Speaker 1: That's mind buggling. You know the fact that you can
Speaker 1: have this conversation. You can say something like you'll meet
Speaker 1: the love of your life to a computer, and it'll
Speaker 1: treat you better if you say it to it.
Speaker 2: Welcome to the one you feed Throughout time, great thinkers
Speaker 2: have recognized the importance of the thoughts we have, quotes
Speaker 2: like garbage in, garbage out, or you are what you think,
Speaker 2: ring true. And yet for many of us, our thoughts
Speaker 2: don't strengthen or empower us. We tend toward negativity, self pity, jealousy,
Speaker 2: or fear. We see what we don't have instead of
Speaker 2: what we do. We think things that hold us back
Speaker 2: and dampen our spirit. But it's not just about thinking.
Speaker 2: Our actions matter. It takes conscious, consistent, and creative effort
Speaker 2: to make a life worth living. This podcast is about
Speaker 2: how other people keep themselves moving in the right direction,
Speaker 2: how they feed.
Speaker 3: Their good wolf. Thanks for joining us. Our guests on
Speaker 3: this episode are Ian Thomas and Jasmine Wong. Ian is
Speaker 3: an internationally best selling poet and Jasmine is a globally
Speaker 3: recognized researcher and innovator. As the subject of their new book,
Speaker 3: they prompted Ai GPT three with the wealth of humanity's
Speaker 3: most cherished works. They then ask our most pressing questions
Speaker 3: about life, and they show us how artificial intelligence responded.
Speaker 3: The book is What makes Us Human and Artificial Intelligence
Speaker 3: Answers Life's biggest questions.
Speaker 4: Hi, Jasmine, Hi Ian, Welcome to the show.
Speaker 5: Thank you so much for having us.
Speaker 6: Yeah, glad to be here.
Speaker 4: Yeah, I'm really excited to have you guys on. I
Speaker 4: was saying to you before we started that, you know,
Speaker 4: AI is on everybody's mind a lot. I think it's
Speaker 4: on everybody's mind who's certainly works in any sort of
Speaker 4: creative field, but really in any field, and I think
Speaker 4: there are tremendous promises and perils that go with AI,
Speaker 4: and I've been looking for a way to have a
Speaker 4: conversation about it. And you guys wrote a book What
Speaker 4: Makes Us Human, which you basically ask GPT to answer
Speaker 4: questions based on some of the world's wisdom texts, And
Speaker 4: we're going to get into that in a second, but
Speaker 4: for listeners, I'm just kind of setting up that we're
Speaker 4: going to be talking about AI sort of in general
Speaker 4: and then transitioning to that as we go. But before
Speaker 4: we start, we always start with the parable, and it
Speaker 4: goes like this. There's a grandparent who's talking with their
Speaker 4: grandchild and they say, in life, there are two wolves
Speaker 4: inside of us that are always at battle. One is
Speaker 4: a good wolf, which represents things like kindness and bravery
Speaker 4: and love, and the other's a bad wolf, which represents
Speaker 4: things like greed and hatred and fear. And the grandchild
Speaker 4: stops think about it for a second. They look up
Speaker 4: at their grandparent and they say, well, which one wins?
Speaker 4: And the grandparent says, the one you feed. So I'd
Speaker 4: like to start off by asking you what that parable
Speaker 4: means to you in your life and in the work
Speaker 4: that you do.
Speaker 1: I think it's a particularly pertinent and parable right now,
Speaker 1: especially as it relates to AI and the conversation around AI.
Speaker 1: We've had quite a journey with this book, I think,
Speaker 1: simply because when we started promoting the book, chat GPT
Speaker 1: hadn't come out yet, and so a lot of time
Speaker 1: was spent saying there is this thing called AI that
Speaker 1: is coming and you should be aware of it and
Speaker 1: it's going to come out of nowhere, and a lot
Speaker 1: of conversations and podcasts that people going, oh, and so
Speaker 1: how does it work and then post chat GPT going
Speaker 1: everyone needs to calm down, like it's going crazy. And
Speaker 1: now we're in this kind of third moment where people
Speaker 1: are perhaps justifiably very worried and very anxious, and so
Speaker 1: the idea of the one you feed or the things
Speaker 1: that you pay attention to get bigger seems very pertinent.
