Amplifying The Uncanny
Analysing the methodology and applications of Machine Learning and Generative Adversarial Networks (GAN) framework.
Computational tools and techniques, such as machine learning and GAN, are definitive to the applications of such technology for a generative purpose. The paper explores the exploitations of these deep generative models in the production of artificial images of human faces (deepfakes) and Read more →
Pursuit of Realism
Uncanny valley is the obstacle that CGI artists have to break through. Mashiro Mori introduced that as a humanoid robot’s representation approaches a great closeness to human form, it induces negative responses. Only past a certain degree of human likeness and familiarity, then a robotic form will induce positive responses. It means that on this scale of human Read more →
Amplifying the Uncanny explores the boundaries of what makes a generated image fake but reversing the generative process and to exaggerate the imitated features of Deepfakes. By producing these unreal images through manipulating the generator, the divide between natural and man-made is clearly evident, proving the vulnerabilities of a mechanical system no matter how
The uncanny is a psychological or aesthetic Read more →
The reading begins with explaining that in recent years, machine learning systems grew excellent at producing images, with major focus on creating human faces that seem very real, resulting in possible controversies with technology such as deepfake.
Deepfake is a technology of generating faces by referring to existing images, to create brand new faces which cannot be found through image searches Read more →
Overarching Thoughts (on GAN , ‘ The Uncanny’ in contemporary society)
In this article, the authors explores how Generative Adversarial Networks (GAN) and Machine Learning have intensified the phenomena of the ‘uncanny’ — described as a certain level of discomfort or unease one feels when a machine mimicking organic human behaviour comes seemingly close to, yet disjointed and ‘faulty’ Read more →
The article, Amplifying The Uncanny, by Broad, Leymarie and Grierson offered an insightful knowledge of how machine learning algorithms can be optimised and used to produce uncanny results.
Machine Learning & Generative Adversarial Network
Machine learning is a system where pattern data are used to predict future data or other outcomes of interest. It is understood as an automated process of optimisation, Read more →
This generative study aims to using magnets to manipulate natural objects to create spontaneous and random sounds
The first trial was done using corals. The magnets were initially hanging side by side however the attraction was too strong which did not cause movement, and I had to move the hold the magnet instead to manipulate the hanging magnet.
Second structure using an Read more →
Waltz mainly discusses how generative art in itself though highly precise and ‘controlled’ in the way the system which produces it is designed, its aim is to reproduce an outcome that is as close to nature as possible — organic and spontaneous phenomena which carry the inherent characteristic of ‘life’ itself — mutability and hence unpredictability. As Read more →
Marius Watz describes generative art as “a computational model of creativity combining principles of unpredictability with the purity of logic”. He explains that from results that cannot be created through human hands, the nature of work is complex, even unpredictable. Generative art often takes inspiration from nature, but there is a struggle between natural forms and mechanical forms; what artists Read more →
Marcus Watz describes generative art as using systems as the computational mode of creativity, thus relying on chance and logic to create a work. It does not constitute an art movement as generative art describes a common strategy for how works are produced through methodology. Unlike interactive art which exploits the feedback loop of interaction between the system and user, Read more →