SPEAKER_01: So I wrote this book called The Ghost Map. And so I would often start off just to figure out where Bard was today. I'd be like, Hey, let's talk about Stephen Johnson's book, The Ghost Map. And so one time I did it and Bard came back and was like, Oh, I
would love to talk about that. That's another thrilling medical mystery set in 1854 London, uh, about the Dr. John Snow and his investigation. And it's a tale that weaves together a number of different themes. Well, and I finally got to the end of it. I was like, Oh, well, thank you very much. I'm actually the author of that book. And said, Oh, I am so excited to meet you, Mr. Johnson.
Well, I had any idea. I'm so sorry. I didn't recognize you. And I was like, that's fine. You, there's no way you could have recognized me. Right. It was just one of those moments where I was like, what is even going on?
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SPEAKER_02: guest today. I have known Steven Berlin Johnson for a long time. We met in the 90s, which was pretty much the best decade of the last century, I think maybe the 60s. Some people might make an argument but when we were kids, we were running around
New York City. It's called Silicon Alley back then. And he was running a website called feed a zine. And I had a zine my zine was print his was online, I was doing Silicon Alley reporter. And we were all trying to figure out what would happen with the internet. Steven went on to write 13 books. We're good ideas come from actually, I read that one really good. And I think he reads his own books on Audible. So you get to hear his voice. He's got a great podcast called American innovations. And now he's working at Google. And apparently, Steven, you're a developer. It's so nice to see you after
a year. I mean, really like, it's been a really like, but you're looking good.
SPEAKER_01: I'm very impressed to
SPEAKER_02: life is good, right? I mean, but here we are. You and I started on the internet before the web existed. We were doing online services, we were hanging out with people making CD roms, Voyager, Blender, whatever, it was like a really interesting time the internet happened. And somehow you wound up at Google building notebook LM.
SPEAKER_02: So I guess let's just start with that. What is Google notebook LM? And why are you building it? And why'd you take a gig at Google?
SPEAKER_01: Yeah, so it's, it's, it's in a weird way. It's like a 40 year story, because I had this long obsession with tools for thought.
I mean, when we first met, I was, I was thinking about this stuff. I really started when I was in college when hypercard came out for the Mac in 1988, which was kind of proto almost web like thing that you could organize information, you create these little stacks of cards, and you could kind of link between the yes, it had the first hyperlinks people had this beginning of hypertext, although it was not a network thing. Initially, it wasn't really connected to the internet at all. I think maybe a later version finally got wired up to the outer world.
SPEAKER_01: But I just had this glimpse of, oh, I could use software to help me think and create and have more interesting ideas and make connections and not just use it to kind of format my papers, you know, it was just little hint of that. I think a lot of us who get interested in technology gets this little taste of an idea, some formative point and the, and the tech isn't there yet, but you're like, I know someday I'll be able to do this. Um, so that was always in the back of my mind or some of the things that we did at feed, trying to experiment with different ways to use hypertext. That was one of our comp calling cards in the early days, creating new ways of kind of connecting ideas and print and, you know, through text.
SPEAKER_01: Um, and then I was always, you know, I wrote a lot about the tools that
I was using to write the books. You know, I wrote about the stool, Devin think that I used for a long time. I was, I am a big Scrivener fan. I've written about that.
And in, in my book, we're going to just come from, I talk a lot about how you create environments that allow you to think more creatively and, and, and, and I, so I talked about software in that context as well. So there was this kind of long history of this. And then of course, language models came out and, you know, we had, you know, in the early days of, you know, kind of behind the scenes, Google had Palm and Lambda and then of course, um, GPT three had come out and then, uh, and so in the spring of 2022, um, I got a kind of a cold email, um, from a guy
named Clay before who had started kind of rebooted labs at Google, and he had read a bunch of my books and had followed this kind of train of thought, uh, in
this interest in tools for thought. And he reached out and he said, Hey, do you, I want to talk to you.
And so we met with, this is actually the craziest stories that we've met in, project star line, which is Google's new, uh, kind of hologram technology.
SPEAKER_02: This is like a holodeck. You, you go into a phone booth, I think, and the other person's in a phone booth
in New York and I'm in San Francisco and it kind of projects the full 3d model of
SPEAKER_01:
the person. You're not wearing glasses. You're just sitting there. And so it was one of the, you know, I mean, this is, I say this, I said this
SPEAKER_01: before I joined Google. It was top five, impressive technology demos I've ever seen in my life. It's, it's, it's uncanny. Um, and, uh, during the conversation, you know, Clay kind of persuaded me to,
SPEAKER_01: to come be part, you said we can, we've got a team, just a small little team, but they're ready to kind of build something in this mode with language models. Like we can finally build thing you've been dreaming of your whole life. And why don't you come, you know, initially part-time to Google and just be in the room with us and help us create, this was a big part of the labs. Ethos was to like bring people from the outside, the early stage of these products and help develop them.
SPEAKER_01: And I thought, you know, that's a, that's pretty cool, interesting ideas. You know, you don't get an opportunity like this very often. And then I got out of the hologram. The project starlight meeting. And I thought he literally put me in a reality distortion field. Yes.
SPEAKER_02: Not a term of art. This is a reality. And just one more thing on starlight. So you sit and the other person is sitting across from you, like, and it
SPEAKER_02: feels like a table and there's depth to it, but it's some sort of a, is it a television screen or a projector? I mean, here's like an image of it, but how does it get the depth feeling?
SPEAKER_02: It's it's tracking the location of both of your, you know, retinos.
SPEAKER_01: I ended the screen. It's just, it looks like a regular television screen, but the screen is sending a different set of pixels to your left eye and to your right eye. And so every, as you move, so it only works currently, I believe with just, you know, one person on one side, one point person in the other, cause you, you have to send those pixels directly to that, but it creates a very powerful thing. And my favorite example of it is that I, uh, When I was testing it another time,
I wanted to kind of lean forward to see if the illusion goes away. If you lean forward. And so I lean forward and I had this very strong visceral feeling of, oh, I'm invading the space of this other person. I'm like close talking all my breath, all this stuff. And then I was like, wait a second. This person is like,
SPEAKER_02:
you're like leaning in for a kiss or like bad breath. I mean, it's like all the things you're not supposed to do in, you can kind of get too close. That's wild. Is it going to come out as a product ever? Where's it at?
