Kingsley,

I strongly agree with your 8 point method.  And it strongly supports my many comments about the need to evaluate and correct output generated by LLMs.

Note that points (1)  and (2) are human preparatory work.  (4) is human evaluation. (5) is human correction.  (6 & 7) are more evaluation.  And (8) is the final application.

In summary, 6 out of the 8 points depend on human work.  With current LLM applications human evaluation is far more reliable than current computational methods.  No claim of ARTIFCIAL GENERAL intelligence can be based on a system that requires that much human intelligence to make the results dependable.

I am not rejecting the value of the LLM-based technology.  I am merely rejecting the claims that it is on the way toward AGI.

John

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From: 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:

1. Collate notes and topic references (e.g., hyperlinks)
2. Feed the collated material into NotebookLM
3. Wait a few minutes for NotebookLM to generate a podcast
4. Listen to the initial version
5. Tweak the material (add or remove content as needed)
6. Listen to the revised edition
7. If satisfied, add the podcast to an RSS or Atom feed
8. Share the feed for subscription by interested parties

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