AIA: Artificial intelligence for art

There are limits to state-of-the-art AI that separate it from human-like intelligence. Today’s AI algorithms are limited in how much previous knowledge they are able to keep through each new training phase and how much they can reuse. There is domain called AGI where will be possible to find solutions for this problems. Artificial general intelligence (AGI) describes research that aims to create machines capable of general intelligent action. "General" means that one AI program realises number of different tasks and the same code can be used in many applications.

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Robert B. Lisek

Conference

Publication date: July 2018

Publication date (Print): July 2018

Pages: 26-31

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[0001]Institute for Research in Science and Art

Enschede, Netherlands

Article

DOI: 10.14236/ewic/EVA2018.5

SO-VID: 3575b5f9-0361-48b7-95d0-77692b84e28c

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Electronic Visualisation and the Arts

Conference acronym: EVA

Conference number:

Conference location: London, UK

Conference date: 9 - 13 July 2018

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Electronic Visualisation and the Arts

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVA2018.5

Self URI (journal page): https://ewic.bcs.org/

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A. KrizhevskyI. SutskeverG. Hinton 2012 Image net classification with deep convolutional neural networks In Advances in Neural Information Processing Systems 25 (NIPS)1106 1114
J. LiW. MonroeA. RitterM. GalleyJ. GaoD. Jurafsky 2016 Deep Reinforcement Learning for Dialogue Generation arXiv preprint arXiv:1606.01541
Maas H. L van derP. F. M. J. VerschureP. C. M. Molenaar 1990 A Note on Chaotic Behavior in Simple Neural Networks Neural Networks, 3:119 122
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R. Schechner 1998 Performance Theory Routledge New York
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T. WenM. Gasic 2015 Stochastic language generation in dialogue using recurrent neural networks In Proceedings of SIG-dial for Computational Linguistics

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[1] J. Bates 1992 Virtual Reality, Art, and Entertainment Presence Teleoperators Virtual Environ, 1:133 138

[2] G. E. DahlD. YuL. DengA. Acero 2012 Context-dependent pre-trained deep neural networks for large- vocabulary speech recognition IEEE Transactions on Audio, Speech, and Language Processing 20(1):30 42

[3] A. DasS. KotturJ. MouraS. Lee 2017 Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning arXiv preprint arXiv:1703.06585

[4] G. HintonL. DengD. YuG. DahlA. MohamedN. JaitlyA. SeniorV. VanhouckeP. NguyenT. Sainath et al. 2012 Deep neural networks for acoustic modeling in speech recognition IEEE Signal Processing Magazine

[5] S. HochreiterJ. Schmidhuber 1997 Long short-term memory Neural Comput., November 15; 9(8):1735 1780

[6] A. KrizhevskyI. SutskeverG. Hinton 2012 Image net classification with deep convolutional neural networks In Advances in Neural Information Processing Systems 25 (NIPS)1106 1114

[7] J. LiW. MonroeA. RitterM. GalleyJ. GaoD. Jurafsky 2016 Deep Reinforcement Learning for Dialogue Generation arXiv preprint arXiv:1606.01541

[8] Maas H. L van derP. F. M. J. VerschureP. C. M. Molenaar 1990 A Note on Chaotic Behavior in Simple Neural Networks Neural Networks, 3:119 122

[9] B. MagerkoW. ManzoulM. Ried 2009 An Empirical Study of Cognition and Theatrical Improvisation. In: Proc. Seventh ACM Conf. Creat. Cogn ACM 117 126

[10] K. Pribram 1991 Brain and Perception: Holonomy and Structure in Figural Processing Lawrence Erlbaum Associates

[11] M. RiedlV. Bulitko 2013 Interactive Narrative: An Intelligent Systems Approach AI Mag 34:67 77

[12] V. RieserO. Lemon 2011 Reinforcement Learning for Adaptive Dialogue Systems: A Data-driven Methodology for Dialogue Management and Natural Language Generation Springer Science & Business Media

[13] R. Schechner 1998 Performance Theory Routledge New York

[14] I. SutskeverJ. MartensG. E. Hinton 2011 Generating text with recurrent neural networks In Proceedings of the 28th International Conference on Machine Learning (ICML-11) 1017 1024

[15] I. SutskeverJ. MartensG. DahlG. Hinton 2016 On the importance of initialization and momentum in deep learning Proceedings of the 30th International Conference on Machine Learning, PMLR, 28(3):1139 1147

[16] T. WenM. Gasic 2015 Stochastic language generation in dialogue using recurrent neural networks In Proceedings of SIG-dial for Computational Linguistics

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