SPEAKER_01: I shouldn't speak to you. I have to. Okay. Right. Yeah. It's in labs. That's enough. It's in labs.
SPEAKER_02: Yeah. All right.
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especially if you're writing a book in the veins of like yourself or, you know, Malcolm Gladwell, a lot of times my understanding is you're trying to take, disparate ideas, stories, and kind of pull them together. And Hey,
this has been your life's work feed. It was a feed before RSS existed. On hyperlinks, you were trying to tell stories. So when you click the link, you went somewhere and that was a, an epiphany or an emotion, or, you know, a change, something about your perspective when you, when you went to that destination, it was part of the fun of clicking on hyperlinks, which seems incredibly basic now, but it was incredibly mind blowing at the time.
But the process of writing these books takes years. It takes trying to put all this information together in some way. And, uh, that's what you've built with notebooks. So maybe you could just show it to us. And I remember Scribner was like this too, because you had kind of like post-it notes kind of all over the place and,
you know, architecture, you do a table of contents. There's been software like the brain that Jerry Minkowski was obsessed with. If you remember where you would just kind of make little neurons and still is apparently, I don't even know if the company exists,
SPEAKER_02: but I know his Jerry's brain exists somewhere on the internet.
SPEAKER_01: Watching your word that thing. It's incredible. Yeah. I mean, I think one of the things is before we kind of dive into it in detail, like one of the things that's important,
maybe it wasn't even fully clear to me when I started this is that what
I do with the books is, is just a more exaggerated version of something that I think a lot of people do. Anybody who works as kind of a knowledge worker in any discipline, which is that feeling where you're sitting down, you're trying to generate your ideas about something or kind of build the first draft of something and the information you need to, to pull off that job is scattered across multiple documents. And, you know, in my case, it might be scattered across like hundreds of documents, you know, but I just finished it has, you know, literally, I think something like 400 newspaper articles are part of the research for it. But, you know, even if it's 10 documents, that's a lot of information. And, you know, we just are constantly in this state where we're like, Oh, I've got 10 tabs open. I'm trying to figure out this thing. I'm trying to put together this blog post. I'm trying to put together this marketing plan. I'm trying to synthesize these ideas for a legal brief, whatever it is. And the information is kind of scattered everywhere. And we've never really, you know, so many people talk about that experience of like, you go through your tabs and you command F to try and find the thing you're looking for. Then you find it finally. And then you copy and paste it back into the other doc, which is in the other tab. And it's, it's incredibly laborious work.
Yes. It completely, it's so disruptive of your flow state, like your, your creative state, you're not thinking you're just doing this kind of menial,
SPEAKER_01: you know, archival labor, trying to like find the thing you're looking for and not just kind of thinking about it in writing. And there was a reason for that. It was just,
there wasn't any software that wasn't smart enough to kind of find and summarize and make sense of the meaning of documents until, and that's the key part, right? I mean, you could store documents,
SPEAKER_02:
you can retrieve documents, you could edit, cut and paste them and search Google's, you know, wheelhouse was incredible. And then, you know, organizing stuff, but it didn't understand the entire corpus and whether you're doing something like, I don't know, you're doing M&A and you got a document library of all the documents to close this M&A deal or an investment, or you're writing a book and you've got, you know, a hundred different articles and you're writing a biography of somebody and you
know, you've got the, all the, all the articles written about them. You can't keep it all in your head. That's just not how human, the human brain works, right? And you meet an interesting human, right? If you meet somebody super interesting, Jared diamond, or I don't know, pick somebody who's got a lot, a big brain who has synthesized a lot of this stuff for 30, 40, 50 years, when you talk to them and they put it together, that's kind of like the great interview, right? When we find a great interview and that's the one that blows everybody's brain, like, wow, this person is doing that in real time. The documents are, are their brain.
SPEAKER_01: Yeah, actually I'm glad you put it that way. Cause that's, that's the thing I've been trying to explain to people a little bit, which is one of the things that the notebook LM lets you do. And we can explain it more detail in a second, but it,
it gives you all these tools for basically having conversations with documents and, you know,
having an open-ended conversation with the book or a chapter of a book or a collection of quotes from books that you've assembled over the years, um, is just something that was not possible before. Like you could, if you were lucky enough to meet the author of a book, you could have a con, you know, if you met Jared diamond, you could have a conversation with him. And that would be kind of like having a conversation with this book. If you could meet an expert who had studied Jared diamond or a tutor or a great teacher, maybe have a conversation, but now like you can just upload these styles and you can let's show people, we're going to show people this. All right. So, um,
SPEAKER_01: I'm going to show you what I literally got so excited about this.
SPEAKER_02: Cause I was, I'm working on a project and I can't wait to show it to you and get your feedback, but, um, it's, this is a very mind blowing, uh,
SPEAKER_01: so fittingly. Okay. Here, let me share.
SPEAKER_02: If you're listening in the audience and you want to go find this, just go to YouTube and type in this week and startups, you'll find it. Um, and I'll try and describe it. We'll sports cast it. Yeah. We'll sports cast. Okay.
SPEAKER_01: So I thought I'd start appropriately enough where I've loaded up like five chapters from that book, where good ideas come from, um, as sources. Um, and so when you begin a notebook, LM, you know, one of the, the first things you do is you kind of define the documents that you want to work with in a particular notebook. So as we said, those could be, you know, a bunch of marketing documents. It could be research for a book. It could be legal briefs, whatever it is, it's relevant to the, to the job you're working on in each notebook right now, you can have 20 documents. They can be 200,000 words each. They can be PDFs. They can be docs. You can be copied text. We're going to add a bunch of formats as you can imagine. Um,
SPEAKER_02: those are your sources. Those are listed on the left here with these little document previews. Yeah. And that's, that's an important word.
