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How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another. Though formalism is an essential tool for art historians, much recent art history has focused on the social and political aspects of art. But now art historians are adopting machine learning methods to develop new ways to analyze the purely visual in datasets of art images. Amanda Wasielewski uses the term “computational formalism” to describe this use of machine learning and computer vision technique in art historical research. At the same time that art historians are analyzing art images in new ways, computer scientists are using art images for experiments in machine learning and computer vision. Their research, says Wasielewski, would be greatly enriched by the inclusion of humanistic issues. The main purpose in applying computational techniques such as machine learning to art datasets is to automate the process of categorization using metrics such as style, a historically fraught concept in art history. After examining a fifteen-year trajectory in image categorization and art dataset creation in the fields of machine learning and computer vision, Wasielewski considers deep learning techniques that both create and detect forgeries and fakes in art. She investigates examples of art historical analysis in the fields of computer and information sciences, placing this research in the context of art historiography. She also raises questions as which artworks are chosen for digitization, and of those artworks that are born digital, which works gain acceptance into the canon of high art.
Although computation and the science of physical systems would appear to be unrelated, there are a number of ways in which computational and physical concepts can be brought together in ways that illuminate both. This volume examines fundamental questions which connect scholars from both disciplines: is the universe a computer? Can a universal computing machine simulate every physical process? What is the source of the computational power of quantum computers? Are computational approaches to solving physical problems and paradoxes always fruitful? Contributors from multiple perspectives reflecting the diversity of thought regarding these interconnections address many of the most important developments and debates within this exciting area of research. Both a reference to the state of the art and a valuable and accessible entry to interdisciplinary work, the volume will interest researchers and students working in physics, computer science, and philosophy of science and mathematics.
Anderson and Piccinini offer the most systematic, rigorous, and comprehensive account of computational implementation to date. Their robust mapping account holds that the key for establishing that a computation is physically implemented is that the physical states bear neither more nor less information than the computational states they map onto.
This volume collects together peer reviewed versions of most of the papers presented at the Ninth Neural Computation and Psychology Workshop (NCPW9), held in 2004 at the University of Plymouth (England). The conference invited submissions on neural computation models of all cognitive and psychological processes. The special theme of this year's workshop was “Modeling of Language, Cognition and Action. This topic had the aim to extend the conference appeal from the connectionist psychology community to leaders in neuroscience, robotics and cognitive systems design.The chapters cover the breadth of research in neural computation and psychology, with numerous papers that focus on language modeling, this year's special theme. The book includes chapters from internationally renowned researchers in the various fields of cognitive psychology (such as Art Glenberg and Jonathan Evans) as well as computer science and robotics (such as Stefan Wermter & Stefano Nolfi).The proceedings have been selected for coverage in:• Neuroscience Citation Index®• Index to Scientific & Technical Proceedings® (ISTP® / ISI Proceedings)• Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings)• Index to Social Sciences & Humanities Proceedings® (ISSHP® / ISI Proceedings)• Index to Social Sciences & Humanities Proceedings (ISSHP CDROM version / ISI Proceedings)• CC Proceedings — Engineering & Physical Sciences• CC Proceedings — Biomedical, Biological & Agricultural Sciences
This title presents the most comprehensive existing "case study" of how the effects of damage in connectionist models can replicate the patterns of cognitive impairments that can arise in humans as a result of brain damage.
With the increasing complexity of software systems and their widespread growth into many aspects of our lives, the need to search for new models, paradigms, and ultimately, technologies, to manage this problem is evident. The way nature solves various problems through processes evolving during billions of years was always an inspiration to many computational paradigms; on the other hand, the complexity of the problems posed by the investigation of biological systems challenged the research of new tractable models. Molecular Computational Models: Unconventional Approaches is looking into new computational paradigms from both a theoretical perspective which offers a solid foundation of the models developed, as well as from a modeling angle, in order to reveal their effectiveness in modeling and simulating, especially biological systems. Tools and programming concepts and implementation issues are also discussed in the context of some experiments and comparative studies.
With the increasing complexity of software systems and their widespread growth into many aspects of our lives, the need to search for new models, paradigms, and ultimately, technologies, to manage this problem is evident. The way nature solves various problems through processes evolving during billions of years was always an inspiration to many computational paradigms; on the other hand, the complexity of the problems posed by the investigation of biological systems challenged the research of new tractable models. Molecular Computational Models: Unconventional Approaches is looking into new computational paradigms from both a theoretical perspective which offers a solid foundation of the models developed, as well as from a modeling angle, in order to reveal their effectiveness in modeling and simulating, especially biological systems. Tools and programming concepts and implementation issues are also discussed in the context of some experiments and comparative studies.
Multilingual communication within the world community is important for economic, political, and cultural interactions. In a global environment where other languages are increasing in importance in addition to recognized intemational standards (i. e., English and French), language learning is becoming more important for improved international relations. At the same time, recent advances in instructional technology make the promise of building intelligent tutoring systems in advanced technology laboratories to teach these language skills a reality in the near future. These tutoring systems, therefore, may help us foster improved methods for acquiring languages. As active language learners and instructional technology researchers, we felt an international meeting with similar individuals was needed to discuss how such advanced tutoring systems are to be designed and implemented. We held such a meeting, the results of which are presented in this volume. The purpose of this Advanced Workshop, sponsored by the NATO Scientific Affairs Division, was to bring together a multidisciplinary group of researchers who were active in the development of intelligent tutoring systems for foreign language learning. Participants came from computer science, computational linguistics, psychology, and foreign language learning. Washington, D.C. was selected for the Workshop site since it is Merryanna's home city, the capitol of the United States, and an international, multilingual community in its own right. Masoud agreed to the location (with a promise to be shown the White House!) and graciously volunteered to coordinate activities from the European side.
This book addresses a part of a problem. The problem is to determine the architecture of cognition, that is, the basic structures and mechanisms underlying cognitive processing. This is a multidimensional problem insofar as there appear to be many distinct types of mechanisms that interact in diverse ways during cognitive processing. Thus, we have memory, attention, learning, sensation, perception, and who knows what else, interacting to produce behavior. As a case in point, consider a bit of linguistic behavior. To tell a friend that I think Greg won a stunning victory, I must evidently rely on various bits of information stored in my memory, including who my friends are, who Greg is, what he won, and what natural languages I share with my friend. I must sense and perceive that my friend is within hearing distance, how loud I need to speak, how loud I am speaking, and whether my friend is paying attention. I must avail myself of what I know about the language I share with my friend, along with innumerable principles about human "folk psychology. " This book does not address the full range of contemporary theorizing about cognitive architecture, but only a part. It addresses theories of cognitive architecture that hypothesize that there exist cognitive representations, then begins to explore the possible structure of these representations. One of the leading hypotheses concerning the structure of cognitive representations is that it is akin to that found in symbolic logic.
Gualtiero Piccinini articulates and defends a mechanistic account of concrete, or physical, computation. A physical system is a computing system just in case it is a mechanism one of whose functions is to manipulate vehicles based solely on differences between different portions of the vehicles according to a rule defined over the vehicles. The Nature of Computation discusses previous accounts of computation and argues that the mechanistic account is better. Many kinds of computation are explicated, such as digital vs. analog, serial vs. parallel, neural network computation, program-controlled computation, and more. Piccinini argues that computation does not entail representation or information processing although information processing entails computation. Pancomputationalism, according to which every physical system is computational, is rejected. A modest version of the physical Church-Turing thesis, according to which any function that is physically computable is computable by Turing machines, is defended.