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PPT
PPT

... Why do we need another paradigm than symbolic AI for building “intelligent” machines? • Symbolic AI is well-suited for representing explicit knowledge that can be appropriately formalized. • However, learning in biological systems is mostly implicit – it is an adaptation process based on uncertain i ...
Matlin, Cognition, 7e, Chapter 11: Problem Solving and Creativity
Matlin, Cognition, 7e, Chapter 11: Problem Solving and Creativity

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Artificial Neural Networks-A Study

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Hybrid Analogies in Conceptual Innovation in Science
Hybrid Analogies in Conceptual Innovation in Science

... in a model-system: an in vitro network of cultured neurons locally referred to as “the dish.” Building this in vitro model-system involves extracting neurons from embryonic rats, dissociating them (breaking the connections between neurons) and plating them on a dish with embedded electrodes known a ...
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... features of biological brains can provide one basis for benchmarking the performance of artificial computational systems along some dimension of interest like power consumption or scalability. Comparing the computational performance of the simulating system in a large-scale neural simulation to that ...
A Cognitive Computation Fallacy?
A Cognitive Computation Fallacy?

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Media Release
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Computational rationality: A converging paradigm
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... networks, broadly referred to as probabilistic graphical models (PGMs) (1, 2). Belief updating procedures were developed that use parallel and distributed computation to update constellations of random variables in the networks. The study of PGMs has developed in numerous directions since these init ...
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Enriching the Blend
Enriching the Blend

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Enriching the Blend: Creative Extensions to Conceptual Blending in

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A Human-Centered Goal for Artificial Intelligence
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Computational Narrative Intelligence: A Human
Computational Narrative Intelligence: A Human

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The Role of analogy in cognitive science
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... Einstein expanded on this theme in a letter to fellow mathematician Jacques Hadamard, writing that "[t]he words of the language, as they are written or spoken, do not seem to play any role in my mechanism of thought. The psychical entities which seem to serve as elements in thought are certain signs ...
Neural Network
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... have a highly interconnected set of 1011 neurons to facilitate your reading, breathing, motion and thinking. In the artificial neural network, the neurons are not biological. They are extremely simple abstractions of biological neurons, realized as elements in a program or perhaps as circuits made o ...
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CREATIVE WORKSHOP OVERVIEW
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... Crazy, foolish ideas all welcome. No judgement, criteria, no negativity. Supportive attitude. Build on one another’s ideas. Principle: Initial ideas will be routine, superficial, obvious. It’s only after working hard at this that the deep, insightful, fresh ideas come. ...
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Creativity as a Virtue of Character
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... In this light, consider someone whose governing motivation in making music, say, is just to gain social credence. Whatever else is true, social status is hardly something that is internal to the values of art, though of course art is often used to achieve or bolster it. Imagine, in this case, that t ...
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Page | 1 Mechanistic and computational explanations

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Mathematical model
Mathematical model

... PEs simulate the function of the neurons in natural networks, while the interconnections between them mimic the functions of dendrites and axons [Graupe , 2007]. ANNs are computational networks which attempt to simulate the networks of the nerve cell (neurons) of the biological (human or animal) cen ...
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Computational creativity

Computational creativity (also known as artificial creativity, mechanical creativity or creative computation) is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts.The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends: To construct a program or computer capable of human-level creativity. To better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans. To design programs that can enhance human creativity without necessarily being creative themselves.The field of computational creativity concerns itself with theoretical and practical issues in the study of creativity. Theoretical work on the nature and proper definition of creativity is performed in parallel with practical work on the implementation of systems that exhibit creativity, with one strand of work informing the other.
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