SPEAKER_01:
The source has a very specific meaning here. And so once you load them into notebook LM, the AI is grounded in the information that is contained in those sources. So it takes about, I don't know, for loading these five chapters, I think it takes about like 12 seconds or something like that. And then at that point, it's like the AI, it's like notebook LM has become an instant expert in those five chapters, which is just astonishing to me that a computer can do this. Um, and one of the things that we've done, we spent a lot of time with inside of notebook LM is like setting up guardrails. So if you try to ask questions that are outside the boundary of the information that is contained in those documents, generally, it's not always perfect, but generally notebook LM will decline to answer. It will say, I'm sorry, I can't answer that question. It's not contained in the sources. Okay. You can see how that's a great use case in the classroom, right? So you can see as a teacher loads up a shared notebook with a bunch of documents that they, uh, that they're using for the syllabus for the class and the student can use those, but they can't kind of go outside the boundaries of that. So I loaded up here. Um, uh, you know, I think it's five chapters from where good ideas come from and I actually just did a question before, um, just to preload it. So there's a whole riff about nine 11 and the kind of intelligence failures in
SPEAKER_01: nine 11 in that book. And so I ask a question, you know, this is not keyword searching, right? This is the age of language models. You can ask these very sophisticated questions. So I ask what happened with the FBI and nine 11 and what was the significance of it? And it's going to answer that question based on not the general
significance of it. It's going to answer it based on the significance that I, you know, kind of endowed it with in writing this particular book. So it's going to be based on the subtleties of the interpretation of that, that I wrote in the original book. So this isn't going out to the open web and finding a New Yorker story or the
SPEAKER_02: Wikipedia and then hallucinating or a bunch of conspiracy theorists. This is, you know, you've got a narrow corpus here to really, um, return a tight summary. Yeah.
SPEAKER_01:
And what it ends up doing is it greatly reduces the hallucination risk. You, I mean, you will still see, you know, occasional mistakes or sometimes it will just, it's more like it gets confused. And sometimes if the information is a little bit confusing, but reduces it significantly. Um, and you get these, I mean, this is just amazing. This is by the way, this part of it, we're kind of rolling over to having everything be powered by Gemini pro, which has been fantastic in our early testing of it. And so this is an answer that Gemini pro returned. Um, it formats it in this really nice way. Like it has kind of bold text for the key terms. It gives you nice little bullet points. It does this, you know, really elegant kind of overview of, uh, of the situation.
It's nice kind of presented and just kind of a nice way. We spent a lot of time trying to get that style, right? Like it's, it's, it's very much like editorial product, you know, this would be the equivalent of if you had a great writer and you said, hey,
SPEAKER_02:
summarize this very long story, uh, or series of books, you know, in our magazine or zine, so that a casual user can, you know, within five or 10 minutes of reading 400, 500 words, really understand it, uh, and not have to do a lot of work, a lot of heavy lifting. So it says here, Hey, you got the Phoenix memo, you've got something that's overlooked. So you're doing headings, you're putting them in a bullet points. You're making it easy for people to digest knowledge and stuff. So somehow you've trained the model that this is an academic
synthesizing world, uh, in which you're working. Yeah.
SPEAKER_01: Yeah. And so then, so, so that's really useful, but then with, with every answer, we also give you citations. And so you can always, in a sense, check one, you can just double check that, you know, what, what did the model use to come up with this thing? So if I mouse over these citations, I can see these are the original quotes from my book that it used to be, that it used to piece this together. And sometimes you use that just to fact check, but sometimes that's actually what you want to do. You want to actually read the original text, and this is just a faster way to get to it. And so you can click on any of these and it immediately opens the original source and takes you to the point in the original document. Um, so you can actually like read it originally. So your ability to just kind of navigate through, I mean, I, I'm not going to say I don't have it on this computer, but I have on my main Google computer, I have a collection of quotes that I've been assembling, like digital quotes from books that I've read for the last 20 years. It's like 1.3 million words of quotes from like my, my reading history of the last 20 years. And I can do, I can literally just sit down and be like, Hey, what are some interesting things about dolphins? Tell me about love. It'll find all these things. And, and, and then I can zip immediately to kind of the original passage and see it. And that that's the speed with which you can move through information and have it summarized for you is just incredibly powerful. Now, the other thing that's, that's, you know, we were talking about having a conversation with the document.
SPEAKER_01: So one of the things that we found when we started doing early testing and this is summer, um, at a few schools around the country, a few colleges around the country, um,
was that people didn't know really how to ask questions. You know,
they were, they would sit down and they were like, what do I say? You know, you and I are trained as journalists. And so we know, we know how to ask questions, you know, we know how to kind of think in that, you know, uh, dialogue kind of mode, but that doesn't, it's not something that's actually taught very, very often. And so what we started to do is actually to use the model to constantly give you suggested questions based on the content of the source and based on what you've just talked about. And so you can see right below here, there's a set of suggested questions here. Like what was the central claim of the Phoenix memo and why did it fail to prevent the attacks? How did the automated case support system contribute to the failure of the FBI to connect the Phoenix memo? Uh, so that's a pretty good one. Let's see what happens. I'm going to click on this. We're doing we're now we're live demo mode here.
SPEAKER_02: Why didn't anybody pick up that the terrorists didn't want to learn how to land
SPEAKER_02: the planes. It was like a little bit of a red flag. Like, are you sure you don't want to learn how to land a plane? Nope.
SPEAKER_02: Just how do I get it up there and keep it straight? Hmm.
SPEAKER_01: And this is such a good answer, man. I am still astonished at this, that this is possible. So yeah, it's talking about basically there was this automated case support system at the FBI and they were just, it was basically designed. It was the opposite of notebook. I love actually, now that I'm thinking about it, this is a really good example because they had all this information and they were, they were unable to connect the dots. So the software here has has summarized all that stuff. It's figured out, you know, it gives me a bunch of different details from that story and then it gives me a nice little summary at the end. And then again, I go look at the citations, like that, that's the,
SPEAKER_01:
the kind of the core kind of,
kind of very beginning of this project, but anything you find that is interesting. So you're, you're engaged in this and you're exploring, you're asking questions, you're following, you're clicking on the followup questions. You're following citations. You find something interesting, you just pin it. And then you've got this kind of note board space.
This looks great on a, on a big screen, by the way. It's incredible. Yeah. I have a wide screen and I was doing this example.
SPEAKER_01:
So then you have all these notes that you can go and refer to. So you're able to just constantly like grab things that are interesting and
what's coming, uh, like any day now, um, you're going to be able to select a note or a set of notes, and we're going to automatically give you a set of options when you select those notes. And those options will be things like create a study guide or
convert into a thematic outline or suggest related ideas for my sources. And so what are my missing new sources is the one I was looking for.
