Like nearly everybody, we had been impressed by the flexibility of NotebookLM to generate podcasts: Two digital individuals holding a dialogue. You may give it some hyperlinks, and it’ll generate a podcast primarily based on the hyperlinks. The podcasts had been fascinating and fascinating. However in addition they had some limitations.
The issue with NotebookLM is that, whilst you may give it a immediate, it largely does what it’s going to do. It generates a podcast with two voices—one male, one feminine—and offers you little management over the consequence. There’s an elective 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’ll be able to strive, but it surely 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’ll be able to’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.
Be taught sooner. Dig deeper. See farther.
Can we do higher? Can we combine our information of books and expertise with AI’s capacity to summarize? We’ve argued (and can proceed to argue) that merely studying tips on how to use AI isn’t sufficient; you might want to learn to do one thing with AI that’s higher than what the AI may do by itself. It is advisable to combine synthetic intelligence with human intelligence. To see what that might appear like in follow, we constructed our personal toolchain that provides us far more management over the outcomes. It’s a multistage pipeline:
- We use AI to generate a abstract for every chapter of a ebook, ensuring that every one the vital subjects are lined.
- We use AI to assemble the chapter summaries right into a single abstract. This step basically 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 suitable subjects in the suitable 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 individuals.
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 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 prospects continuously ask for summaries: summarize this ebook, summarize this chapter. They wish to discover the data they want. They wish to discover out whether or not they really want to learn the ebook—and in that case, what elements. A abstract helps them do this whereas saving time. It lets them uncover rapidly whether or not the ebook can be useful, and does so higher than the again cowl copy or a blurb on Amazon.
With that in thoughts, we needed to actually suppose by way of 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 ebook, my eyes (ears?) glazed over rapidly. It was a lot simpler to take heed to a podcast-style abstract the place the digital individuals 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 ebook’s total 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 count on summaries to be minutes lengthy reasonably than hours. I’d 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 totally 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 ebook has what they’re in search of? That depends 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 tips on how to program with AI. Summaries are helpful for presenting the important thing concepts introduced within the ebook: For instance, the summaries of Cloud Native Go gave an excellent overview of how Go might 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 practising—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 ebook 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 follow. However once more, the podcast abstract is barely an summary. To get all the worth and element, you want the ebook. 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 be taught, even when it feels prefer it must be. To be taught 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 enable the NotebookLM podcasts to be extra detailed, however in actuality that’s not what occurs. Slightly than discussing the ebook itself, NotebookLM tends to make use of the ebook as a leaping off level for a broader dialogue. The O’Reilly-generated podcasts are extra directed. They comply with the ebook’s construction as a result of we supplied a plan, a top level view, for the AI to comply with. The digital podcasters nonetheless specific enthusiasm, nonetheless herald concepts from different sources, however they’re headed in a path. The longer NotebookLM podcasts, in distinction, can appear aimless, looping again round to choose up concepts they’ve already lined. To me, at the very least, that seems like an vital level. Granted, utilizing the ebook 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 if you need a dialogue of a ebook, you must get a dialogue of the ebook.
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 wished, however the voices themselves weren’t underneath our management and wouldn’t essentially comply with the textual content’s lead. (It’s controversial that reflecting the nuances of a 250-page ebook in a six-minute podcast is a dropping proposition.) Bias—a sort of implied nuance—is a much bigger subject. 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 received’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 introduced themselves.
Our experiments are completed; it’s time to point out 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!
