<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Laion-5b on AI and Society Course</title><link>https://msucerl.org/cmse101/tags/laion-5b/</link><description>Recent content in Laion-5b on AI and Society Course</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 21 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://msucerl.org/cmse101/tags/laion-5b/index.xml" rel="self" type="application/rss+xml"/><item><title>4.1 Generative Image Models &amp; Artist Rights</title><link>https://msucerl.org/cmse101/use-cases/4-1-generative-image-models/</link><pubDate>Thu, 21 May 2026 00:00:00 +0000</pubDate><guid>https://msucerl.org/cmse101/use-cases/4-1-generative-image-models/</guid><description>&lt;h1 id="generative-image-models--artist-rights"&gt;Generative Image Models &amp;amp; Artist Rights&lt;/h1&gt;
&lt;h2 id="context--systems-architecture"&gt;Context &amp;amp; Systems Architecture&lt;/h2&gt;
&lt;p&gt;The sudden scaling of generative artificial intelligence in visual arts—anchored by commercial systems such as Midjourney, Stable Diffusion, and OpenAI’s DALL-E—relies on deep generative diffusion models. These tools allow users to output high-fidelity illustrations, digital paintings, and photographic assets from simple text prompts. However, the foundational infrastructure of this sector was built on the non-consensual extraction of billions of copyrighted creative works, leading to an intense legal, economic, and ethical conflict between tech conglomerates and the global creative labor force.&lt;/p&gt;</description></item></channel></rss>