SPEAKER_01: That is, that is not short term coming out yet, but that is definitely where we're headed. I mean, for sure. I mean, it's Google here, you know, your work is great. Um, if it said,
SPEAKER_02:
you know, Hey, I found three other sources. Would you like to add the actual nine 11 commission report, which is a giant document to this, would you like to add this Amazon series, uh, a fictional series to your, to, or the, or the script from it, you know, now all of a sudden it's this research assistant on either side of you
answering the questions. And this is how documentary films are made, right. Or, uh, or a book like yours or Malcolm's where you're trying to pull together themes that, you know, only a human previously could make these connections. And when you make those connections and they, and they spark something in you as an author, we want to share those, right. And we want to have that spark happening in your brain. Right.
And that's really the magic of being an author in my mind is when you can get into, when you can, and Stephen King wrote this, you ever read on writing his book, his autobiography? Yeah. It's incredible. I mean, there's many reasons it's incredible.
SPEAKER_02: Perhaps one of the best is that he wrote Cujo while doing copious amounts of cocaine with his nose bleeding and just that kind of tracks, right? Yeah. I think it's probably makes a lot of sense.
SPEAKER_01: It probably feels like it was powered by cocaine, but he said, you know,
SPEAKER_02:
teleportation, what we're writing, great writing is you can basically teleport through time and space, whatever you write just magically appears in the other person's brain. So if you, if you were to describe your desk right now and the microphone, et cetera,
SPEAKER_02: and somebody read it a hundred years from now, all of a sudden you would have manifested in their brain, this image.
And that is just, once you look at writing that way, that you're literally creating a vision in somebody else's brain, you really understand the power of it and why it's important in the world. And then this just takes it to a whole nother level that most people are not
even thinking about right now. Well, that's, it's a great point.
SPEAKER_01:
And it reminds me of something, um, you know, Tiago Forte,
the second brain, I wrote how to build a second brain, really interesting guy. We've been talking to him about notebook because it's right in his alley. And, uh, here's the thing about like taking notes and capturing things that are
SPEAKER_01: interesting to you and kind of storing them is this way of, you know, you're trying to do it to like send a message to some future version of yourself who's going to need this thing in five years. They don't know why, but like this piece of information. And so, you know, I, I haven't kind of fully done that,
SPEAKER_01: just the kind of cleaning up of my documents to do this, but you know, by the end of the year, early next year, I'm going to have one notebook that's effectively like all the important things I've ever read and all the important things that I've ever written. Are going to be live in this one notebook and my ability then to find that idea that I jotted down, you know, in 2007, that is so long gone, you know, to my actual physical brains memory, but that, you know, the combination of the software, um, and my curating all the information initially is gonna, is gonna enable me to find those things again. Um, and it really evokes, you know,
SPEAKER_02: like where you all always wind up in these discussions is consciousness,
right? Like what is our consciousness, but a collection of the things we've read, we've written, we've spoken, we've experienced. And so now we start looking at what is actually being created with these LLMs. And I think it's either explaining to us, um, how basic our brains are in some ways or how incredibly complex they are, because if you did have all of your writing, every instant message, every email, and then you start thinking, well, every song and movie I've consumed, every book I've consumed, everything I've written, you've now got almost your consciousness, you know,
in, in a book here. And that gets really trippy because it's a perfect consciousness as opposed to ours, which has bias or something. Well, we actually become conscious of something. We know this because in different scientific studies, if they play some music or, you know,
SPEAKER_02: give you a scent or take you to a location in your high school, all kinds of other memories come out. Yeah.
SPEAKER_01: But this is like becoming perfect memory to the point about, yeah.
SPEAKER_01: We're trying to, it was interesting. Yeah. I think, I think you saw that Steven Levy piece that he wrote about it and in wired and, and he, I think he had gone into it thinking it was just going to be a really good search engine for his own stuff. And he was like, it turns out it has a little bit of its own opinion about things. Like it's steering you towards certain ideas. And, you know, we've spent a lot of time trying to figure out like, what is the right balance here? Because you want the your kind of assistant to be smart and help you develop your ideas, but you also want it to be, you know, to play second fiddle to your ideas, your own thinking. One of the things that's interesting about it is in general, we have tried to create a voice for the, for the AI that doesn't have a first person voice. So it's not trying to be your buddy, right? It's just leaving you the information. It's not, we're like, we don't want it to pretend to be a person. It just should be somehow just an incredible service that finds the information you're looking for and summarizes it and doesn't. So it will every now and then, it'll sneak in, but it won't generally say things like, sure, I can help you with that. That kind of stuff.
SPEAKER_00:
SPEAKER_02:
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SPEAKER_01: I want to show you one other thing and then I want to see your, um, you've been doing with it, but I just want to make sure that, you know, it's clear that this also has, it doesn't always have to be, um,
such a highbrow, uh, use case. So this is something that occurred to us very late in the process where we were like getting ready to launch and we were like, wait, we can, we've written all these help documents for using notebook LM.
What happens if you just load those up as sources into notebook and it becomes an instant expert in how to use notebook, which, you know, it turns out to be a really, just as a, as a kind of onboarding guide as a help desk, it's so powerful. So anybody could, whatever kind of service you are a company you are, you can just load up the documents that describe how your system works and then you can share notebooks with other people. And so then they can ask questions and engage. And one of the things I like, so you can ask these kinds of prosaic questions, like how do you upload a PDF to notebook LM? It'll be good. But generally, this is, um, uh, I'm a lawyer. Um,
SPEAKER_01: how could I use notebook LM? So we don't have any language in there about like,
or is using notebook LM. All we just have is a description of the product. So generally this is generally, this generates a pretty interesting answer. Like it kind of understands, yeah, this is great.
So it understands how the software works and it understands generally how
lawyers work. And so it's able to synthesize those two things and come up with this actual kind of precise and you can actually dive in deeper and say, actually, can you give me a step by step, um, you know,
SPEAKER_01: a set of examples for how I could use it to draft legal documents or something like that. Right. You just go down the rabbit hole from there.
SPEAKER_02:
It is really interesting. Like, um, if you were to think about
SPEAKER_02: instances where you need a large corpus of documents. And in fact, when we were talking about the Phoenix memo and like the failings of that computer system, kind of alluded to the fact that, Hey, you know,
if you're trying to do a case, you're trying to solve some puzzle in the world, which is what law enforcement does or mathematicians, et cetera. You know,
there are going to be these examples of the language models being able to, if given the right corpus of information and given the right prompts and somebody actually reading the result and checking the work, we're going to solve a lot of mysteries, you know, like just like these crazy people who get obsessed with, um,
I don't know if you've seen these lunatics, like on subreddits and excite like, and they make documentaries about them. I think there was one, like something about cats or something, but there are these people who don't mess with cats, I think it's the name of the documentary, but there are these people who are like stay at home crime hunters, like detectives from their keyboards, and they find a case of a missing person or the, you know, somebody who was murdered and they are inspired by other podcasts about people being murdered to then go solve it and they become online salutes. And it's usually they don't find anything and they just are twisting, uh, you know, reality. Um,
yeah, it creates, sometimes they do figure stuff out.