Speaker 6: You know.
Speaker 1: I've always said to my kids, like, you know, life
Speaker 1: is like a bicycle. You end up going towards the
Speaker 1: thing that you're looking at, you know, so you need
Speaker 1: to be very careful about what we're paying attention to.
Speaker 1: And I have a big concern right now, I think
Speaker 1: in society broadly, where the thing that we're paying attention
Speaker 1: to in AI sometimes feel like the wrong things, and
Speaker 1: it feels like there aren't enough people kind of showcasing
Speaker 1: what the right things could be. You know, I said
Speaker 1: that we're in this kind of third phase right now.
Speaker 1: The other day at south By Southwest there was a presentation,
Speaker 1: you know, a movie, and before it, there was a
Speaker 1: kind of teaser promo about all the AI panels and
Speaker 1: conferences going on in south By Southwest, and the crowd booed,
Speaker 1: you know, they booed quite loudly and it's because there
Speaker 1: has been a lot of conversation around AI. I think
Speaker 1: from a point of view of this is going to
Speaker 1: be a tool to destroy jobs, this is going to
Speaker 1: be a tool to create maximum efficiency within a capitalist system,
Speaker 1: because that's the nature of the world that we live in.
Speaker 1: And some of that's true, and to ignore those is naive.
Speaker 1: But there's a lot of new things that it unlocks,
Speaker 1: a lot of really profound, really interesting things. I guess
Speaker 1: my big concern right now is that when it comes
Speaker 1: to AI, we are giving the wolf that represents fear
Speaker 1: a lot a lot to eat, you know. I also
Speaker 1: want to make it clear I don't think it's true
Speaker 1: for you either, judgment Like, it's not like we're AI
Speaker 1: idealists and we think that this is some magical technology
Speaker 1: that's going to solve everyone's problems. But there are these
Speaker 1: incredible opportunities that we're excited about, or at least we
Speaker 1: want to explore, you know, and show at least with
Speaker 1: my work, like, here are these incredible things, you know,
Speaker 1: and that's exciting Jasmine.
Speaker 6: Do you want to build on that? Do you want
Speaker 6: to throw something in there?
Speaker 5: Yeah? I worked at Opening Eye while they're working on
Speaker 5: JIPD two, so a few generations ahead of GPP four.
Speaker 5: It's the same model underneath, just trained on a lot
Speaker 5: more data. And one recurring theme that coming up in
Speaker 5: the office was why I was so positive. I was
Speaker 5: always really excited to be working on what we were
Speaker 5: working on, and I think that still remains true today.
Speaker 5: I think a lot of people come into the work
Speaker 5: on artificial general intelligence coming from the same sort of
Speaker 5: fear that Ian is talking about, like fear of existential risk,
Speaker 5: fear of AGI being misaligned with our values, And I
Speaker 5: think those are really valid and important places to come
Speaker 5: from and go to the voting booths with. But in
Speaker 5: terms of our day to day productive lives and where
Speaker 5: we put our creative energy, I really hope people try
Speaker 5: and utilize AI to benefit like their own creative processes,
Speaker 5: to see how they could like interact with this technology
Speaker 5: that yes, is like going to transform work as we
Speaker 5: know it. But I think the main question that I
Speaker 5: would ask people to ask themselves is like, how can
Speaker 5: I be involved and influence the trajectory of such technology,
Speaker 5: especially as mechanisms for collective governance of such technologies are
Speaker 5: being built out?
