Legal consequences of generated content

Practical AI: Machine Learning, Data Science

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.

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Featuring:

• Damien Riehl – Mastodon, Twitter, LinkedIn
• Chris Benson – Twitter, GitHub, LinkedIn, Website
• Daniel Whitenack – Twitter, GitHub, Website

Show 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 | TED

Something missing or broken? PRs welcome!

Chapters

1. Welcome to Practical AI (00:00:07)

2. Damien Riehl (00:00:43)

3. Regulations on AI (00:01:36)

4. All the music (00:03:52)

5. Human vs AI output (00:07:21)

6. Working alongside AI (00:11:30)

7. Finding the gray area (00:14:51)

8. Ai is not copyright-able (00:20:47)

9. Where is the line drawn? (00:24:19)

10. Snake eating its own tail (00:27:42)

11. Can I use these models? (00:30:08)

12. Lessening value of IP (00:31:15)

13. No more patents (00:34:05)

14. 4 Worlds (00:35:55)

15. Scarcity or abundance? (00:38:27)

16. Outrunning the tsunami (00:39:36)

17. Goodbye (00:41:26)

18. Outro (00:42:05)