Like nearly everybody, we had been impressed by the flexibility of NotebookLM to generate podcasts: Two digital individuals holding a dialogue. You can provide it some hyperlinks, and it’ll generate a podcast based mostly on the hyperlinks. The podcasts had been attention-grabbing and interesting. However additionally they had some limitations.
The issue with NotebookLM is that, whilst you can provide it a immediate, it largely does what it’s going to do. It generates a podcast with two voices—one male, one feminine—and provides you little management over the end result. There’s an non-obligatory immediate to customise the dialog, however that single immediate doesn’t permit you to do a lot. Particularly, you may’t inform it which subjects to debate or in what order to debate them. You possibly can strive, but it surely gained’t pay attention. It additionally isn’t conversational, which is one thing of a shock now that we’ve all gotten used to chatting with AIs. You possibly can’t inform it to iterate by saying “That was good, however please generate a brand new model altering these particulars” like you may with ChatGPT or Gemini.
Study sooner. Dig deeper. See farther.
Can we do higher? Can we combine our information of books and know-how with AI’s potential to summarize? We’ve argued (and can proceed to argue) that merely studying the way to use AI isn’t sufficient; you could discover ways to do one thing with AI that’s higher than what the AI may do by itself. It is advisable combine synthetic intelligence with human intelligence. To see what that may appear like in follow, we constructed our personal toolchain that provides us rather more management over the outcomes. It’s a multistage pipeline:
- We use AI to generate a abstract for every chapter of a guide, ensuring that each one the vital subjects are coated.
- We use AI to assemble the chapter summaries right into a single abstract. This step primarily provides us an prolonged define.
- We use AI to generate a two-person dialogue that turns into the podcast script.
- We edit the script by hand, once more ensuring that the summaries cowl the proper subjects in the proper order. That is additionally a possibility to appropriate errors and hallucinations.
- We use Google’s speech-to-text multispeaker API (nonetheless in preview) to generate a abstract podcast with two contributors.
Why are we specializing in summaries? Summaries curiosity us for a number of causes. First, let’s face it: Having two nonexistent individuals focus on one thing you wrote is fascinating—particularly since they sound genuinely and excited. Listening to the voices of nonexistent cyberpeople focus on your work makes you are feeling such as you’re residing in a sci-fi fantasy. Extra virtually: Generative AI is definitely good at summarization. There are few errors and nearly no outright hallucinations. Lastly, our customers need summarization. On O’Reilly Solutions, our clients continuously ask for summaries: summarize this guide, summarize this chapter. They wish to discover the knowledge they want. They wish to discover out whether or not they actually need to learn the guide—and in that case, what components. A abstract helps them do this whereas saving time. It lets them uncover shortly whether or not the guide will probably be useful, and does so higher than the again cowl copy or a blurb on Amazon.
With that in thoughts, we needed to suppose via what essentially the most helpful abstract could be for our members. Ought to there be a single speaker or two? When a single synthesized voice summarized the guide, my eyes (ears?) glazed over shortly. It was a lot simpler to hearken to a podcast-style abstract the place the digital contributors had been excited and enthusiastic, like those on NotebookLM, than to a lecture. The give and take of a dialogue, even when simulated, gave the podcasts vitality {that a} single speaker didn’t have.
How lengthy ought to the abstract be? That’s an vital query. Sooner or later, the listener loses curiosity. We may feed a guide’s complete textual content right into a speech synthesis mannequin and get an audio model—we might but do this; it’s a product some individuals need. However on the entire, we anticipate summaries to be minutes lengthy slightly than hours. I would pay attention for 10 minutes, possibly 30 if it’s a subject or a speaker that I discover fascinating. However I’m notably impatient once I hearken to podcasts, and I don’t have a commute or different downtime for listening. Your preferences and your scenario could also be a lot completely different.
What precisely do listeners anticipate from these podcasts? Do customers anticipate to study, or do they solely wish to discover out whether or not the guide has what they’re searching for? That relies on the subject. I can’t see somebody studying Go from a abstract—possibly extra to the purpose, I don’t see somebody who’s fluent in Go studying the way to program with AI. Summaries are helpful for presenting the important thing concepts offered within the guide: For instance, the summaries of Cloud Native Go gave a superb overview of how Go may very well be used to handle the problems confronted by individuals writing software program that runs within the cloud. However actually studying this materials requires taking a look at examples, writing code, and training—one thing that’s out of bounds in a medium that’s restricted to audio. I’ve heard AIs learn out supply code listings in Python; it’s terrible and ineffective. Studying is extra doubtless with a guide like Facilitating Software program Structure, which is extra about ideas and concepts than code. Somebody may come away from the dialogue with some helpful concepts and presumably put them into follow. However once more, the podcast abstract is simply an summary. To get all the worth and element, you want the guide. In a current article, Ethan Mollick writes, “Asking for a abstract isn’t the identical as studying for your self. Asking AI to unravel an issue for you isn’t an efficient strategy to study, even when it feels prefer it needs to be. To study one thing new, you’re going to must do the studying and pondering your self.”
One other distinction between the NotebookLM podcasts and ours could also be extra vital. The podcasts we generated from our toolchain are all about six minutes lengthy. The podcasts generated by NotebookLM are within the 10- to 25-minute vary. The longer size may permit the NotebookLM podcasts to be extra detailed, however in actuality that’s not what occurs. Reasonably than discussing the guide itself, NotebookLM tends to make use of the guide as a leaping off level for a broader dialogue. The O’Reilly-generated podcasts are extra directed. They observe the guide’s construction as a result of we supplied a plan, a top level view, for the AI to observe. The digital podcasters nonetheless specific enthusiasm, nonetheless usher in concepts from different sources, however they’re headed in a route. The longer NotebookLM podcasts, in distinction, can appear aimless, looping again round to select up concepts they’ve already coated. To me, at the very least, that looks like an vital level. Granted, utilizing the guide because the jumping-off level for a broader dialogue can also be helpful, and there’s a steadiness that must be maintained. You don’t need it to really feel such as you’re listening to the desk of contents. However you additionally don’t need it to really feel unfocused. And if you would like a dialogue of a guide, you need to get a dialogue of the guide.
None of those AI-generated podcasts are with out limitations. An AI-generated abstract isn’t good at detecting and reflecting on nuances within the unique writing. With NotebookLM, that clearly wasn’t underneath our management. With our personal toolchain, we may actually edit the script to mirror no matter we needed, however the voices themselves weren’t underneath our management and wouldn’t essentially observe the textual content’s lead. (It’s debatable that reflecting the nuances of a 250-page guide in a six-minute podcast is a dropping proposition.) Bias—a type of implied nuance—is an even bigger concern. Our first experiments with NotebookLM tended to have the feminine voice asking the questions, with the male voice offering the solutions, although that appeared to enhance over time. Our toolchain gave us management, as a result of we supplied the script. We gained’t declare that we had been unbiased—no person ought to make claims like that—however at the very least we managed how our digital individuals offered themselves.
Our experiments are completed; it’s time to point out you what we created. We’ve taken 5 books, generated brief podcasts summarizing every with each NotebookLM and our toolchain, and posted each units on oreilly.com and in our studying platform. We’ll be including extra books in 2025. Take heed to them—see what works for you. And please tell us what you suppose!
