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Adobe Firefly is developed using Adobe's Sensei platform. Firefly is trained with images from Creative Commons, Wikimedia and Flickr Commons as well as 300 million images and videos in Adobe Stock and the public domain. [4] [5] [6] It uses image data sets to generate various designs. [7] It learns from user feedback by adjusting its designs ...
Although it is free of copyright restrictions, this image may still be subject to other restrictions. See WP:PD § Fonts and typefaces or Template talk:PD-textlogo for more information. This work includes material that may be protected as a trademark in some jurisdictions.
Although it is free of copyright restrictions, this image may still be subject to other restrictions. See WP:PD § Fonts and typefaces or Template talk:PD-textlogo for more information. This work includes material that may be protected as a trademark in some jurisdictions.
One color entry in a single GIF or PNG image's palette can be defined as "transparent" rather than an actual color. This means that when the decoder encounters a pixel with this value, it is rendered in the background color of the part of the screen where the image is placed, also if this varies pixel-by-pixel as in the case of a background image.
A major concern raised about AI-generated images and art is sampling bias within model training data leading towards discriminatory output from AI art models. In 2023, University of Washington researchers found evidence of racial bias within the Stable Diffusion model, with images of a "person" corresponding most frequently with images of males ...
[17]: 1 These activities have more recently been joined by the Generator.x conference in Berlin starting in 2005. In 2012 the new journal GASATHJ, Generative Art Science and Technology Hard Journal was founded by Celestino Soddu and Enrica Colabella [ 18 ] jointing several generative artists and scientists in the editorial board.
Marvin Minsky et al. raised the issue that AI can function as a form of surveillance, with the biases inherent in surveillance, suggesting HI (Humanistic Intelligence) as a way to create a more fair and balanced "human-in-the-loop" AI. [61] Explainable AI has been recently a new topic researched amongst the context of modern deep learning.