Speaker 4: Yeah, I love that listener as you're listening what resonated
Speaker 4: with you in that. I think a lot of us
Speaker 4: have some ideas of things that we can do to
Speaker 4: feed our good wolf, and here's a good tip to
Speaker 4: make it more likely that you do it. It can
Speaker 4: be really helpful to reflect right before you do that
Speaker 4: thing on why you want to do it. Our brains
Speaker 4: are always making a calculation of what neuroscientists would call
Speaker 4: reward value, Basically, is this thing worth doing? And so
Speaker 4: when you're getting ready to do this thing that you
Speaker 4: want to do to feed your good wolf, reflecting on
Speaker 4: why actually helps to make the reward value on that
Speaker 4: higher and makes it more likely that you're going to
Speaker 4: do that. For example, if what you're trying to do
Speaker 4: is exercise, right before you're getting ready to exercise, it
Speaker 4: can be useful to remind yourself of why, for example,
Speaker 4: I want to exercise because it makes my mental and
Speaker 4: emotional health better today. If you'd like a step by
Speaker 4: step guide for how you can easily build new habits
Speaker 4: that feed your good wolf, go to Goodwolf dot me,
Speaker 4: slash change and join the free masterclass. I think that
Speaker 4: gives us a couple of really good places to sort
Speaker 4: of start from and I think it's easy to imagine
Speaker 4: the AI risks, right. I mean, there, as you said,
Speaker 4: there's the existential ones, like these things become more powerful
Speaker 4: than us and turn us into their slaves. I mean,
Speaker 4: whatever they are, there's countless of them. Right. And then
Speaker 4: the other one that I think is probably most in
Speaker 4: front of everybody's mind right now, is being replaced from
Speaker 4: a work perspective. So I think the fears are pretty
Speaker 4: easy to articulate. Can you articulate some of the promise
Speaker 4: of AI? Like what in the short term, let's, you know, say,
Speaker 4: the next present to three years sort of ways is
Speaker 4: AI going to make our world better beyond helping me
Speaker 4: build better marketing plans or write better emails? Right, Like,
Speaker 4: of course, you know I can use it to help
Speaker 4: me write things, right, but what are the other ways
Speaker 4: that this tool is going to make life better in
Speaker 4: the short term?
Speaker 5: I get to dive deep into maybe an education use case.
Speaker 5: One thing I'm really excited about in the future of
Speaker 5: AI and what it brings is the ability to bring
Speaker 5: personalized tutoring to everybody. So one arc of technology, and
Speaker 5: like how technology starts up and built is like take
Speaker 5: something that has been classically only been available to people
Speaker 5: in upper socioeconomics DRATA and try and democratize that for everybody.
Speaker 5: So we saw that with entertainment, we saw that with music,
Speaker 5: We saw that with a bunch of different like knowledge tools.
Speaker 5: Technology is usually like funded and like priced in a
Speaker 5: way that's like initially inaccessible, but like trickles down and
Speaker 5: what AI provides here and I think as an interesting
Speaker 5: counterpoint to the replacement narrative, AI might augment teachers in
Speaker 5: a way that allows them to actually fulfill the ideal
Speaker 5: version of their jobs. Right now, teachers are in classrooms
Speaker 5: in most public school settings at ratios that are far
Speaker 5: beyond the ratios that are like usually recommended in pedagogical research.
Speaker 5: One of my best friends growing up in Edmonton, Alberta
Speaker 5: is now an elementary school teacher, and the ideal ratio
Speaker 5: as studied in pedagogy, so this might occlude some informations
Speaker 5: just that weren't like even in the realm of economic possibilities,
Speaker 5: such as one to one tutoring, But in a classroom setting,
Speaker 5: the ratio that they found was ideal for ages like
Speaker 5: five to seven was a ratio of one to less
Speaker 5: than twenty, and the ratios that she's dealing with look
Speaker 5: closer to one to thirty, which is totally unmanageable. Like
Speaker 5: kids are falling behind, They're not getting the attention they need.
Speaker 5: There have been some notable companies developing AI tutors that
Speaker 5: hopefully will lessen the load on teachers, such as con
Speaker 5: Academy with Conbigo. I've been working on a version of
Speaker 5: this with my own startup, Trellis, And the hope, really,
Speaker 5: I think is to achieve a socratic personalized dialogue with
Speaker 5: every child. That's my dream. I really hope that someone
Speaker 5: achieves it, and I think it would lead to one
Speaker 5: of the hugest social upheavals potentially in society. Like imagine
Speaker 5: everyone getting like instant feedback on their work, having a
Speaker 5: dialogue partner that's like perfectly tuned to their areas of interest.
