Search Results for "fundamentals-of-artificial-neural-networks-mit-press"

Fundamentals of Artificial Neural Networks

Fundamentals of Artificial Neural Networks

  • Author: Mohamad H. Hassoun
  • Publisher: MIT Press
  • ISBN: 9780262082396
  • Category: Computers
  • Page: 511
  • View: 8680
DOWNLOAD NOW »
Fundamentals of Building Energy Dynamics assesses how and why buildings use energy, and how energy use and peak demand can be reduced. It provides a basis for integrating energy efficiency and solar approaches in ways that will allow building owners and designers to balance the need to minimize initial costs, operating costs, and life-cycle costs with need to maintain reliable building operations and enhance environmental quality both inside and outside the building. Chapters trace the development of building energy systems and analyze the demand side of solar applications as a means for determining what portion of a building's energy requirements can potentially be met by solar energy.Following the introduction, the book provides an overview of energy use patterns in the aggregate U.S. building population. Chapter 3 surveys work on the energy flows in an individual building and shows how these flows interact to influence overall energy use. Chapter 4 presents the analytical methods, techniques, and tools developed to calculate and analyze energy use in buildings, while chapter 5 provides an extensive survey of the energy conservation and management strategies developed in the post-energy crisis period.The approach taken is a commonsensical one, starting with the proposition that the purpose of buildings is to house human activities, and that conservation measures that negatively affect such activities are based on false economies. The goal is to determine rational strategies for the design of new buildings, and the retrofit of existing buildings to bring them up to modern standards of energy use. The energy flows examined are both large scale (heating systems) and small scale (choices among appliances).Solar Heat Technologies: Fundamentals and Applications, Volume 4

Neuronale Netze selbst programmieren

Neuronale Netze selbst programmieren

Ein verständlicher Einstieg mit Python

  • Author: Tariq Rashid
  • Publisher: O'Reilly
  • ISBN: 3960101031
  • Category: Computers
  • Page: 232
  • View: 9286
DOWNLOAD NOW »
Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Sie sind Grundlage vieler Anwendungen im Alltag wie beispielsweise Spracherkennung, Gesichtserkennung auf Fotos oder die Umwandlung von Sprache in Text. Dennoch verstehen nur wenige, wie neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie neuronale Netze arbeiten: - Zunächst lernen Sie die mathematischen Konzepte kennen, die den neuronalen Netzen zugrunde liegen. Dafür brauchen Sie keine tieferen Mathematikkenntnisse, denn alle mathematischen Ideen werden behutsam und mit vielen Illustrationen und Beispielen erläutert. Eine Kurzeinführung in die Analysis unterstützt Sie dabei. - Dann geht es in die Praxis: Nach einer Einführung in die populäre und leicht zu lernende Programmiersprache Python bauen Sie allmählich Ihr eigenes neuronales Netz mit Python auf. Sie bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. - Im nächsten Schritt tunen Sie die Leistung Ihres neuronalen Netzes so weit, dass es eine Zahlenerkennung von 98 % erreicht – nur mit einfachen Ideen und simplem Code. Sie testen das Netz mit Ihrer eigenen Handschrift und werfen noch einen Blick in das mysteriöse Innere eines neuronalen Netzes. - Zum Schluss lassen Sie das neuronale Netz auf einem Raspberry Pi Zero laufen. Tariq Rashid erklärt diese schwierige Materie außergewöhnlich klar und verständlich, dadurch werden neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

Geophysical Applications of Artificial Neural Networks and Fuzzy Logic

Geophysical Applications of Artificial Neural Networks and Fuzzy Logic

  • Author: W. Sandham,M. Leggett
  • Publisher: Springer Science & Business Media
  • ISBN: 9781402017292
  • Category: Mathematics
  • Page: 325
  • View: 684
DOWNLOAD NOW »
The past fifteen years has witnessed an explosive growth in the fundamental research and applications of artificial neural networks (ANNs) and fuzzy logic (FL). The main impetus behind this growth has been the ability of such methods to offer solutions not amenable to conventional techniques, particularly in application domains involving pattern recognition, prediction and control. Although the origins of ANNs and FL may be traced back to the 1940s and 1960s, respectively, the most rapid progress has only been achieved in the last fifteen years. This has been due to significant theoretical advances in our understanding of ANNs and FL, complemented by major technological developments in high-speed computing. In geophysics, ANNs and FL have enjoyed significant success and are now employed routinely in the following areas (amongst others): 1. Exploration Seismology. (a) Seismic data processing (trace editing; first break picking; deconvolution and multiple suppression; wavelet estimation; velocity analysis; noise identification/reduction; statics analysis; dataset matching/prediction, attenuation), (b) AVO analysis, (c) Chimneys, (d) Compression I dimensionality reduction, (e) Shear-wave analysis, (f) Interpretation (event tracking; lithology prediction and well-log analysis; prospect appraisal; hydrocarbon prediction; inversion; reservoir characterisation; quality assessment; tomography). 2. Earthquake Seismology and Subterranean Nuclear Explosions. 3. Mineral Exploration. 4. Electromagnetic I Potential Field Exploration. (a) Electromagnetic methods, (b) Potential field methods, (c) Ground penetrating radar, (d) Remote sensing, (e) inversion.

