Google NotebookLM Experiments -- Part 2
Kingsley Idehen
Hi Everyone,
Here’s a new example
of what’s possible with Google’s NotebookLM as an AI Agent for
creating audio summaries from a variety of sources (e.g.,
clipboard text, doc urls, pdfs etc.).
How-To: Generate a Podcast with NotebookLM for Distribution Across Social Media Platforms
Communicating complex, thorny issues to a target audience requires delivering content in their preferred format. For humans, the preferred communication modality typically follows this order: video, audio, and then text. In the age of GenAI, leveraging tools like NotebookLM makes it easier than ever to streamline communication. Here’s a step-by-step guide on how to create and distribute a podcast using NotebookLM:
- Collate notes and topic references (e.g., hyperlinks)
- Feed the collated material into NotebookLM
- Wait a few minutes for NotebookLM to generate a podcast
- Listen to the initial version
- Tweak the material (add or remove content as needed)
- Listen to the revised edition
- If satisfied, add the podcast to an RSS or Atom feed
- Share the feed for subscription by interested parties
As demonstrated in the GIF below, subscribing to the generated RSS or Atom feed can be easily managed by our OPML, RSS, and Atom Reader AI Agent. This dynamic workspace is made possible by recent innovations in LLM-based AI.
Additionally, since people are often hesitant to click on links in content, you can record a video of the podcast playback and upload it to YouTube. This allows you to embed the podcast link in content shared across social media platforms, making it more accessible.
Here’s a practical example of the workflow outlined above in the form of a podcast titled Hyperlinks and Data Meshing Podcast. It showcases how you can communicate complex topics effectively using audio, delivered as a two-person podcast session by industry analysts.
Links:
-- Regards, Kingsley Idehen Founder & CEO OpenLink Software Home Page: http://www.openlinksw.com Community Support: https://community.openlinksw.com Weblogs (Blogs): Company Blog: https://medium.com/openlink-software-blog Virtuoso Blog: https://medium.com/virtuoso-blog Data Access Drivers Blog: https://medium.com/openlink-odbc-jdbc-ado-net-data-access-drivers Personal Weblogs (Blogs): Medium Blog: https://medium.com/@kidehen Legacy Blogs: http://www.openlinksw.com/blog/~kidehen/ http://kidehen.blogspot.com Profile Pages: Pinterest: https://www.pinterest.com/kidehen/ Quora: https://www.quora.com/profile/Kingsley-Uyi-Idehen Twitter: https://twitter.com/kidehen Google+: https://plus.google.com/+KingsleyIdehen/about LinkedIn: http://www.linkedin.com/in/kidehen Web Identities (WebID): Personal: http://kingsley.idehen.net/public_home/kidehen/profile.ttl#i : http://id.myopenlink.net/DAV/home/KingsleyUyiIdehen/Public/kingsley.ttl#this
John F Sowa
Kingsley Idehen
Hi John,
If you listen closely to the podcasts I shared, you’ll notice they weren’t produced in a single step. I had to refine the content based on my understanding of NotebookLM’s processing structure. After a few interactions (most of which are in the RSS feed page I shared), I realized the best results require a clear structure:
1. What’s the problem?
2. How is the problem addressed?
3. Use case examples to support #2 (the podcast agents typically need about 3 use cases).
4. Terminology breakdown.
Ultimately, NotebookLM streamlines productivity by reinforcing a disciplined approach to selecting and editing articles, making it easier to communicate concepts to target audiences.
Dan Brickley
- What are you trying to do? Articulate your objectives using absolutely no jargon.
- How is it done today, and what are the limits of current practice?
- What is new in your approach and why do you think it will be successful?
- Who cares? If you are successful, what difference will it make?
- What are the risks?
- How much will it cost?
- How long will it take?
- What are the mid-term and final “exams” to check for success?
--
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Kingsley Idehen
Hi Dan,
Something like
- What are you trying to do? Articulate your objectives using absolutely no jargon.
- How is it done today, and what are the limits of current practice?
- What is new in your approach and why do you think it will be successful?
- Who cares? If you are successful, what difference will it make?
- What are the risks?
- How much will it cost?
- How long will it take?
- What are the mid-term and final “exams” to check for success?
Yes, but it can be more concise, aligned with the objectives of the message. In my experience, NotebookLM encourages a more disciplined approach to communication. It also highlights an often-overlooked aspect of LLMs—they’re just tools. Operator skills still significantly impact the output, meaning one size still doesn’t fit all in our diverse world :)
Kingsley
To view this discussion on the web visit https://groups.google.com/d/msgid/ontolog-forum/CAFfrAFrn0sDpv5aaCU%2BCtcVpOViD8eLZ4hFxaTBjeSL3_E16jg%40mail.gmail.com.
John F Sowa
Kingsley Idehen
Hi John,
Kingsley,
Your reply shows how and why many applications of LLMs can be valuable.
KI: [They] can be more concise, aligned with the objectives of the message. In my experience, NotebookLM encourages a more disciplined approach to communication. It also highlights an often-overlooked aspect of LLMs—they’re just tools. Operator skills still significantly impact the output, meaning one size still doesn’t fit all in our diverse world :)
I agree that they can gather valuable information and produce useful results, but the human user has to evaluate the results. In your example, 6 out of 8 steps depend on some human to accept, reject, or guide what the LLM-based technology is doing.
Our Permion.ai company uses LLMs for what they do best, The symbolic methods of our VivoMind company (prior to 2010) were very advanced for that time. The new Permion.ai technology combines the best features of the symbolic methods with the LLM methods. It builds on the good stuff, rejects the bad stuff, and gets advice from the users about the doubtful stuff.
John
Yes, and in my opinion, that’s the ultimate endgame. There will always be a role for knowledgeable human operators because the fluid nature of data, information, and knowledge will always lead to doubtful stuff for machines. Commercially, this age-old process will remain entangled in hype cycles driven by the marketing desires of vendors to gain market share and lock in customers—something AI can’t fix.
Kingsley
From: "Kingsley Idehen' via ontolog-forum" <ontolo...@googlegroups.com>
Hi Dan,
On 10/11/24 8:18 AM, 'Dan Brickley' via ontolog-forum wrote:Something like
- What are you trying to do? Articulate your objectives using absolutely no jargon.
- How is it done today, and what are the limits of current practice?
- What is new in your approach and why do you think it will be successful?
- Who cares? If you are successful, what difference will it make?
- What are the risks?
- How much will it cost?
- How long will it take?
- What are the mid-term and final “exams” to check for success?
Yes, but it can be more concise, aligned with the objectives of the message. In my experience, NotebookLM encourages a more disciplined approach to communication. It also highlights an often-overlooked aspect of LLMs—they’re just tools. Operator skills still significantly impact the output, meaning one size still doesn’t fit all in our diverse world :)
Kingsley