Like nearly everybody, we have been impressed by the power of NotebookLM to generate podcasts: Two digital folks holding a dialogue. You may give it some hyperlinks, and it’ll generate a podcast based mostly on the hyperlinks. The podcasts have been fascinating and fascinating. However in addition they had some limitations.
The issue with NotebookLM is that, when 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 elective immediate to customise the dialog, however that single immediate doesn’t assist you to do a lot. Particularly, you possibly can’t inform it which matters to debate or in what order to debate them. You’ll be able to strive, 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’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 possibly can with ChatGPT or Gemini.
Study sooner. Dig deeper. See farther.
Can we do higher? Can we combine our data of books and expertise with AI’s capacity to summarize? We’ve argued (and can proceed to argue) that merely studying find out how to use AI isn’t sufficient; that you must learn to do one thing with AI that’s higher than what the AI may do by itself. It’s essential combine synthetic intelligence with human intelligence. To see what that might appear to be 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 guide, ensuring that each one the essential matters are lined.
- We use AI to assemble the chapter summaries right into a single abstract. This step basically 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 suitable matters 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 folks 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 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 incessantly 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 if that’s the case, what elements. A abstract helps them do this whereas saving time. It lets them uncover shortly whether or not the guide might 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 can 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 take heed to a podcast-style abstract the place the digital individuals 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. Sooner or later, the listener loses curiosity. We may feed a guide’s whole textual content right into a speech synthesis mannequin and get an audio model—we could but do this; it’s a product some folks need. However on the entire, we count on summaries to be minutes lengthy somewhat 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 state of affairs could also be a lot totally different.
What precisely do listeners count on from these podcasts? Do customers count on to study, or do they solely wish to discover out whether or not the guide has what they’re on the lookout for? 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 find out how 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 great overview of how Go may very well be used to handle the problems confronted by folks writing software program that runs within the cloud. However actually studying this materials requires taking a look at examples, writing code, and working towards—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 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 probably put them into follow. However once more, the podcast abstract is just an summary. To get all the worth and element, you want the guide. In a latest article, Ethan Mollick writes, “Asking for a abstract will not be the identical as studying for your self. Asking AI to unravel an issue for you will not be an efficient technique to study, even when it feels prefer it needs to be. To study one thing new, you will should 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. Relatively 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 offered 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 path. The longer NotebookLM podcasts, in distinction, can appear aimless, looping again round to choose up concepts they’ve already lined. To me, no less than, that looks like an essential level. Granted, utilizing the guide because the jumping-off level for a broader dialogue can also be helpful, and there’s a stability 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 must 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 authentic writing. With NotebookLM, that clearly wasn’t below our management. With our personal toolchain, we may actually edit the script to mirror no matter we needed, however the voices themselves weren’t below our management and wouldn’t essentially observe the textual content’s lead. (It’s controversial that reflecting the nuances of a 250-page guide in a six-minute podcast is a dropping proposition.) Bias—a form of implied nuance—is an even bigger challenge. 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 gained’t declare that we have been unbiased—no person ought to make claims like that—however no less than we managed how our digital folks 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!