I Feel You: What makes algorithmic experience personal?

Since artificial neural networks are excelling in stylization of visual, textual and acoustic objects, we discuss the potential of such technologies in context of human-to-machine interactions in general and in an artistic context specifically. We discuss a paradigm of an uncanny-valley that was originally proposed for human-robot interaction and look at it at a new angle working with algorithms rather than with robots. We propose an exploration framework for human-algorithm interaction and illustrate basic principles of such interactions with two case studies on the verge of art and computer science. Finally, we discuss how exploration paradigm outlines the vector of further humanalgorithm collaborations.

Content

Author and article information

Contributors

Ivan P. Yamshchikov

Alexey Tikhonov

Conference

Publication date: May 2018

Publication date (Print): May 2018

Pages: 1-6

Affiliations

[1 ] Max Planck Institute for Mathematics in the Sciences, Leipzig

04103, Inselstrasse 22

[2 ] Yandex, Berlin

10178, Karl-Liebknecht-Strasse, 1

Article

DOI: 10.14236/ewic/EVAC18.32

SO-VID: ec24c066-de78-4713-9bb5-00190d0fa58c

License:

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: Politics of the Machines - Art and After

Conference acronym: EVA Copenhagen

Conference number: 7

Conference location: Aalborg University, Copenhagen, Denmark

Conference date: 15 - 17 May 2018

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Digital arts and culture

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVAC18.32

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

References

A Abe 2011 Curation and communication in chance discovery Proc. of 6th International Workshop on Chance Discovery (IWCD6) in IJCAI
E Broadbent 2017 Interactions with robots: The truths we reveal about ourselves Annual review of psychology 68 627 652
T. J. BurleighJ. R. SchoenherrG. L. Lacroix 2013 Does the uncanny valley exist? An empirical test of the relationship between eeriness and the human likeness of digitally created faces Computers in Human Behavior 29 3 759 771
S. K. CardT.P MoranA. Newell 1983 The psychology of human-computer interaction CRC Press
T ChiD. Fan 1997 Cognitive limitations and investment “myopia” Decision Sciences 28 1 27 57
D Faust 1982 A needed component in prescriptions for science: Empirical knowledge of human cognitive limitations Knowledge 3 4 555 570
L. A. GatysA. S. EckerM. Bethge 2016 Image style transfer using convolutional neural networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2414 2423
M Gromov 2017 Meaning of mathematics and mathematics of meaning. https://youtu.be/EE2sXkljsU
B JoshiK. StewartD. Shapiro 2017 Bringing impressionism to life with neural style transfer in come swim arXiv preprint arXiv:1701.04928
A NewellH. A. Simon 1972 Human problem solving (Vol. 104, No. 9) Englewood Cliffs, NJ Prentice-Hall
M. B. MathurD. B. Reichling 2016 Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley Cognition 146 22 32
J McCormackO. BownA. DorinJ. McCabeG. MonroM. Whitelaw 2014 Ten questions concerning generative computer art Leonardo 47 2 135 141
K. F. MacDormanS. O. Entezari 2015 Individual differences predict sensitivity to the uncanny valley Interaction Studies 16 2 141 172
D Michie 1972 Programmer's gambit New Scientist 17^th of August 329 332
M Mori 1970 The uncanny valley Energy 7 4 33 35
M MoriK. F. MacDormanN. Kageki 2012 The uncanny valley [from the field] IEEE Robotics & Automation Magazine 19 2 98 100
J. KätsyriK. FörgerM. MäkäräinenT. Takala 2015 A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness Frontiers in psychology 6 390
Y Ohsawa 2003 Modeling the process of chance discovery Chance discovery 2 15 Springer Berlin, Heidelberg
N SaabW. R. JoolingenB. H. Hout-Wolters 2005 Communication in collaborative discovery learning British Journal of Educational Psychology 75 4 603 621
J Sweller 2006 How the human cognitive system deals with complexity Handling complexity in learning environments: Theory and research 13 25
A TikhonovI.P. Yamshchikov 2017a Model by Neurona https://medium.com/@creaited/model-byneurona-b6208e2693d1
A TikhonovI.P. Yamshchikov 2017b Neuroscriabin: generating a melody for the anniversary of the maestro https://medium.com/@creaited/neuroscriabin-generating-a-melody-for-the-anniversary-of-the-maestro-d2e6acac956
A TikhonovI. P. Yamshchikov 2017c Music generation with variational recurrent autoencoder supported by history arXiv preprint arXiv:1705.05458
A TikhonovI.P. Yamshchikov 2018 Guess who? Multilingual approach for the automated generation of author-stylized poetry arXiv preprint arXiv:1807.07147
D. C. Vladeck 2014 Machines without principals: liabilitylity rules and artificial intelligence Wash. L. Rev 89 117
J Weizenbaum 1966 ELIZA—a computer program for the study of natural language communication between man and machine Communications of the ACM 9 1 36 45
B Whitby 2008 Sometimes it's hard to be a robot: a call for action on the ethics of abusing artificial agents Interact. Comput 20:326 33
Y YamadaT. KawabeK. Ihaya 2013 Categorization difficulty is associated with negative evaluation in the “uncanny valley” phenomenon Japanese Psychological Research 55 1 20 32
IP YamshchikovA. Tikhonov 2018 An interactive visualization for VRASH exploration https://youtu.be/3-KCR2OnjFg

