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The Ghost in the Machine: Can AI Truly Resurrect Dead Composers?

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Manage episode 498074861 series 3670994
Content provided by Ran Chen. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Ran Chen or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
Can AI bring the legendary composers back to life? In this episode, we explore the fascinating and controversial world of neural networks attempting to replicate the styles of masters like Bach and Schubert. We journey from a concert hall in London, where an AI attempts to complete a symphony left unfinished for two centuries, to the digital realm where an algorithm learns to compose like a baroque master. The results are often shockingly accurate on the surface, sometimes even fooling the experts. But does technical perfection equal artistic genius? We dissect a famous attempt to complete a masterpiece and what it reveals about the deep-seated differences between algorithmic imitation and human creation. It’s a story about the limits of technology and the enduring, irreplaceable spark of the human soul in art. In this episode: 1. How did a tech giant use AI to complete Schubert's legendary "Unfinished" Symphony? 2. Can an algorithm really fool music experts into thinking it's Bach? 3. What is the critical difference between imitating a musical style and capturing a creative soul? 4. Why do AI-completed works often feel technically perfect but emotionally hollow? 5. Is the future of composition a collaboration between human artists and AI assistants? 6. What are the ethical questions behind resurrecting a dead composer's musical voice? 7. Can an algorithm truly understand the human suffering and joy that drives great art? Follow my YouTube: https://www.youtube.com/@chenran818 or listen to my music on Apple music, Spotify or other platforms: https://ffm.bio/chenran818
  continue reading

80 episodes

Artwork
iconShare
 
Manage episode 498074861 series 3670994
Content provided by Ran Chen. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Ran Chen or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
Can AI bring the legendary composers back to life? In this episode, we explore the fascinating and controversial world of neural networks attempting to replicate the styles of masters like Bach and Schubert. We journey from a concert hall in London, where an AI attempts to complete a symphony left unfinished for two centuries, to the digital realm where an algorithm learns to compose like a baroque master. The results are often shockingly accurate on the surface, sometimes even fooling the experts. But does technical perfection equal artistic genius? We dissect a famous attempt to complete a masterpiece and what it reveals about the deep-seated differences between algorithmic imitation and human creation. It’s a story about the limits of technology and the enduring, irreplaceable spark of the human soul in art. In this episode: 1. How did a tech giant use AI to complete Schubert's legendary "Unfinished" Symphony? 2. Can an algorithm really fool music experts into thinking it's Bach? 3. What is the critical difference between imitating a musical style and capturing a creative soul? 4. Why do AI-completed works often feel technically perfect but emotionally hollow? 5. Is the future of composition a collaboration between human artists and AI assistants? 6. What are the ethical questions behind resurrecting a dead composer's musical voice? 7. Can an algorithm truly understand the human suffering and joy that drives great art? Follow my YouTube: https://www.youtube.com/@chenran818 or listen to my music on Apple music, Spotify or other platforms: https://ffm.bio/chenran818
  continue reading

80 episodes

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