Numerical Computer Methods

Numerical Computer Methods

  • Author: Michael L. Johnson,Ludwig Brand
  • Publisher: Gulf Professional Publishing
  • ISBN: 9780121822224
  • Category: Medical
  • Page: 471
  • View: 2323
DOWNLOAD NOW »
The critically acclaimed laboratory standard for more than forty years, Methods in Enzymology is one of the most highly respected publications in the field of biochemistry. Since 1955, each volume has been eagerly awaited, frequently consulted, and praised by researchers and reviewers alike. Now with more than 300 volumes (all of them still in print), the series contains much material still relevant today--truly an essential publication for researchers in all fields of life sciences.

Self-organizing Map Formation

Self-organizing Map Formation

Foundations of Neural Computation

  • Author: Klaus Obermayer,Terrence Joseph Sejnowski,Howard Hughes Medical Institute Computational Neurobiology Laboratory Terrence J Sejnowski,Tomaso A Poggio
  • Publisher: MIT Press
  • ISBN: 9780262650601
  • Category: Computers
  • Page: 440
  • View: 8910
DOWNLOAD NOW »
This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher. The articles are drawn from the journal Neural Computation.The book consists of five sections. The first section looks at attempts to model the organization of cortical maps and at the theory and applications of the related artificial neural network algorithms. The second section analyzes topographic maps and their formation via objective functions. The third section discusses cortical maps of stimulus features. The fourth section discusses self-organizing maps for unsupervised data analysis. The fifth section discusses extensions of self-organizing maps, including two surprising applications of mapping algorithms to standard computer science problems: combinatorial optimization and sorting. Contributors J. J. Atick, H. G. Barrow, H. U. Bauer, C. M. Bishop, H. J. Bray, J. Bruske, J. M. L. Budd, M. Budinich, V. Cherkassky, J. Cowan, R. Durbin, E. Erwin, G. J. Goodhill, T. Graepel, D. Grier, S. Kaski, T. Kohonen, H. Lappalainen, Z. Li, J. Lin, R. Linsker, S. P. Luttrell, D. J. C. MacKay, K. D. Miller, G. Mitchison, F. Mulier, K. Obermayer, C. Piepenbrock, H. Ritter, K. Schulten, T. J. Sejnowski, S. Smirnakis, G. Sommer, M. Svensen, R. Szeliski, A. Utsugi, C. K. I. Williams, L. Wiskott, L. Xu, A. Yuille, J. Zhang

Theorie der neuronalen Netze

Theorie der neuronalen Netze

Eine systematische Einführung

  • Author: Raul Rojas
  • Publisher: Springer-Verlag
  • ISBN: 3642612318
  • Category: Computers
  • Page: 446
  • View: 9404
DOWNLOAD NOW »
Neuronale Netze sind ein Berechenbarkeitsparadigma, das in der Informatik zunehmende Beachtung findet. In diesem Buch werden theoretische Ansätze und Modelle, die in der Literatur verstreut sind, zu einer modellübergreifenden Theorie der künstlichen neuronalen Netze zusammengefügt. Mit ständigem Blick auf die Biologie wird - ausgehend von einfachsten Netzen - gezeigt, wie sich die Eigenschaften der Modelle verändern, wenn allgemeinere Berechnungselemente und Netztopologien eingeführt werden. Jedes Kapitel enthält Beispiele und ist ausführlich illustriert und durch bibliographische Anmerkungen abgerundet. Das Buch richtet sich an Leser, die sich einen Überblick verschaffen oder vorhandene Kenntnisse vertiefen wollen. Es ist als Grundlage für Neuroinformatikvorlesungen an deutschsprachigen Universitäten geeignet.