Comments

Comment on this article

[1] A Abe 2011 Curation and communication in chance discovery Proc. of 6th International Workshop on Chance Discovery (IWCD6) in IJCAI

[2] E Broadbent 2017 Interactions with robots: The truths we reveal about ourselves Annual review of psychology 68 627 652

[3] T. J. BurleighJ. R. SchoenherrG. L. Lacroix 2013 Does the uncanny valley exist? An empirical test of the relationship between eeriness and the human likeness of digitally created faces Computers in Human Behavior 29 3 759 771

[4] S. K. CardT.P MoranA. Newell 1983 The psychology of human-computer interaction CRC Press

[5] T ChiD. Fan 1997 Cognitive limitations and investment “myopia” Decision Sciences 28 1 27 57

[6] D Faust 1982 A needed component in prescriptions for science: Empirical knowledge of human cognitive limitations Knowledge 3 4 555 570

[7] L. A. GatysA. S. EckerM. Bethge 2016 Image style transfer using convolutional neural networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2414 2423

[8] M Gromov 2017 Meaning of mathematics and mathematics of meaning. https://youtu.be/EE2sXkljsU

[9] B JoshiK. StewartD. Shapiro 2017 Bringing impressionism to life with neural style transfer in come swim arXiv preprint arXiv:1701.04928

[10] A NewellH. A. Simon 1972 Human problem solving (Vol. 104, No. 9) Englewood Cliffs, NJ Prentice-Hall

[11] M. B. MathurD. B. Reichling 2016 Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley Cognition 146 22 32

[12] J McCormackO. BownA. DorinJ. McCabeG. MonroM. Whitelaw 2014 Ten questions concerning generative computer art Leonardo 47 2 135 141

[13] K. F. MacDormanS. O. Entezari 2015 Individual differences predict sensitivity to the uncanny valley Interaction Studies 16 2 141 172

[14] D Michie 1972 Programmer's gambit New Scientist 17^th of August 329 332

[15] M Mori 1970 The uncanny valley Energy 7 4 33 35

[16] M MoriK. F. MacDormanN. Kageki 2012 The uncanny valley [from the field] IEEE Robotics & Automation Magazine 19 2 98 100

[17] J. KätsyriK. FörgerM. MäkäräinenT. Takala 2015 A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness Frontiers in psychology 6 390

[18] Y Ohsawa 2003 Modeling the process of chance discovery Chance discovery 2 15 Springer Berlin, Heidelberg

[19] N SaabW. R. JoolingenB. H. Hout-Wolters 2005 Communication in collaborative discovery learning British Journal of Educational Psychology 75 4 603 621

[20] J Sweller 2006 How the human cognitive system deals with complexity Handling complexity in learning environments: Theory and research 13 25

[21] A TikhonovI.P. Yamshchikov 2017a Model by Neurona https://medium.com/@creaited/model-byneurona-b6208e2693d1

[22] A TikhonovI.P. Yamshchikov 2017b Neuroscriabin: generating a melody for the anniversary of the maestro https://medium.com/@creaited/neuroscriabin-generating-a-melody-for-the-anniversary-of-the-maestro-d2e6acac956

[23] A TikhonovI. P. Yamshchikov 2017c Music generation with variational recurrent autoencoder supported by history arXiv preprint arXiv:1705.05458

[24] A TikhonovI.P. Yamshchikov 2018 Guess who? Multilingual approach for the automated generation of author-stylized poetry arXiv preprint arXiv:1807.07147

[25] D. C. Vladeck 2014 Machines without principals: liabilitylity rules and artificial intelligence Wash. L. Rev 89 117

[26] J Weizenbaum 1966 ELIZA—a computer program for the study of natural language communication between man and machine Communications of the ACM 9 1 36 45

[27] B Whitby 2008 Sometimes it's hard to be a robot: a call for action on the ethics of abusing artificial agents Interact. Comput 20:326 33

[28] Y YamadaT. KawabeK. Ihaya 2013 Categorization difficulty is associated with negative evaluation in the “uncanny valley” phenomenon Japanese Psychological Research 55 1 20 32

[29] IP YamshchikovA. Tikhonov 2018 An interactive visualization for VRASH exploration https://youtu.be/3-KCR2OnjFg

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