“Syntropic Counterpoints : Art of AI Sense or Machine Made Context Art” by Nikolić, Yang, Chen and Stankevich

  • ©Predrag K. Nikolić, Hua Yang, Jyunjye Chen, and George Peter Stankevich

  • ©Predrag K. Nikolić, Hua Yang, Jyunjye Chen, and George Peter Stankevich

  • ©Predrag K. Nikolić, Hua Yang, Jyunjye Chen, and George Peter Stankevich

Conference:


Entry Number: 18

Title:

    Syntropic Counterpoints : Art of AI Sense or Machine Made Context Art

Presenter(s):



Abstract:


    INTRODUCTION

    Project Syntropic Counterpoints has been conceptualized in the form of a series of discussions between artificial intelligence (historical persons) clones, related to topics we want to expose to AI interpretation. The project is an artist response to rising technology singularity and emerging Artificial Intelligence implementation in every aspect of everyday life which changes the social interaction landscape forever. With this project we intend to point to questions such as: Are we using AI to make humans smarter or to create a new living entity equal to us? How will this reflect on human society and its present planetary supremacy? Can we share the world and accept equality with a new AI living entity? What could be the consequences of that decision? We are also trying to point to AI limitations and to examine the cultural, creative, historical and social benefits we can gain by using AI.

References:


    • [n. d.]. Softmax Regression – Ufldl. http://ufldl.stanford.edu/wiki/index.php/Softmax_ Regression. ([n. d.]). (Accessed on 01/03/2018).
    • [n. d.]. Unsupervised Feature Learning and Deep Learning Tutorial. http://ufldl. stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/. ([n. d.]). (Accessed on 01/03/2018). Andrej Karpathy. 2015. The unreasonable effectiveness of recurrent neural networks. Andrej Karpathy blog (2015).

Keyword(s):



PDF:



ACM Digital Library Publication:


Overview Page:



Type: