The history of cognitive science and artificial intelligence

2008 ◽  
Vol 36 (3-4) ◽  
pp. 264-265
Author(s):  
Yves Laberge
2021 ◽  
Vol 5 (5) ◽  
pp. 23
Author(s):  
Robert Rowe

The history of algorithmic composition using a digital computer has undergone many representations—data structures that encode some aspects of the outside world, or processes and entities within the program itself. Parallel histories in cognitive science and artificial intelligence have (of necessity) confronted their own notions of representations, including the ecological perception view of J.J. Gibson, who claims that mental representations are redundant to the affordances apparent in the world, its objects, and their relations. This review tracks these parallel histories and how the orientations and designs of multimodal interactive systems give rise to their own affordances: the representations and models used expose parameters and controls to a creator that determine how a system can be used and, thus, what it can mean.


2005 ◽  
Vol 60 (3) ◽  
pp. 425-427
Author(s):  
Csaba Pléh

Ádám György: A rejtozködo elme. Egy fiziológus széljegyzetei Carpendale, J. I. M. és Müller, U. (eds): Social interaction and the development of knowledge Cloninger, R. C.: Feeling good. The science of well being Dunbar, Robin, Barrett, Louise, Lycett, John: Evolutionary psychology Dunbar, Robin: The human story. A new history of makind's evolution Geary, D. C.: The origin of mind. Evolution of brain, cognition and general intelligence Gedeon Péter, Pál Eszter, Sárkány Mihály, Somlai Péter: Az evolúció elméletei és metaforái a társadalomtudományokban Harré, Rom: Cognitive science: A philosophical introduction Horváth György: Pedagógiai pszichológia Marcus, G.: The birth of the mind. How a tiny number of genes creates the complexities of human thought Solso, R. D.: The psychology of art and the evolution of the conscious brain Wray, A. (ed.): The transition to language


Author(s):  
Stephen R. Barley

The four chapters of this book summarize the results of thirty-five years dedicated to studying how technologies change work and organizations. The first chapter places current developments in artificial intelligence into the historical context of previous technological revolutions by drawing on William Faunce’s argument that the history of technology is one of progressive automation of the four components of any production system: energy, transformation, and transfer and control technologies. The second chapter lays out a role-based theory of how technologies occasion changes in organizations. The third chapter tackles the issue of how to conceptualize a more thorough approach to assessing how intelligent technologies, such as artificial intelligence, can shape work and employment. The fourth chapter discusses what has been learned over the years about the fears that arise when one sets out to study technical work and technical workers and methods for controlling those fears.


This book is the first to examine the history of imaginative thinking about intelligent machines. As real artificial intelligence (AI) begins to touch on all aspects of our lives, this long narrative history shapes how the technology is developed, deployed, and regulated. It is therefore a crucial social and ethical issue. Part I of this book provides a historical overview from ancient Greece to the start of modernity. These chapters explore the revealing prehistory of key concerns of contemporary AI discourse, from the nature of mind and creativity to issues of power and rights, from the tension between fascination and ambivalence to investigations into artificial voices and technophobia. Part II focuses on the twentieth and twenty-first centuries in which a greater density of narratives emerged alongside rapid developments in AI technology. These chapters reveal not only how AI narratives have consistently been entangled with the emergence of real robotics and AI, but also how they offer a rich source of insight into how we might live with these revolutionary machines. Through their close textual engagements, these chapters explore the relationship between imaginative narratives and contemporary debates about AI’s social, ethical, and philosophical consequences, including questions of dehumanization, automation, anthropomorphization, cybernetics, cyberpunk, immortality, slavery, and governance. The contributions, from leading humanities and social science scholars, show that narratives about AI offer a crucial epistemic site for exploring contemporary debates about these powerful new technologies.


2018 ◽  
Vol 10 (9) ◽  
pp. 3245 ◽  
Author(s):  
Tianxing Wu ◽  
Guilin Qi ◽  
Cheng Li ◽  
Meng Wang

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the “One Belt One Road (OBOR)” initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.


2021 ◽  
Vol 12 (1) ◽  
pp. 101-112
Author(s):  
Kishore Sugali ◽  
Chris Sprunger ◽  
Venkata N Inukollu

The history of Artificial Intelligence and Machine Learning dates back to 1950’s. In recent years, there has been an increase in popularity for applications that implement AI and ML technology. As with traditional development, software testing is a critical component of an efficient AI/ML application. However, the approach to development methodology used in AI/ML varies significantly from traditional development. Owing to these variations, numerous software testing challenges occur. This paper aims to recognize and to explain some of the biggest challenges that software testers face in dealing with AI/ML applications. For future research, this study has key implications. Each of the challenges outlined in this paper is ideal for further investigation and has great potential to shed light on the way to more productive software testing strategies and methodologies that can be applied to AI/ML applications.


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