Legal consequences of generated content
As a technologist, coder, and lawyer, few people are better equipped to discuss the legal and practical consequences of generative AI than Damien Riehl. He demonstrated this a couple years ago by generating, writing to disk, and then releasing every possible musical melody. Damien joins us to answer our many questions about generated content, copyright, dataset licensing/usage, and the future of knowledge work.Join the discussionChangelog++ members save 1 minute on this episode because they made the ads disappear. Join today!Sponsors:Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.comFly.io – The home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs. Typesense – Lightning fast, globally distributed Search-as-a-Service that runs in memory. You literally can’t get any faster! Featuring:Damien Riehl – LinkedIn, Mastodon, XChris Benson – Website, GitHub, LinkedIn, XDaniel Whitenack – Website, GitHub, XShow Notes:Talk - Legal and Practical Consequences of Generative AI (LLMs like GPT, Bart, PaLM, LLaMA, Alpaca, Codex)Talk - Why All Melodies Should Be Free for Musicians to Use | Damien Riehl | TEDSomething missing or broken? PRs welcome! ★ Support this podcast ★