An Artificial Discussion of the Innumerable Meanings Sutra

Listen to this podcast at notebooklm.google.com

 


I’ve been having fun applying AI tools to my Buddhist exploration. There’s been “A Little AI Magic” and last year’s “Compare and Contrast Tendai Teachings and Nichiren Doctrine” and even back in 2020, AI and Buddhism.

Today I ran across the ZD Net article Google’s hidden AI tool turns your text into stunningly lifelike podcasts – for free. Listen for yourself

Google’s NotebookLM allows you unload a document and have the NotebookLM work it’s magic on that material. But it also allows you to specify the website you want to use for the source material. This, unfortunately, is not as thorough as I’d like. For example, if I give NotebookLM my root URL – 500yojanas.org – NotebookLM only looks at the content displayed on that page, it does not follow the links to subsequent pages or include the content found under each of the menus. To get around that limitation, you need to focus on content that is self-contained. That’s why I chose the URL 500yojanas.org/lotus-sutra/full-text/sutra-of-innumerable-meanings/ for my Podcast example above.

As for the podcast product produced, I am quite impressed with the presentation and general quality of the discussion. We are a long way away from the days when computer generated talking was easily identifiable.

However, this is not a perfect system. As an example of the limitations listen to this NotebookLM podcast discussing “Daimoku.” For source material I gave the URL 500yojanas.org/blog/?s=Daimoku This brings up the 10 most recent blog posts in which I’ve discussed Daimoku.

(Listen on NotebookLM)

The failure to pronounce “ren ge” – Lotus Flower – is a killer for me. But much of the remainder of the discussion is equally off. It just feels fake. That’s probably a product of NotebookLM failing to use the full articles and instead summarizing the 10 summaries.

I plan to experiment more, but obviously this is a very new world we live in today.


See The Output Really Depends on the Input