments, before focusing on four brief case studies rooted in popular culture. The first turns to rock band Modest Mouse as an example of music as a space of humanist expression. Next, the chapter explores Austin-based Rock band Quiet Company and Westcoast rapper Ras Kass and their use of music to critique religion. Last, the chapter discusses contemporary popular music created by artificial intelligence and considers what non-human production of music suggests about the category of the human and, resultantly, humanism. These case studies give attention to the historical and theoretical relationship between humanism and music, and they offer examples of that relationship as it plays out in contemporary music.

scholarly journals The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 133
Author(s):  
Jérémie Sublime
Keyword(s):  
Artificial Intelligence ◽  
Case Studies ◽  
Damage Assessment ◽  
Structural Damage ◽  
North East ◽  
2011 Tohoku Tsunami ◽  
Artificial Intelligence Methods ◽  
The Future ◽  
Tohoku Tsunami

The Tohoku tsunami was a devastating event that struck North-East Japan in 2011 and remained in the memory of people worldwide. The amount of devastation was so great that it took years to achieve a proper assessment of the economical and structural damage, with the consequences still being felt today. However, this tsunami was also one of the first observed from the sky by modern satellites and aircrafts, thus providing a unique opportunity to exploit these data and train artificial intelligence methods that could help to better handle the aftermath of similar disasters in the future. This paper provides a review of how artificial intelligence methods applied to case studies about the Tohoku tsunami have evolved since 2011. We focus on more than 15 studies that are compared and evaluated in terms of the data they require, the methods used, their degree of automation, their metric performances, and their strengths and weaknesses.

Ontologies and E-Learning

2005 ◽  
pp. 345-356
Author(s):  
Matteo Cristani
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Natural Language Processing ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Information Integration ◽  
Knowledge Engineering ◽  
Formal Ontologies ◽  
E Learning ◽  
Intelligent Information

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.

Ontologies and E-Learning

2011 ◽  
pp. 322-331 ◽  
Author(s):  
Matteo Cristani
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Natural Language Processing ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Information Integration ◽  
Knowledge Engineering ◽  
Formal Ontologies ◽  
E Learning ◽  
Intelligent Information

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.

Engineering AI Systems

2021 ◽  
pp. 1-19 ◽  
Author(s):  
Jan Bosch ◽  
Helena Holmström Olsson ◽  
Ivica Crnkovic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Case Studies ◽  
Research Agenda ◽  
Production Quality ◽  
Research Community ◽  
Design Methods ◽  
Engineering Approach ◽  
Quality Design

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry. However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this chapter, the authors provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that they have studied. The main contribution of the chapter is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large.

From manual to artificial intelligence fitting: Two cochlear implant case studies

2019 ◽  
Vol 21 (5) ◽  
pp. 299-305
Author(s):  
Justine Wathour ◽  
Paul J. Govaerts ◽  
Naïma Deggouj
Keyword(s):  
Artificial Intelligence ◽  
Cochlear Implant ◽  
Case Studies
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