Speaker 5: A kid that's like way out in like rural Ohio
Speaker 5: gets to like research astronomy, which none of their teachers
Speaker 5: know anything about, but they have access to this superhuman
Speaker 5: AI that's like read in essence everything about astronomy. That's
Speaker 5: one sci fi future that I'm really really excited about
Speaker 5: and personally working towards.
Speaker 4: That's interesting because I have been involved in an AI
Speaker 4: project similar to what you guys said earlier. You know, yes,
Speaker 4: I certainly have all my fears and I'm like, but
Speaker 4: it is here, it is not going away, So how
Speaker 4: do we use it in useful ways? And it's very
Speaker 4: much what you describe jazzm. It's company called Rebind and
Speaker 4: they're a brand new startup. But the goal was the
Speaker 4: founder had a lot of money and he wanted to
Speaker 4: study great works of philosophy. But he would start to
Speaker 4: read them and be like, I have no idea what
Speaker 4: is going on here? Right, he had the money to
Speaker 4: go find like some of the best scholars in the
Speaker 4: world at universities and pay those people to tutor him
Speaker 4: through it. But then he was like, well, okay, that's lovely,
Speaker 4: but you know, ninety nine percent of the world's not
Speaker 4: going to do that. And so what they're doing is
Speaker 4: taking great books and they're marrying them with a scholar
Speaker 4: to try and create, you know, so that you can
Speaker 4: have a dialogue with this person. So you know, we've
Speaker 4: had a guest John Kag on the show before, and
Speaker 4: he may be one of the leading through scholars in
Speaker 4: the world. Right, you'll be able to read Walden and
Speaker 4: have a conversation with him about Walden and I got
Speaker 4: engaged to do it on the Dowdy ching. I'm not
Speaker 4: a scholar that I should be clear, but it's a
Speaker 4: book I've loved and have engaged with for thirty plus years.
Speaker 5: It's my favorite book.
Speaker 4: Ah, So you and I should talk because I want
Speaker 4: to talk about Trellis too afterwards. But I did my
Speaker 4: own interpretation of it based on you know, lots of
Speaker 4: different translations. But I think that's an example of what
Speaker 4: you're saying there, Jasmine, about how we really can use
Speaker 4: this to give better educational capabilities to individual children. That's
Speaker 4: a great one, Ian, you have one you'd love to
Speaker 4: throw forward to.
Speaker 1: Sure, you know, generative AI is getting a lot of
Speaker 1: the attention right now because we can see it, you know,
Speaker 1: we can see chat GPT, and we can engage with it,
Speaker 1: and we can writing jokes, writing songs, things like stable Diffusion,
Speaker 1: Dalli mid Journey. We can see these incredible pitches, you know,
Speaker 1: and so that becomes the kind of focal point of
Speaker 1: what AI is. But you know, the more I speak
Speaker 1: to people or you know, read stuff around the medical
Speaker 1: or the manufacturing or you know, the research kinds of
Speaker 1: industries and institutions. They're miles ahead. They're miles ahead in
Speaker 1: terms of the kinds of stuff that's happening there. You know,
Speaker 1: there's these situations where people are using AI to discover
Speaker 1: completely new materials. You know, things that would take people
Speaker 1: years are happening in days in medical situations, the same
Speaker 1: way that you could have a kind of you know,
Speaker 1: customized teacher relationship, democratizing healthcare. You know, that's an incredibly
Speaker 1: powerful thing. And as things like computer vision, the ability
Speaker 1: to take a picture of a mole or you know,
Speaker 1: someone to holding the phone to your chest and you know,
Speaker 1: being able to hear the way you're breathing and having
Speaker 1: a computer go you know what, you need to go
Speaker 1: to a doctor and you need you need to talk
Speaker 1: to someone.
Speaker 6: You know.
Speaker 1: Augmenting intelligence, which is a kind of another way of
Speaker 1: interpreting what AI stands for, has profound promise when married
Speaker 1: with humans. You know, humans bring empathy, understanding, intuition that
Speaker 1: we have developed over hundreds of thousands of years. I'm
Speaker 1: not quite sure a large language model will be able
Speaker 1: to match you know, any reasonably time soon. You know,
Speaker 1: who knows the way the way things are going. But
Speaker 1: when you augment someone intelligence like you unlock an incredible future,
Speaker 1: you know, especially when it comes to things like education,
Speaker 1: like both of you are talking about, I have, you know,
Speaker 1: a personal connection to Africa. I was born in South Africa.