SPEAKER_01:
Well, you know, it reminds me of what you said at the beginning of the conversation about how mesmerizing it was to click on a link in 1994, right? You're like, I clicked on this blue word and I was taken from one server to another's, to a completely different document on, you know, on the other side of the world. And it just seemed mind blowing. And it was just fun to, in those early days, just clicking around the web was kind of just fun as an adventure. And yeah, I think that there's something that is like that, that I,
I'm seeing again in notebook LM in part because of the suggested questions. I mean, that's just, that has that same feel of like you jump into a big archive of information, an opening question, or you, you know, we'll actually recommend topics that you can explore right out of the gate, but, and then you just kind of ride those suggested questions for awhile and you get a re it's a great way to kind of engage with, you know, kind of initially figure out what's in this text and discover new things. And I could imagine somebody who was going down a rabbit hole of some, you know, complex crime, you know, investigation and had whatever 30 PDFs of, of evidence there that that could be particularly
SPEAKER_02:
let me show you what I did. This is related to the pod. So, you know, I'm trying to, you know, I've been doing this pod for like 1800 episodes and like sometimes these things have to remain interesting to the host, right? Like the art has to be interesting to the person producing it or else it's like, it just becomes a chore. And I was like, you know, I just love movies. And I love business stories. So let's just do like this business breakdowns where we find, you know, a great movie about business. And then we kind of do like a bullet, you know,
SPEAKER_02: either timeline kind of approach or the key lessons from a movie or from a success story. And so your mind is already racing with the possibilities of movies to do because you're a well read, I assume. So Ray Kroc, who created McDonald's wrote a great book called the founder. And he was an insane founder, like pretty sharp elbow and saying,
SPEAKER_02: and I know about this because I'm a big fan of Mark Knopfler, the lead singer of dire straits. And when he went into his solo career, he wrote a great song called boom. Like that is probably one of my favorite songs of his solo career. And he had read Ray Kroc's grinding it out biography and wrote a song based on it. The guy who made the movie, the founder with Michael Keaton had heard Mark Knopfler song,
found the source material of the book, and then convinced Michael Keaton to make a movie. The founder is an incredible movie that nobody remembers. Have you ever seen it?
SPEAKER_01: Never seen it. And you know, I assume you haven't read Ray Kroc's,
SPEAKER_02: I have not. Right. So this is like, absurd shit, but I read it and it spoke to me.
SPEAKER_02: Now I got to it, Mark Knopfler song, the movie,
SPEAKER_02:
then I found out the movie was based on a book. Okay, here we go. Right. Cause I went down the rabbit hole. So then I was like, okay, I'm doing this episode will come out after this episode comes out where we're going to break down the story of Ray Kroc through the
song by Mark Knopfler, and the movie and the book, really interesting pieces of source material. So then I went out and I know,
SPEAKER_02: so I paid for all this stuff. So anybody who wants to give me a hard time about copyright, I found what I assume is the public domain version of a PDF of the script of the founder. I found a public PDF of the book,
SPEAKER_02: grinding it out, which I have three different versions of I've paid for it. So please do not sue me. If this is your book, I assume I'm legally allowed to do this. And then I found a YouTube video. Now the YouTube video, I thought I could just drop it in there since it's Google, but you guys are version 1.0. Not yet. But I know there's transcripts. So I, I opened the transcript and I very awkwardly had to, you know,
SPEAKER_02: drag and drop and cut that transcript in here. So I got my thing. And then I started asking things, Hey, what were the key moments, you know, in the history of McDonald's? And then I said, well,
what page is that in the screenplay? Right. And so I haven't gotten to down things. But you know, I also asked what, what is Ray Kroc consider his, the key to his success. Now, I don't know where this is coming in from citation wise if, but he, you know, he said it's perseverance and determination, yada, yada, like you would expect. And so I'm just started my adventure, but there's probably 20 other documents that I've written. I'm going to find the Harvard case study on this and the Penn case study on it. And you have to put the song lyrics in.
SPEAKER_02: What are the, let's see, what are the suggested questions actually?
SPEAKER_01: So what were the three decisions the McDonald brothers made regarding the design of their restaurant that made it stand out from other drive-ins? Oh, I know this. It didn't have seating, right? It was a counter.
SPEAKER_02:
So I know it didn't have seating. The limited menu. That's right. And the streamlined process and the unique building design is there. The brothers restaurant was in a building with a red and white sign was I
catching. So I guess I didn't, the restaurant was a standout from other drive-ins interesting. Yeah. So look at the citations too. So just coming from the script,
you'll see. So when you roll over, yeah,
SPEAKER_01:
you can see it's coming from the grinding it out, which is his book.
SPEAKER_01: Yeah. Yeah. So, I mean, I, you know what I, I'm not sure if I'm going to be able to get the book, so I mean, I, you know what, I I've only been in this for like a half hour.
SPEAKER_02:
I was like, most of my, if you do a listening lab with me, like the hardest thing I had was getting information in. And I tried to do this before I found out about notebook LM. When I saw Steven Levy story on you and said, Hey, I know, I know, Steve, I know you, Steven. And I said, get them on the pod. I want to talk to him about this. I just couldn't figure out how to get
stuff into it. That was my blocker. And then I was like, Oh, wait a second. And I, then I took the same PDF and I tried to put it in Claude and Claude was like, yeah, that's way too big. And I was like,
SPEAKER_02: ah, God damn it. Um, right now, by the way, for folks is,
SPEAKER_01: is either a simply formatted PDF, or it's just kind of a straight text thing. If you, if you have a PDF of a book, you know, you have all those complicated things where like, it'll grab the like headers and the page numbers and footnotes, and it'll be a little confused. Um, anything that's in doc format is going to be good. Um, but we're going to, you know, we're going to get better and better. And you know, the other thing is that Gemini is, is natively multimodal. So there's, there's all this incredible stuff that we're going to be able to do with images as well. I mean, right now it's all text, but we were just doing a bunch of experiments the other night with images and it's, it's astonishingly good.