An Introduction to Neural Networks

An Introduction to Neural Networks

  • Author: Kevin Gurney
  • Publisher: CRC Press
  • ISBN: 1482286998
  • Category: Computers
  • Page: 234
  • View: 5761
DOWNLOAD NOW »
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.

Grundlagen zur Neuroinformatik und Neurobiologie

Grundlagen zur Neuroinformatik und Neurobiologie

The Computational Brain in deutscher Sprache

  • Author: Patricia S. Churchland,Terrence J. Sejnowski
  • Publisher: Springer-Verlag
  • ISBN: 3322868214
  • Category: Technology & Engineering
  • Page: 702
  • View: 2074
DOWNLOAD NOW »
The Computational Brain, das außergewöhnliche Buch über vergleichende Forschung in den Bereichen von menschlichem Gehirn und neuesten Möglichkeiten der Computertechnologie, liegt hiermit erstmals in deutscher Sprache vor. Geschrieben von einem führenden Forscherteam in den USA, ist es eine Fundgrube für alle, die wissen wollen, was der Stand der Wissenschaft auf diesem Gebiet ist. Die Autoren führen die Bereiche der Neuroinformatik und Neurobiologie mit gut ausgesuchten Beispielen und der gebotenen Hintergrundinformation gekonnt zusammen. Das Buch wird somit nicht nur dem Fachwissenschaftler sondern auch dem interdisziplinären Interesse des Informatikers und des Biologen auf eine hervorragende Weise gerecht. Übersetzt wurde das Buch von Prof. Dr. Steffen Hölldobler und Dipl.-Biol. Claudia Hölldobler, einem Informatiker und einer Biologin. Rezension in Spektrum der Wissenschaft nr. 10, S. 122 f. im Oktober 1997 (...) Die 1992 erschienene amerikanische Originalausgabe des vorliegenden Werkes ist so erfolgreich, daß man bereits von einem Klassiker reden kann. (...) (...) ....ist das Buch sehr zu empfehlen. In Verbindung von Neurobiologie und Neuroinformatik konkurrenzlos, vermittelt es einiges von der Faszination theoretischer Hirnforschung, die auch in Deutschland zunehmend mehr Wissenschaftler in ihren Bann schlägt. Rezension erschienen in: Computer Spektrum 3/1997, S. 2 (...)Das Buch wird somit nicht nur dem Fachwissenschaftler, sondern auch den interdisziplinären Interesse des Informatikers und des Biologen auf eine hervorragende Weise gerecht(...)

The Cumulative Book Index

The Cumulative Book Index

  • Author: N.A
  • Publisher: N.A
  • ISBN: N.A
  • Category: American literature
  • Page: N.A
  • View: 5295
DOWNLOAD NOW »

Proceedings of the Symposium on Low Temperature Electronics and High Temperature Superconductivity

Proceedings of the Symposium on Low Temperature Electronics and High Temperature Superconductivity

  • Author: Cor L. Claeys
  • Publisher: The Electrochemical Society
  • ISBN: 9781566771030
  • Category: Cryoelectronics
  • Page: 446
  • View: 421
DOWNLOAD NOW »

Das Geheimnis des menschlichen Denkens

Das Geheimnis des menschlichen Denkens

Einblicke in das Reverse Engineering des Gehirns

  • Author: Ray Kurzweil
  • Publisher: Lola Books
  • ISBN: 394420316X
  • Category: Science
  • Page: 352
  • View: 2415
DOWNLOAD NOW »
Der Wettlauf um das Gehirn hat begonnen. Sowohl die EU als auch die USA haben gewaltige Forschungsprojekte ins Leben gerufen um das Geheimnis des menschlichen Denkens zu entschlüsseln. 2023 soll es dann soweit sein: Das menschliche Gehirn kann vollständig simuliert werden. In "Das Geheimnis des menschlichen Denkens" gewährt Googles Chefingenieur Ray Kurzweil einen spannenden Einblick in das Reverse Engineering des Gehirns. Er legt dar, wie mithilfe der Mustererkennungstheorie des Geistes der ungeheuren Komplexität des Gehirns beizukommen ist und wirft einen ebenso präzisen wie überraschenden Blick auf die am Horizont sich bereits abzeichnende Zukunft. Ist das menschliche Gehirn erst einmal simuliert, wird künstliche Intelligenz die Fähigkeiten des Menschen schon bald übertreffen. Ein Ereignis, das Kurzweil aufgrund der bereits in "Menschheit 2.0" entworfenen exponentiellen Wachstumskurve der Informationstechnologien bereits für das Jahr 2029 prognostiziert. Aber was dann? Kurzweil ist zuversichtlich, dass die Vorteile künstlicher Intelligenz mögliche Bedrohungsszenarien überwiegen und sie uns entscheidend dabei hilft, uns weiterzuentwickeln und die Herausforderungen der Zukunft zu meistern.

