As someone who is constantly involved in art, and has taken multiple classes in different mediums, color theory is something that I am experienced in. I have a good eye for colors and color matching and so while I love working with anything involving color it can be slightly difficult for me to push back against perfectionism when something doesn't match exactly as I envisioned it, especially when working in real life, such as with matching curtains or posters with the rest of my furniture. The games that were used in this exercise reinforced my knowledge of color theory. The first involved basic knowledge and color matching, the second was entirely based in color matching, and the third and fourth games had me arrange a palate of colors in the correct order based on their hue, tints, and shades. While I can not say I learned much from this exercise I feel that it was a good way to practice and reinforce my knowledge and skills. The games were a fun challenge, with the most difficult being the color matching game (2nd Image) largely due to it being a timed exercise.
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The topic of AI generated “art” is currently an ethical and legal warzone. AI generated “artwork”, or Artificial Intelligence generated “artwork” is generated through machine learning, where a dataset of images is fed into a machine, which then learns to imitate and draw connections between pieces of those images. Those datasets, filled with billions of pieces of art, are called into question when one wonders whose art is being used. AI generated images and the companies that own the machines are then brought under questioning: Where do they get the art used to train their machine? AI generated images and the systems that generate them at this point in time are illegal and unethical and can not continue.
At this point in time AI is a very prevalent issue among artists, with lawsuits already appearing regarding their legality. However, with this challenge artists have followed the advice of AI “artists”; “Adapt or Die.” There has now been the development of many tools for artists to protect their work from being replicated by AI such as Glaze and Nightshade. While Glaze allows artists to “mask” their works, Nightshade is created to “poison” training sets with every addition of an artwork using it to the dataset, and the best part is that neither affect how the artwork is seen by human viewers (Heikkilä). These tools allow artists to rightfully take back their rights over their artworks, while still being able to share their creations online with others. AI meant to generate artwork are unethical from how they are created, to how they are monetized, to even the users of these machines and how they defend the “legitimacy” of their “artwork”. Although unethical image generation is at the forefront of conflicts regarding AI, and there have been actions against it, art has never been the only thing affected by unethical datasets. Facial recognition used by police, for example, enforced racial discrimination due to its inability to identify darker skinned individuals, with error rates sometimes “up to 34% higher [for darker-skinned females] than for lighter-skinned males” (Najibi). There needs to not only be legal ramifications for those who have stolen artwork to train their AI, but also regulations around datasets used by all AI for any purpose. Helyer, Ruby. “What Are the Copyright Rules around AI Art?” MUO, 14 Feb. 2023, www.makeuseof.com/copyright-rules-ai-art/.
NO to AI Generated Images – Protest on Artstation – BrushWarriors. brushwarriors.com/no-to-ai/. Baio, Andy. “Exploring 12 Million of the 2.3 Billion Images Used to Train Stable Diffusion’s Image Generator.” Waxy, 30 Aug. 2022, waxy.org/2022/08/exploring-12-million-of-the-images-used-to-train-stable-diffusions-image-generator/. Heikkilä, Melissa. “This New Data Poisoning Tool Lets Artists Fight Back against Generative AI.” MIT Technology Review, 23 Oct. 2023, www.technologyreview.com/2023/10/23/1082189/data-poisoning-artists-fight-generative-ai/. Najibi, Alex. “Racial Discrimination in Face Recognition Technology.” Science in the News, Harvard University, 24 Oct. 2020, sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/. This exercise was my first introduction to photoshop. While I have previous experience in a few digital drawing programs I have not used any adobe products before this. Because of this previous experience I feel that I grasped the tools I used in photoshop quickly, as many programs share the same base abilities.
This was a guided project with a tutorial given for each tool used during it. Through this I learned how to mask images using the magic wand and brush tool, how to move and resize objects, and how to import and export images as different file types. As an extension I also learned how to use the Bevel & Emboss setting to quickly give objects a feeling of depth, such as the condiment. |
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April 2024
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