Memo Akten
ABSTRACT
The authors present a visual instrument developed as part of the creation of the artwork Learning to See. The artwork explores bias in artificial neural networks and provides mechanisms for the manipulation of specifically trained-for real-world representations. The exploration of these representations acts as a metaphor for the process of developing a visual understanding and/or visual vocabulary of the world. These representations can be explored and manipulated in real time, and have been produced in such a way so as to reflect specific creative perspectives that call into question the relationship between how both artificial neural networks and humans may construct meaning.
- F. Santoni de Sio and J. van den Hoven, "Meaningful Human Control over Autonomous Systems: A Philosophical Account," Frontiers in Robotics and AI 5, (2018), p. 15.Google Scholar
Cross Ref
- Y. LeCun and Y. Bengio, "Convolutional Networks for Images, Speech, and Time-Series" in The Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed. (Cambridge, MA: The MIT Press, 1995), pp. 255--258. Google ScholarDigital Library
- D.H. Hubel and T.N. Wiesel, "Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex," The Journal of Physiology <b>160</b>, No. 1, pp. 106--154 (1962).Google Scholar
- A. Clark, "Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science," Behavioral and Brain Sciences <b>36</b>, No. 3, pp. 181--204 (2013).Google Scholar
- D.D. Hoffman, Visual Intelligence: How We Create What We See (New York: W.W. Norton & Company, 2000).Google Scholar
- L.-B. Castel, L'Optique des Couleurs (Paris: Chez Briasson, 1740).Google Scholar
- B. Alves, "Digital Harmony of Sound and Light," Computer Music Journal <b>29</b>, No. 4, pp. 45--54 (2005). Google ScholarCross Ref
- P. Isola et al., "Image-to-Image Translation with Conditional Adversarial Networks," arXiv preprint arXiv:1611.07004 (2016).Google Scholar
- L.A. Gatys, A.S. Ecker and M. Bethge, "A Neural Algorithm of Artistic Style," arXiv preprint arXiv:1508.06576 (2015).Google Scholar
- V. Dumoulin, J. Shlens and M. Kudlur, "A Learned Representation for Artistic Style," arXiv preprint arXiv:1610.07629 (2016).Google Scholar
Recommendations
SEE: A Spatial Exploration Environment Based on a Direct-Manipulation Paradigm
The need forproviding effective tools for analyzing and querying spatial data is becoming increasingly important with the explosion of data in applications such as geographic information systems, image databases, CAD, and remote sensing. The SEE (...
How to see the beauty that is not there: the aesthetic element of programming in the computer-based media art
OCSC'07: Proceedings of the 2nd international conference on Online communities and social computingThis study is to define aesthetic elements of the programming in the computer-based media art. It can be explained as the totality of the concept and reality in the respect of collaboration of art and science. The programming as aesthetic object ...
What you see is what you can change
Visual analytics (VA) systems help data analysts solve complex problems interactively, by integrating automated data analysis and mining, such as machine learning (ML) based methods, with interactive visualizations. We propose a conceptual framework ...
Comments