Talking Nets

Talking Nets

An Oral History of Neural Networks

  • Author: James A. Anderson,Edward Rosenfeld
  • Publisher: MIT Press
  • ISBN: 9780262511117
  • Category: Computers
  • Page: 448
  • View: 7850
DOWNLOAD NOW »
Since World War II, a group of scientists has been attempting to understand the human nervous system and to build computer systems that emulate the brain's abilities. Many of the early workers in this field of neural networks came from cybernetics; others came from neuroscience, physics, electrical engineering, mathematics, psychology, even economics. In this collection of interviews, those who helped to shape the field share their childhood memories, their influences, how they became interested in neural networks, and what they see as its future. The subjects tell stories that have been told, referred to, whispered about, and imagined throughout the history of the field. Together, the interviews form a Rashomon-like web of reality. Some of the mythic people responsible for the foundations of modern brain theory and cybernetics, such as Norbert Wiener, Warren McCulloch, and Frank Rosenblatt, appear prominently in the recollections. The interviewees agree about some things and disagree about more. Together, they tell the story of how science is actually done, including the false starts, and the Darwinian struggle for jobs, resources, and reputation. Although some of the interviews contain technical material, there is no actual mathematics in the book. Contributors: James A. Anderson, Michael Arbib, Gail Carpenter, Leon Cooper, Jack Cowan, Walter Freeman, Stephen Grossberg, Robert Hecht-Neilsen, Geoffrey Hinton, Teuvo Kohonen, Bart Kosko, Jerome Lettvin, Carver Mead, David Rumelhart, Terry Sejnowski, Paul Werbos, Bernard Widrow.

Advances in Kernel Methods

Advances in Kernel Methods

Support Vector Learning

  • Author: Rosanna Soentpiet
  • Publisher: MIT Press
  • ISBN: 9780262194167
  • Category: Computers
  • Page: 376
  • View: 8546
DOWNLOAD NOW »
A young girl hears the story of her great-great-great-great- grandfather and his brother who came to the United States to make a better life for themselves helping to build the transcontinental railroad.

Artificial Neural Networks - ICANN 2010

Artificial Neural Networks - ICANN 2010

20th International Conference, Thessaloniki, Greece, Septmeber 15-18, 2020, Proceedings

  • Author: Konstantinos Diamantaras,Wlodek Duch,Lazaros S. Iliadis
  • Publisher: Springer
  • ISBN: 3642158226
  • Category: Computers
  • Page: 543
  • View: 2811
DOWNLOAD NOW »
th This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15–18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the ?eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti?cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the signal being communicated. ANNs have the ability “to learn” by example (a large volume of cases) through several iterations without requiring a priori ?xed knowledge of the relationships between process parameters.

Neural Networks for Applied Sciences and Engineering

Neural Networks for Applied Sciences and Engineering

From Fundamentals to Complex Pattern Recognition

  • Author: Sandhya Samarasinghe
  • Publisher: CRC Press
  • ISBN: 9781420013061
  • Category: Computers
  • Page: 570
  • View: 2346
DOWNLOAD NOW »
In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by extensive step-by-step coverage on linear networks, as well as, multi-layer perceptron for nonlinear prediction and classification explaining all stages of processing and model development illustrated through practical examples and case studies. Later chapters present an extensive coverage on Self Organizing Maps for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for scientific data analysis. With an easy to understand format using extensive graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics. Features § Explains neural networks in a multi-disciplinary context § Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding ? Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting § Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction, input selection, model development and validation, model uncertainty assessment, sensitivity analyses on inputs, errors and model parameters Sandhya Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, USA. Her neural networks research focuses on theoretical understanding and advancements as well as practical implementations.

Artificial Neural Networks as Models of Neural Information Processing

Artificial Neural Networks as Models of Neural Information Processing

  • Author: Marcel van Gerven,Sander Bohte
  • Publisher: Frontiers Media SA
  • ISBN: 2889454010
  • Category:
  • Page: N.A
  • View: 8607
DOWNLOAD NOW »
Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.

