Human-Centric AI: The Symbiosis of Human and Artificial Intelligence
Well-evidenced advances of data-driven complex machine learning approaches emerging within the so-called second wave of artificial intelligence (AI) fostered the exploration of possible AI applications in various domains and aspects of human life, practices, and society. Most of the recent success i...
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doaj-7e3c6bf86ec74db8a4c8fed4a3ae4e8a2021-03-12T00:06:01ZengMDPI AGEntropy1099-43002021-03-012333233210.3390/e23030332Human-Centric AI: The Symbiosis of Human and Artificial IntelligenceDavor Horvatić0Tomislav Lipic1Department of Physics, Faculty of Science, University of Zagreb, Bijenička Cesta 32, 10000 Zagreb, CroatiaDivision of Electronics, Ruđer Bošković Institute, Bijenička Cesta 54, 10000 Zagreb, CroatiaWell-evidenced advances of data-driven complex machine learning approaches emerging within the so-called second wave of artificial intelligence (AI) fostered the exploration of possible AI applications in various domains and aspects of human life, practices, and society. Most of the recent success in AI comes from the utilization of representation learning with end-to-end trained deep neural network models in tasks such as image, text,<br>and speech recognition or strategic board and video games. By enabling automatic feature engineering, deep learning models significantly reduce the reliance on domain-expert knowledge, outperforming traditional methods based on handcrafted feature engineering and achieving performance that equals or even supersedes humans in some respects.https://www.mdpi.com/1099-4300/23/3/332artificial intelligencedeep neural networksinterpretabilityexplainabilityfairnessaccountability |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Davor Horvatić Tomislav Lipic |
spellingShingle |
Davor Horvatić Tomislav Lipic Human-Centric AI: The Symbiosis of Human and Artificial Intelligence Entropy artificial intelligence deep neural networks interpretability explainability fairness accountability |
author_facet |
Davor Horvatić Tomislav Lipic |
author_sort |
Davor Horvatić |
title |
Human-Centric AI: The Symbiosis of Human and Artificial Intelligence |
title_short |
Human-Centric AI: The Symbiosis of Human and Artificial Intelligence |
title_full |
Human-Centric AI: The Symbiosis of Human and Artificial Intelligence |
title_fullStr |
Human-Centric AI: The Symbiosis of Human and Artificial Intelligence |
title_full_unstemmed |
Human-Centric AI: The Symbiosis of Human and Artificial Intelligence |
title_sort |
human-centric ai: the symbiosis of human and artificial intelligence |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2021-03-01 |
description |
Well-evidenced advances of data-driven complex machine learning approaches emerging within the so-called second wave of artificial intelligence (AI) fostered the exploration of possible AI applications in various domains and aspects of human life, practices, and society. Most of the recent success in AI comes from the utilization of representation learning with end-to-end trained deep neural network models in tasks such as image, text,<br>and speech recognition or strategic board and video games. By enabling automatic feature engineering, deep learning models significantly reduce the reliance on domain-expert knowledge, outperforming traditional methods based on handcrafted feature engineering and achieving performance that equals or even supersedes humans in some respects. |
topic |
artificial intelligence deep neural networks interpretability explainability fairness accountability |
url |
https://www.mdpi.com/1099-4300/23/3/332 |
work_keys_str_mv |
AT davorhorvatic humancentricaithesymbiosisofhumanandartificialintelligence AT tomislavlipic humancentricaithesymbiosisofhumanandartificialintelligence |
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