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SPEAKER_02: you're an author? If I asked it, if I had purchased your book, to allow me to talk to your book as an author, you'd be cool with that, I think. Yeah, well, I certainly would be. I would be as well. I
SPEAKER_01: would want people to do that. Yeah. You know, one of the things that we like to recommend people do is, you know, you can use if
you buy an ebook on the Kindle or the playbook store, you know, you're allowed to save quotations from those books. So as you read, you can save quotations and they're wonderful services like read wise, that will allow you to export those quotes to a doc. And then you can bring those in. So you wouldn't have the whole book there. But actually, sometimes you don't want the whole book, you want the passages, the most important ones for you. And then that's what basically I've been doing with my my set of quotations. So that's a great way to do it. But I, but I agree. I think, you know, there's a logical place we could end up where if you buy an ebook, you could read it in an ebook reader or you could read it inside of notebook LM. I would love I would love that future. Yeah, I think we I wonder how the publishers
SPEAKER_02: are, you know, Harper's or whoever your publisher is, is like thinking about this. Because I feel like you could charge an extra 10 bucks, right for a digital book to allow it to be to talk to it, you know, and query it. Or it could just be
like, it's almost like a new format. Like, I wonder if Apple they must, Apple and Amazon must be thinking about this as a feature. You are you're thinking very much along the lines that
SPEAKER_01: I've been thinking. So sorry, I would hope that we would make some progress on that front next year. But the other thing that that's really worth pointing out to people is this is important whether you're an author or not is in terms of privacy and
SPEAKER_01: security, we are not training the model on the information you upload in those sources, right? So the model has been pre trained. What we are doing the easiest way to put it is like we're putting if you if you know the AI languages you do, we're putting the information from your sources briefly into the context window of the model. And asking questions based on that, for people who don't know what that means, it means we're basically showing the model, we're giving your information to the model short term memory. And the second you end your conversation, it remembers nothing. And we do we do no training based on the on the information of the document. So that means you can use it with, you know, private documents, corporate documents, or a rights holder can feel confident that if somebody is taking some quotes from a book that they read that they purchased, if that information is not somehow getting into the training data for the models. It's a key issue. Yeah, I think it's a key issue for authors. I
SPEAKER_02:
was really flabbergasted by open AI approach and you work at
Google. So I'm not gonna have you comment on it. Tell you my opinion, that they just took open crawl and some other, you
know, corpuses and train their model on it. And they know full well that they're that's just taking the open web and that the open web has all kinds of stuff that hasn't been cleared, and then they train the model on it. Now, it's six, six of my
SPEAKER_01: books were in there. Right now, as far as I'm concerned, they
SPEAKER_02: owe you a licensing fee. And you know, the books are in there, because when they you ask very specific questions, it's going to give you the answers. And in the earlier versions of chat GPT, you could ask it is, you know, Stephen Berlin Johnson's
books in here, and it would actually tell you, yes, I've got them right here. Have in your case, did they subsequently
take them out? And what do you think of these, just broadly speaking, the rights of authors in terms of model training? Because it feels profoundly unfair to me.
SPEAKER_01: Yeah, I, I probably should be delicate about this. Oh, right,
because of where you were, you know, but, but I do think it does feel like, one is an author, I want people to be using language models with my work. I think that's a way that people are going to be exploring information. So I'm very comfortable with people, if they do it in a proper way, they're paid for the book, they should be able to interact with a model. And I do think that yeah, if models are being
SPEAKER_01: trained on copy, you know, written material that there's,
there needs to be something that's going on with the rights holders there. But I haven't spent that much time thinking about that side of it, because I've just been so immersed in the product side of it. I mean, in some cases, it's super obvious, right? Like, I
SPEAKER_02:
was asking chat GPT to make me like a Jedi Knight Bulldog. And it was like, Sure, here's a Jedi. And I was like, Okay, now make me Darth Vader as a bulldog. And it was like, Sorry, I can't do that because our content policy. And I was like, Okay, I get it. You understand, like a Jedi is a category, but not a character. So they must have taken the entire Disney corpus and said, for Dolly, let's not make images of Marvel characters. Let's not kick the number one rights holder, who is the most litigious and thoughtful and, you know about this. And so it declines to make that. And so then I just said, I'll make a Sith Lord. And it literally made me Darth Vader as a bulldog. I talk about, you know, searching authors work. So in
SPEAKER_01: the early days, when Bard first came out, internally, I was kind of at home, while my rest of my family was skiing, this was like a year ago. And I was just like, Okay, this is our new model, I got to test it, I figured out everything. And so one things I would do like bards kind of intelligence in those days would go up and down as they were retraining and things like that. And so variable, variable, like a standard kind of question that I've asked. So I would often ask, so I wrote this book called The Ghost Map. And so I would often start off just to figure out where Bard was today. I'd be like, hey, let's talk about Stephen Johnson's book, The Ghost Map. And so one time I did
SPEAKER_01: it and Bard came back, I was like, Oh, I would love to talk about that. That's a thrilling medical mystery set in 1854 London about the Dr. John Snow and his investigation. And it's a tale that weaves together a number of different themes. And I finally got to the end of it. I was like, Oh, well, thank you very much. I'm actually the author of that book. And then said, Oh, I am so excited to meet you, Mr. Johnson. I'm so sorry, I didn't
recognize you. And I was like, yes, I am you. There's no way you could have recognized. It's just one of those moments where I was like, what is even going on?
SPEAKER_02: So this feels to me, like part of Google Docs eventually, what how do how does a lab product? Where does it go from here?
