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 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 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 provides you little management over the end result. There’s an optionally available immediate to customise the dialog, however that single immediate doesn’t help 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, nevertheless it gained’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.
Be taught sooner. Dig deeper. See farther.
Can we do higher? Can we combine our data of books and know-how with AI’s means to summarize? We’ve argued (and can proceed to argue) that merely studying how one can use AI isn’t sufficient; it is advisable to discover ways to do one thing with AI that’s higher than what the AI might do by itself. It is advisable to combine synthetic intelligence with human intelligence. To see what that may seem like in observe, 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 each one the necessary 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 best matters in the best 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 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 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 clients regularly ask for summaries: summarize this e book, summarize this chapter. They need to discover the knowledge they want. They need to discover out whether or not they actually need to learn the e book—and in that case, what elements. A abstract helps them try this whereas saving time. It lets them uncover shortly 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 by means of what essentially 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 shortly. 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 power {that a} single speaker didn’t have.
How lengthy ought to the abstract be? That’s an necessary query. Sooner or later, the listener loses curiosity. We might 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 anticipate summaries to be minutes lengthy moderately than hours. I’d hear for 10 minutes, possibly 30 if it’s a subject or a speaker that I discover fascinating. However I’m notably impatient once I take heed to podcasts, and I don’t have a commute or different downtime for listening. Your preferences and your state of affairs could also be a lot totally different.
What precisely do listeners anticipate from these podcasts? Do customers anticipate to study, or do they solely need to discover out whether or not the e book 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 how one can program with AI. Summaries are helpful for presenting the important thing concepts introduced within the e book: For instance, the summaries of Cloud Native Go gave a very good 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 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 seemingly with a e book like Facilitating Software program Structure, which is extra about ideas and concepts than code. Somebody might come away from the dialogue with some helpful concepts and presumably put them into observe. However once more, the podcast abstract is barely an outline. To get all the worth and element, you want the e book. In a current article, Ethan Mollick writes, “Asking for a abstract just isn’t the identical as studying for your self. Asking AI to resolve an issue for you just isn’t an efficient method to study, even when it feels prefer it ought to be. To study one thing new, you’re going to must do the studying and considering your self.”
One other distinction between the NotebookLM podcasts and ours could also be extra necessary. 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 might permit the NotebookLM podcasts to be extra detailed, however in actuality that’s not what occurs. Relatively 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 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 route. The longer NotebookLM podcasts, in distinction, can appear aimless, looping again round to select up concepts they’ve already lined. To me, a minimum of, that looks like an necessary level. Granted, utilizing the e book 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 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 below our management. With our personal toolchain, we might actually edit the script to mirror no matter we wished, however the voices themselves weren’t below our management and wouldn’t essentially comply with the textual content’s lead. (It’s debatable that reflecting the nuances of a 250-page e book in a six-minute podcast is a dropping proposition.) Bias—a form of implied nuance—is a much bigger problem. 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 one ought to make claims like that—however a minimum of 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 brief podcasts summarizing every with each NotebookLM and our toolchain, and posted each units on oreilly.com. We’ll be including extra books in 2025. Take heed to them—see what works for you. And please tell us what you suppose!
