Like nearly everybody, we have been impressed by the power of NotebookLM to generate podcasts: Two digital individuals holding a dialogue. You can provide it some hyperlinks, and it’ll generate a podcast primarily based on the hyperlinks. The podcasts have 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 consequence. There’s an non-obligatory immediate to customise the dialog, however that single immediate doesn’t will let you do a lot. Particularly, you possibly can’t inform it which matters to debate or in what order to debate them. You may attempt, however it received’t hear. It additionally isn’t conversational, which is one thing of a shock now that we’ve all gotten used to chatting with AIs. You may’t inform it to iterate by saying “That was good, however please generate a brand new model altering these particulars” like you possibly can with ChatGPT or Gemini.
Study sooner. Dig deeper. See farther.
Can we do higher? Can we combine our data of books and know-how with AI’s capacity to summarize? We’ve argued (and can proceed to argue) that merely studying learn how 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’s essential to combine synthetic intelligence with human intelligence. To see what that may appear to be in apply, 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 e book, ensuring that every one the essential matters are lined.
- We use AI to assemble the chapter summaries right into a single abstract. This step primarily offers 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 precise matters in the precise order. That is additionally a possibility to right 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 talk about one thing you wrote is fascinating—particularly since they sound genuinely and excited. Listening to the voices of nonexistent cyberpeople talk about your work makes you’re feeling such as you’re dwelling 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 prospects often ask for summaries: summarize this e book, summarize this chapter. They wish to discover the knowledge they want. They wish to discover out whether or not they really want to learn the e book—and in that case, what components. A abstract helps them try this whereas saving time. It lets them uncover rapidly whether or not the e book will likely 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 probably the most helpful abstract can be for our members. Ought to there be a single speaker or two? When a single synthesized voice summarized the e book, my eyes (ears?) glazed over rapidly. It was a lot simpler to take heed to a podcast-style abstract the place the digital contributors have 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 essential query. In some unspecified time in the future, the listener loses curiosity. We may feed a e book’s whole textual content right into a speech synthesis mannequin and get an audio model—we could but try this; it’s a product some individuals need. However on the entire, we count on summaries to be minutes lengthy reasonably than hours. I would hear for 10 minutes, perhaps 30 if it’s a subject or a speaker that I discover fascinating. However I’m notably impatient after I take heed 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 count on from these podcasts? Do customers count on to be taught, or do they solely wish to discover out whether or not the e book has what they’re in search of? That is dependent upon the subject. I can’t see somebody studying Go from a abstract—perhaps extra to the purpose, I don’t see somebody who’s fluent in Go studying learn how to program with AI. Summaries are helpful for presenting the important thing concepts offered within the e book: For instance, the summaries of Cloud Native Go gave a great overview of how Go might be used to deal with 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 probably with a e book 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 probably put them into apply. However once more, the podcast abstract is just an summary. To get all the worth and element, you want the e book. In a current article, Ethan Mollick writes, “Asking for a abstract is just not the identical as studying for your self. Asking AI to resolve an issue for you is just not an efficient strategy to be taught, even when it feels prefer it must be. To be taught one thing new, you will must do the studying and considering your self.”
One other distinction between the NotebookLM podcasts and ours could also be extra essential. 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. Fairly than discussing the e book itself, NotebookLM tends to make use of the e book as a leaping off level for a broader dialogue. The O’Reilly-generated podcasts are extra directed. They comply with the e book’s construction as a result of we offered a plan, a top level view, for the AI to comply with. 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 lined. To me, at the least, that looks like an essential level. Granted, utilizing the e book because the jumping-off level for a broader dialogue can 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 in order for you a dialogue of a e book, you must get a dialogue of the e book.
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 beneath our management. With our personal toolchain, we may definitely edit the script to replicate no matter we needed, however the voices themselves weren’t beneath our management and wouldn’t essentially comply with the textual content’s lead. (It’s controversial that reflecting the nuances of a 250-page e book in a six-minute podcast is a shedding proposition.) Bias—a form of implied nuance—is a much bigger situation. 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 offered the script. We received’t declare that we have been unbiased—no person ought to make claims like that—however at the least we managed how our digital individuals offered themselves.
Our experiments are completed; it’s time to indicate you what we created. We’ve taken 5 books, generated quick 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!