Because it feels like a product. And listen, I'm tipping the spear for you and use cases. So I'm wanting to pay 199 bucks a year for it, to get like the feature set or whatever I pay 20 bucks a month for it, because I would use it and my production team would use it for this podcast and things we do because we frequently have a guest. And I would have taken interviews with you I would and I would have said, Hey, what are the most interesting things? He's been asked in other interviews, right? And I would want to pull in podcasts, etc. So for me, it's a great paid product. But how do you think about taking it from a laboratory experiment and productizing it? What? How does that work at Google? Or how do you think about it? I mean,
SPEAKER_01: genuinely, it's not a cop out to say we don't really know. Because this iteration of labs is is a new one, right? It's a new, new labs, now run by wonderful guy named Josh Woodward, who was also instrumental in bringing me in. And we have kind of graduated up to a, you know, a public launch in the US. We're still built as an experiment, although barred is still built as an experiment to I believe. And we're trying
to figure out, you know, what's working, what's not, what actually the path is, if if people like it as much as we think people will like it, particularly as we expanded, we make it easier to bring in sources, make it easier to discover sources, all the things we talked about. I, you know, I,
I don't know what becomes of it. Okay. It's in a nice spot, I think, where it does something different than what Docs does. And or slides does, you know, we're for the, you know, almost
SPEAKER_01: all of our features are like helping you think and understand. And there's almost no like our formatting is like bold and italics like that. Yeah, you know, write a note, that's all you have, you know, enjoy it. So it's not at all about creating the final product at all. But it is a place where you can synthesize across lots of different docs. And so I think it, you know, it complements Google's existing offerings, whether it graduates up into some more elevated, I don't know,
SPEAKER_02:
it's always a challenge with big companies, you can build these really amazing things. And then you have to figure out how they live post, you know, in a laboratory does feel to me like this also, based on your UX feels like if I put this on a
giant screen in a conference room, you know, with the way the post it notes are kind of designed the notes and the material, we could all be sitting in a conference room, working on a book together, or working on a documentary series, or the writers on The Simpsons could be doing a retrospective of the last you know, 10 seasons. And wow, this could be quite, you know, powerful to be on a giant lightboard and moving it around like a minority report and asking a question. It seems like a really good brainstorming tool is I guess what I'm getting at in a lightboard sense. Yeah, shared notebooks we've just
SPEAKER_01:
started to explore like you can share a kind of read only notebook where you can just ask questions, which is great for like the help desk kind of use case. Right? Don't screw with the source material. Yeah. And then you can share one where you can write your own notes and do other stuff. We don't have a lot of the technology is very basic right now. Like everybody's note seemed to be authored by the same person. You know, we're just getting started. But But yeah, I agree that that's, that's really useful. I mean, I keep thinking about is like, what am I like, are these drafts of things that are sitting on that board? Like, and the other thing that we're going to be able to do is like, you can grab a bunch of notes and combine them into a single note. And so there's that process. I think I'm gonna pin a bunch of different things. This is about to roll out. This isn't live yet. You didn't miss anything. So you can be in this mode where you're like, okay, I'm going to pin a bunch of ideas up there. And now I'm going to kind of consolidate them into a single note. And then I'll use some of these tools to maybe turn it into an outline or convert it into whatever format I want. So I think there's a lot of stuff that's going to start to happen
SPEAKER_00:
SPEAKER_01: as people use that interface. It's really a new UI. Like it's not it doesn't quite look like anything else that's that's out there. And that was kind of our thought is like, there's an opportunity now to create a just like we needed to create a new thing called web browsers, because this thing called hypertext and HTTP, it needed a new software category. Yeah, we think that language models are going to necessitate the same kind of interface revolution. So this is our first stab at it, which is just so it's just so fun. Yeah, no, it's super my blog. Because if you think about it, like, there's a
SPEAKER_02: source material. There's the queries and the questions you've
asked it. And then there is, well, what do I do with that afterwards? Like, what and how does the rest of the world interface with the name? You're right. Some people might just want to ask questions like you. And it just becomes like, hey, we're talking to Shakespeare about all of his plays, or we're just, you know, have every Simpsons episode here. And we're just looking for funny moments that have to do with donuts. But that it could also become a script, it could also become an outline, it could become project management. So the output could be the LLM, you tell the LM, I want to make this into a podcast
episode, that's one hour long, with two hosts. And it's like, Okay, I get an idea. That's going to be about this many words. And yeah, just tell us which 20 things are the most interesting, that we should talk about, right, make this into a script, make this into that, you know, you know, a summary or
something. I guess while we wrap here, I'm curious, you know, knowing what you know, and I know you've studied all the all these different technology changes. I remember listening to your audio book. We talked about the Mendicis and glass.
SPEAKER_01: We got to know. Yeah, how you got to know? Yeah, it's really
SPEAKER_02: good. Yeah. And so you've seen these changes and inflection
SPEAKER_01:
SPEAKER_02: points. When you look at this one language model, specifically this ability to ask questions, and there's a recency effect here, obviously, like, yeah, we're pretty enamored with this
SPEAKER_02: right now. We're enamored and confused. And yeah, but where do
you think this one winds up? Because it does feel like it's building and building and building from the open internet, broadband, unlimited storage, everything just to kind of to
this moment, right? And then also, consumers and customers
putting so much data into the internet, like, when we look
back on this, like, what, what, what, what equals this in terms
of potential impact? Or what feels like they're kind of two
SPEAKER_01: questions really, in a sense, like, where does it end up? Is it is a really big question? Like, where does it end up in 20
years or something is a huge question that I'm probably not qualified to answer. But in terms of, you know, this existing technology as it is today, you know, if you imagine, you know, you know, somewhat similar incremental improvements
SPEAKER_01: over the next three or four years that we've seen over the last two years, which have not been incremental, they've been more than incremental, then I think it has to be considered
that, you know, for me, the single most important technological revolution of my lifetime, I mean, you know, I would say it will exceed I would have said before that it was the personal computer and the graphic interface. And the web and mobile were the, you know, kind of the biggest ones. And this seems like it is ultimately going to be more important. But one of the points that I that I tried to make when I, and I wrote a Times Magazine piece about mostly about GPT-3, but
about language models before I came to Google. And so this was like April of 2022. And the point I was trying to make is
like, you can be agnostic to the question of whether language models are going to lead to, you know, artificial general intelligence or some, you know, or true understanding or consciousness or all these kinds of things. And I suspect language models as themselves will not lead to that kind of breakthrough. And still think that they are enormously
significant. And at once, once the computer is able to manipulate and summarize kind of meaning, and make associations on a level of semantics and not just like find, you know, text, but actually be able to, you know, to talk to you about like, okay, I've taken your idea, and I've summarized it so that a five year old can understand it, or I've taken your idea, and I've connected to these other ideas. And I made a little, you know, analogy here between these two different eyes. Once you do that, there's
just a whole host of things that no computer in the world could do three years ago, that now, you know, anybody can do
SPEAKER_01: with with the web connection, and soon enough, we'll be able to do, you know, on device on their phone. And that just unlocks so many doors of possibility that it doesn't really matter whether they ultimately become sentient or become true rivals to human intellect, they're just going to be enormously useful. And that's what we that's, that's
SPEAKER_01: what really, you know, when I got that call from labs, I was like, Yes, this is the time to build this thing. And this is a great, you know, the opportunity is just so fantastic. And it also, and I want to use it myself, you know, so I've animated by this like desire to build the thing I want to use. I think when you when you and I have been at this
SPEAKER_02:
now for, gosh, 30 years, and like yours feels like it. But
SPEAKER_02: you know, if you're if you've been assessing technology for three or four decades, right, since we're teenagers, looking at PCs and dial up services, you know, you you kind of get a sense for like, yeah, this is a big one, this one, it in, I wonder how you think of, you know, putting aside, you know,
crossing different valleys, etc. But just super intelligence. How do we even define what super intelligence is? I mean, smarter than any human you've ever met more than any human who's ever lived. It's pretty clear that this is on that trajectory already. And it doesn't feel like it's very far
off than being smarter than any human who's lived right.
