Social Understanding: On Hermeneutics, Geometrical Models and Artificial Intelligence
Springer Science & Business Media, Dec 17, 2010 - Computers - 260 pages
The operation of understanding is the fundamental methodical procedure of hermeneutics and is usually seen as contradiction to scientific explanation by the usage of mathematical models. Yet understanding is the basic way in which humans organize their everyday practice, namely by understanding other people and social situations. In this book the authors demonstrate how an integration of hermeneutical understanding and scientific explanation can be done via the construction of suited geometrical models with neural networks of processes of understanding. In this sense the authors develop some kind of mathematical hermeneutics. Connecting links for the integration of the two methodical poles are the developments of particular models of Artificial Intelligence (AI), which are able to perform certain tasks of understanding.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Considerations About Formal Systematizations of Understanding
3 Mental Models the Geometry of Meaning Generation and of InformationDegrees
4 The Understanding of Learning
Understanding Humans by Artificial Systems
Other editions - View all
Social Understanding: On Hermeneutics, Geometrical Models and Artificial ...
Jürgen Klüver,Christina Klüver
No preview available - 2013
according Accordingly activation values actor Adidas AI-systems algorithms androids Artificial Intelligence artificial systems assume attractor basins of attraction behavior Calvinists cellular automata Chapter classical conditioning cluster cognitive complex computer programs concepts conditioning process connections constructed course cultural death cells defined definition Delta rule demonstrated described dynamical Eliza empirical enforcing rule evolutionary algorithms example experiments explain fact factual feed forward frequently hence hermeneutical hypothesis information degree input neurons intelligence interpretation Klüver layer learner learning processes learning rules logic MC-values meaning mental model methodical mouse namely neural networks observer operations output neurons output vector parameters particular possible probability probands problem prototype reinforcement learning respective rules of interaction self-organized learning semantical network sense similar simulation programs situation social actions social rules specific structure supervised learning task test person theory topology Turing Test understand understood usually valid weight matrix weight values