Modelling Perception with Artificial Neural Networks

Modelling Perception with Artificial Neural Networks

  • Author: Colin R. Tosh,Graeme D. Ruxton
  • Publisher: Cambridge University Press
  • ISBN: 9781139489058
  • Category: Science
  • Page: N.A
  • View: 1682
DOWNLOAD NOW »
Studies of the evolution of animal signals and sensory behaviour have more recently shifted from considering 'extrinsic' (environmental) determinants to 'intrinsic' (physiological) ones. The drive behind this change has been the increasing availability of neural network models. With contributions from experts in the field, this book provides a complete survey of artificial neural networks. The book opens with two broad, introductory level reviews on the themes of the book: neural networks as tools to explore the nature of perceptual mechanisms, and neural networks as models of perception in ecology and evolutionary biology. Later chapters expand on these themes and address important methodological issues when applying artificial neural networks to study perception. The final chapter provides perspective by introducing a neural processing system in a real animal. The book provides the foundations for implementing artificial neural networks, for those new to the field, along with identifying potential research areas for specialists.

Graphical Models

Graphical Models

Foundations of Neural Computation

  • Author: Michael Irwin Jordan,Terrence Joseph Sejnowski,Tomaso A. Poggio
  • Publisher: MIT Press
  • ISBN: 9780262600422
  • Category: Computers
  • Page: 421
  • View: 2943
DOWNLOAD NOW »
This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithm and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research. Graphical models use graphs to represent and manipulate joint probability distributions. They have their roots in artificial intelligence, statistics, and neural networks. The clean mathematical formalism of the graphical models framework makes it possible to understand a wide variety of network-based approaches to computation, and in particular to understand many neural network algorithms and architectures as instances of a broader probabilistic methodology. It also makes it possible to identify novel features of neural network algorithms and architectures and to extend them to more general graphical models.This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research. Contributors H. Attias, C. M. Bishop, B. J. Frey, Z. Ghahramani, D. Heckerman, G. E. Hinton, R. Hofmann, R. A. Jacobs, Michael I. Jordan, H. J. Kappen, A. Krogh, R. Neal, S. K. Riis, F. B. Rodríguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss

Neuronale Netze

Neuronale Netze

Eine Einführung in die Neuroinformatik

  • Author: Rüdiger Brause
  • Publisher: Springer-Verlag
  • ISBN: 3322921182
  • Category: Technology & Engineering
  • Page: 293
  • View: 3882
DOWNLOAD NOW »
Vierzig Jahre, nachdem John von Neumann sein Konzept eines rechnenden, programmgesteuerten Automaten entworfen hat, setzt sich nun in der Informatik die Erkenntnis durch, daß sequentiell arbeitende Rechner für manche Probleme zu langsam arbeiten. Mit vielen, parallel arbeitenden Prozessoren versucht man heutzutage in Multiprozessoranlagen, den "von Neumann-Flaschenhals" zu umgehen. Dabei ergeben sich eine Menge neuer Probleme: Die Aktivität der Prozessoren muß synchronisiert werden, die Daten müssen effektiv verteilt werden und "Knoten" im Datenfluß (hot spots) zwischen den Prozessoren und dem Speicher müssen vermieden werden. Dazu werden Mechanismen benötigt, um auftretende Defekte in ihren Auswirkungen zu erfassen und zu kompensieren, das Gesamtsystem zu rekonfigurieren und alle Systemdaten zu aktualisieren. Bedenken wir noch zusätzlich die Schwierigkeiten, die mit einer parallelen Programmierung der eigentlichen Probleme verbunden sind, so können wir uns nur wundem, wieso wir Menschen "im Handumdrehen" und "ganz natürlich" Leistungen erbringen können, die mit den heutigen Rechnern bisher nicht nachvollziehbar waren. Betrachten wir beispielsweise die Probleme der "künstlichen Intelligenz", besser "wissensbasierte Datenverarbeitung" genannt, so hinken die heutigen Systeme zum Sehen. Hören und Bewegen hoffnungslos der menschlichen Realität hinterher. Weder in der Bildverarbeitung und -erkennung, noch in der Spracherkennung oder in der Robotersteuerung reichen die Systeme in Punkto Schnelligkeit (real-time), Effektivität und Flexibilität (Lernen) oder Fehlertoleranz an die menschlichen Leistungen heran.