Common Misconceptions About Neural Networks as Approximators

Neural Networks & Combinatorial Optimization in Civil & Structural Engineering ◽

10.4203/ccp.16.2.1 ◽

2009 ◽

Author(s):

W.C. Carpenter ◽

J-F. Barthelemy

Keyword(s):

Neural Networks ◽

Common Misconceptions

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Common Misconceptions about Neural Networks as Approximators

Journal of Computing in Civil Engineering ◽

10.1061/(asce)0887-3801(1994)8:3(345) ◽

1994 ◽

Vol 8 (3) ◽

pp. 345-358 ◽

Cited By ~ 43

Author(s):

William C. Carpenter ◽

Jean‐Francois Barthelemy

Keyword(s):

Neural Networks ◽

Common Misconceptions

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Artificial Intelligence: A Clarification of Misconceptions, Myths and Desired Status

Frontiers in Artificial Intelligence ◽

10.3389/frai.2020.524339 ◽

2020 ◽

Vol 3 ◽

Author(s):

Frank Emmert-Streib ◽

Olli Yli-Harja ◽

Matthias Dehmer

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Deep Neural Networks ◽

Current Status ◽

The Veil ◽

Common Misconceptions

The field artificial intelligence (AI) was founded over 65 years ago. Starting with great hopes and ambitious goals the field progressed through various stages of popularity and has recently undergone a revival through the introduction of deep neural networks. Some problems of AI are that, so far, neither the “intelligence” nor the goals of AI are formally defined causing confusion when comparing AI to other fields. In this paper, we present a perspective on the desired and current status of AI in relation to machine learning and statistics and clarify common misconceptions and myths. Our discussion is intended to lift the veil of vagueness surrounding AI to reveal its true countenance.

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