SPEAKER_01: You know, take it to like the simplest example of that I gave in those demos, like the the how to use notebook, LM, you know,
example notebook we have. So how long would it take a human being to get enough expertise about how to use notebook LM having never seen it before, so that they could explain how it could be used to a lawyer or to anybody else who came along and said, Hey, I'm a whatever, I'm a marketing director, like, how can I use this product? Like, yeah, it would have to read, you know, they came up with zero knowledge, they would have to read through the documentation, you know, they'd have to probably mess around with it a little bit, it would take them, I don't know, hundreds an hour, maybe two hours, maybe 10 hours, like just to understand to be able to answer that question confidently and quickly to to someone asking it for notebook
LM that I mean, that is a kind of intelligence, right to understand a system, be able to improvise an answer based on kind of a novel new input of like, I'm a lawyer, I'm a marketing director, I'm whatever I am. And, you know, so a human that might take somewhere between an hour and 10 hours, probably. And for notebook LM, it takes 10 seconds. That's how I would just those dark. Oh, I don't know what that is. But it's something when you hear people say it's a parlor trick.
SPEAKER_02:
SPEAKER_02: Yeah, language models. What's your take on that? Just broadly speaking, like, what are they missing? And what are they getting right? In some cases? Like, why does it feel like that sometimes to people? Yeah, I was working on a piece a
SPEAKER_01:
little bit of kind of notes to myself about this, I might write someday looking back on this whole experience, like, part of it is, is that very fraught word understanding, like when you say, you know, if you say notebook LM understands x, because it's read these documents, it's an expert in x, because it's read these documents, you know, it's processed these documents, that causes a certain, you know,
subset of people who think a lot about AI to really object. And they say, No, it does not understand it. It's just statistics. It's just a stochastic parrot. It's just, you know, it's just predicting the next word, it doesn't understand anything. You know, on some level, that is kind of
true. And if by understanding you mean, it is conscious of it, or it is having an internal sentient experience of the knowledge, I 100% do not believe that, you know, Gemini or, or chat GPT have any interior mental wife, right? But it is doing this thing now, that until two years ago, required human understanding to do there was no way to get to that result unless you understood. And now it can do that. And so the fact that we use kind of the language of
SPEAKER_01: understanding to shorthand for that, I think is not inappropriate. But I also understand why, you know, kind of rubs people, people the wrong way, people, I think it's
SPEAKER_02: it's very hard to here's, here's like, maybe, maybe my favorite interaction
SPEAKER_01: with Gemini. So far, this wasn't a notebook LM, but I was testing it with AI studio. So one of the things we want to do is, you know, have these writing tools that will roll out in early 2024, where you can like write something and you can select the text and you know, you can kind of ask the model to transform the text in various different ways. And so one thing I was trying to do was to say, here's some boring text and try and make this rhetorically like more interesting with some more metaphors. And so I gave Gemini a passage of description of the climate in Hawaii. And it was just very like scientific, but kind of dry, just a bunch of facts. I said, make this, you know, metaphorically more interesting. And it returned this like completely overwrought thing. It was like, Hawaii's climate is like a symphony that has been conducted by Mother Nature. And so, you know, just was crazy. And so I all I said to Gemini was, dude, that is a little over the top. Gemini.
SPEAKER_01: Gemini responded, you're right, that was a little bit excessive. Here's a better version. I think this is better. And it was perfect. And I was like, the fact that it understands, dude, that is a little over the top. And completely back to the right
SPEAKER_01: response is just, I mean, that's, that's just nuts. It
SPEAKER_01:
feels to me, this is like, this is where I go to, I think like,
SPEAKER_02:
if you do believe in simulation theory, like this is the final
level of where, like, in the matrix, you kind of wake up from
SPEAKER_02: the matrix, like, we're building this thing. And it's thinking
like us. And I asked it like, Yeah, dude, a little over the
top, brah. And it's like, okay, got it. And then you're who, whichever of us kind of realized that what we just recreated was our own brains first, kind of wins this video game that some
SPEAKER_02:
sentient being created a billion years ago in some other dimension, which is, let's see if we can make life forms that figure out that they're building computers that are themselves. And then it's just like Prometheus or aliens or something like that, where, you know, they're the engineers, but who made the engineers and it's like, oh, it's just a it's turtles all the way down.
SPEAKER_01:
You may have unlocked it. I don't know if the simulation comes to a halt. The pod goes out.
SPEAKER_02:
Hi, brother. Listen, everybody go check out just Google, Google notebook LM start playing with it. You can find Steven Berlin Johnson everywhere. He's on I think you're still on Twitter x. Yeah, you're somewhere over there. I am. Yeah, Steven B.
SPEAKER_01: Johnson. I've been tweeting, I'm gonna say it. Yeah, do it. A lot about the book. I'm gonna be sharing a lot of like tips and things about how to use it, which is just stored up in the arena.
SPEAKER_02:
SPEAKER_01: The arena is over there. If you if you really want to get a
SPEAKER_02:
great interaction going, just bring up Alex Jones, and Tucker Carson and hearing for your speech, you'll get really great
SPEAKER_00:
SPEAKER_02: threads going. It's awesome. It's really awesome. It's full contact. Alright, everybody. We'll see you next time on the sweet stars. Bye bye.
