GMU:Hallucinating computers and dreaming non-machines/Lina Louise Wolff: Difference between revisions

Revision as of 09:28, 27 May 2025

Background information

The work explores how the lack of female representation in research and clinical trials has led to disproportionate adverse drug reactions in women. These effects are the result of healthcare systems historically designed around male bodies, often treating them as the default in diagnostics, treatment, and pharmaceutical development, showing that health systems are taking better care of male bodies then of any others. The unequal treatment gets visible through gaps, most common known as the gender data gap.

This work is based on a previous semester project and explores how the lack of female representation in research and clinical trials has led to disproportionate adverse drug reactions in women. These effects are the result of healthcare systems historically designed around male bodies, often treating them as the default in diagnostics, treatment, and pharmaceutical development, showing that care systems are taking better care of male bodies then of any others.

The AI functions by analyzing visual input through multiple neural network layers. With each layer, specific features are exaggerated, leading to a progressively surreal image. When the algorithm encounters ambiguity, it begins to “hallucinate”—projecting its own learned patterns onto the input.

Idea/Concept

The installation focuses on visualizing the blank which is created through data blind spots and takes a closer look on how AI fills these blanks. AI s function by analyzing visual input through multiple neural network layers. With each layer, specific features are exaggerated, leading to a progressively surreal image. When the algorithm encounters ambiguity, it begins to “hallucinate”, projecting its own learned patterns onto the input. This work critically examines how both care systems and AI technologies are structured around existing assumptions, ultimately asking: whose bodies are being seen, and whose are being overlooked? In an analoge way the artwork reinterprets how image dreaming AIs, like Google’s DeepDream, start to hallucinate and dream and therefore tries to close gaps.

Tech implementation

Installation exsisting of multiple layers with different textures

Glass, maybe latex, projections..

We might use the visuals which we are currently creating in the class ‘‘macro worlds‘‘

References

Artists:

Ryoji Ikeda-> sound artist

Kristen Jakubek

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 The work explores how the lack of female representation in research and clinical trials has led to disproportionate adverse drug reactions in women. These effects are the result of healthcare systems historically designed around male bodies, often treating them as the default in diagnostics, treatment, and pharmaceutical development, showing that health systems are taking better care of male bodies then of any others. The unequal treatment gets visible through gaps, most common known as the gender data gap.
-In a previous
+This work is based on a previous semester project and explores how the lack of female representation in research and clinical trials has led to disproportionate adverse drug reactions in women. These effects are the result of healthcare systems historically designed around male bodies, often treating them as the default in diagnostics, treatment, and pharmaceutical development, showing that care systems are taking better care of male bodies then of any others.
 The AI  functions by analyzing visual input through multiple neural network layers. With each layer, specific features are exaggerated, leading to a progressively surreal image. When the algorithm encounters ambiguity, it begins to “hallucinate”—projecting its own learned patterns onto the input.
 ==Idea/Concept==
-The installation reinterprets in an analoge way how image dreaming AIs, like Google’s DeepDream, start to hallucinate and dream .
+The installation focuses on visualizing the blank which is created through  data blind spots and takes a closer look on how AI fills these blanks. AI s function by analyzing visual input through multiple neural network layers. With each layer, specific features are exaggerated, leading to a progressively surreal image. When the algorithm encounters ambiguity, it begins to “hallucinate”, projecting its own learned patterns onto the input. This work critically examines how both care systems and AI technologies are structured around existing assumptions, ultimately asking: whose bodies are being seen, and whose are being overlooked? In an analoge way the artwork reinterprets how image dreaming AIs, like Google’s DeepDream, start to hallucinate and dream and therefore tries to close gaps.
-The Image AI Creator DeepDream from google uses multiple layers to create a surreal image. With every layer the image gets more obvious.
-If the response is not clear enough for the machine, it learns to <nowiki>''hallucinate ''</nowiki> different parts of the image so to say interprets them.
-In an instellation we want to recreate this machine learning technique by creating a surreal painting, which shows a Utopia, in which the gender data gap does not exist, or there are no inequalities between gender.
-Additional there will be a sound design inspired by the sound artist Ryoki Ikeda, who uses errors in hard drives to create sound effects. In theory light should react to the sound. To visualize missing data the light will glitch, as well as the sound.
 ==Tech implementation==