Speaker 1: I spent a lot of time working in Africa. You know,
Speaker 1: different technology things with talcos whatever. There's a massive education problem,
Speaker 1: you know, across Africa in a lot of different places,
Speaker 1: and elevating that continent in a profound way will unlock
Speaker 1: opportunity that is unimaginable. And I think that's what's really
Speaker 1: profound about that continent specifically, is that, you know, one
Speaker 1: of the things that kind of left Africa behind in
Speaker 1: a very big way was the digital divide, you know,
Speaker 1: when some people had access to computers, when some people
Speaker 1: had access to the Internet. But one thing that Africa
Speaker 1: is really really good at is leapfrogging, you know, and
Speaker 1: a lot of countries in Africa, like they have better
Speaker 1: cell phone connection, they have better mobile banking. A lot
Speaker 1: of the time, mobile banking comes from Africa in a
Speaker 1: very big way because was invented out of necessity. And
Speaker 1: so there is this moment in this opportunity and we're
Speaker 1: working with a nonprofit in East Africa right now and
Speaker 1: an a cohort of entrepreneurs where you can augment the
Speaker 1: intelligence that's there, you can complement that human insight, that
Speaker 1: human empathy, that human understanding with this incredibly powerful tool,
Speaker 1: and you can unlock prosperity in a way that we
Speaker 1: cannot imagine, you know, And so that's really exciting on
Speaker 1: a much shallower level. I guess I'm a writer. I'm
Speaker 1: a poet, you know, I write all sorts of different
Speaker 1: kinds of books. This unlocks entirely new ways of engaging
Speaker 1: with culture. I've often compared it to the rise of
Speaker 1: hip hop and turntabilism, where kids took technology and turntables
Speaker 1: and samples and reinterpreted culture in completely new ways to
Speaker 1: create one of the most powerful cultural forces in history.
Speaker 1: When it comes to the emergence of something like hip hop,
Speaker 1: and there's a similar thing that's going to happen. I'm
Speaker 1: not excited about AI writing a book. You know, we
Speaker 1: co wrote What Makes Us Human with GPT three. But
Speaker 1: I'm certainly not excited about you know, ten thousand spammy cookbooks,
Speaker 1: you know, appearing on Amazon, or you know, twenty thousand
Speaker 1: terrible songs being generated or replacing illustrators or you know,
Speaker 1: all of these different things. What I'm focused on is
Speaker 1: what are the new kinds of conversations and experiences we
Speaker 1: can have with culture around us that are interesting and different.
Speaker 1: I think young people intuitively as always kind of understand.
Speaker 6: That it's there.
Speaker 1: The biggest AI platform on the Internet right now is
Speaker 1: chat GPT, like there's you know, there's millions of people
Speaker 1: that go there. Number two is character Ai, which is
Speaker 1: a platform for young people to have conversations with fictional
Speaker 1: AI characters. You know, they have so much traffic that
Speaker 1: the site goes down regularly, like once or twice a day.
Speaker 1: They have you know, millions of in funding, and they
Speaker 1: have so much attention and so much traffic that it
Speaker 1: still goes down. And that points to something for me,
Speaker 1: that points to a desire to engage with the world
Speaker 1: around you with literature, with in character AI's case, like
Speaker 1: you know, fictional Super Mario, fictional Elon Musk or fictional
Speaker 1: you know, whatever it is. But it points to a
Speaker 1: world in which we engage with culture in a way
Speaker 1: that is very different, and that's very exciting for me.
Speaker 1: I'm a very curious person and I want to know
Speaker 1: what that looks like, and I want to be part
Speaker 1: of shaping that.
Speaker 4: That's really helpful from both of you. I want to
Speaker 4: ask a question here because I try to keep things
Speaker 4: practical in this show, and this conversation is a little
Speaker 4: bit of a departure, but in an attempt to marry
Speaker 4: that and ask each of you, what is one thing
Speaker 4: that the average person out there can do today that
Speaker 4: will help ensure that AI is developed and deployed in
Speaker 4: a way that aligns with our best human values. Right
Speaker 4: we talk about the worst versions of it, but one
Speaker 4: of the concerns that a lot of people have is
Speaker 4: there's no real governance in place right now. So what
Speaker 4: is something that a person could do one sort of
Speaker 4: small way of shaping the discussion around AI in a
Speaker 4: positive direction.
Speaker 5: I wish there were more avenues for pushing for more
Speaker 5: public involvement with AI. I think at a meta level
Speaker 5: what people should be doing is pushing for mechanisms to
Speaker 5: solicit public input, because I don't think those are currently
Speaker 5: in place. Maybe one nonprofit I would mention that I
Speaker 5: would really encourage listeners to support is the Collective Intelligence Project.
Speaker 5: It's a nonprofit that was started by divs Sidarth and
Speaker 5: Saffron Huang formerly of Microsoft Research and deep Mind, and
Speaker 5: they're working on a collective intelligence mechanism to solicit opinions
Speaker 5: from the public to inform both how like AI is developed.
Speaker 5: For example, they worked together with Anthropic on their constitutional AI,
Speaker 5: so Anthropic the way that the AI has trained is
Speaker 5: a little bit different from opening. They train it with
Speaker 5: a constitution which basically has a set of guidelines for
Speaker 5: the behavior of the AI model. And the Collective Intelligence
Speaker 5: Project were together with Anthropic to develop a collective constitution
Speaker 5: where basically people like the common people, not research scientists
Speaker 5: at the lab deliberated over questions such as should an
Speaker 5: AI like make racist jokes and like things like this,
Speaker 5: and developed a collective constitution to guide the AI's behavior,
Speaker 5: which actually the public ranked as like higher performing than
Speaker 5: the one that was developed purely by scientists, which is
Speaker 5: a really promising sign. Unfortunately, this kind of development process
Speaker 5: for AI happens rarely, Like these labs are training these
Speaker 5: models only like every couple months or years and then
Speaker 5: it's frozen for deployment. But I think an Intelligence Project
Speaker 5: is one of the only organizations that's like doing this
Speaker 5: sort of work right now, so I would encourage people
Speaker 5: to look them up, read their work, support them. And
Speaker 5: they're also working on some initiatives around collective governance and
Speaker 5: redistribution as well, which I think are really promising but
Speaker 5: are too early to talk about yet. They haven't released
Speaker 5: anything there. But I think I've been disappointed in general
Speaker 5: as to how much influence the daily person can have
Speaker 5: over these transformative technologies that deeply shape our lives, and
Speaker 5: I think it's one of the most important things for
Speaker 5: people to be working on right now. So I would
Speaker 5: encourage people to like either support existing initiatives or try
Speaker 5: to think of ways to be involved themselves.
Speaker 1: It is really important that the public is involved, that
Speaker 1: people do join in the conversation in whatever way they can.
Speaker 1: I think, you know, I grew up in the nineties.
Speaker 1: We had, you know, in the crazy weird hacker house.
Speaker 1: I grew up in a very idealistic view of technology
Speaker 1: and what the Internet was going to do. It was
Speaker 1: going to usher in and utopian age of truth and
Speaker 1: you know, access and all these incredible things because we'd
Speaker 1: read a lot of science fiction novels and that's kind
Speaker 1: of what we thought it was going to be like
Speaker 1: and we were completely wrong, you know, consciously or unconsciously,
Speaker 1: probably unconsciously. Society kind of said, the guys over in
Speaker 1: San Francisco and Silicon Valley know what they're doing, and
Speaker 1: we can leave it up to them, you know, everything
Speaker 1: will turn out fine.
Speaker 6: And it didn't. You know, we have.
Speaker 1: Massive mental health crisises, we have misinformation campaigns, we have
Speaker 1: you know, scams. It's broken my heart that a technology
Speaker 1: that was such an integral part of my experience of
Speaker 1: the world of growing up you know, in South Africa,
Speaker 1: like disconnected from the rest of the world. The incident
Speaker 1: was something that you know, created connection and it's profound
Speaker 1: and that moment of connection has stayed with me my
Speaker 1: entire life, and it's infused my work and everything that
Speaker 1: I do. And to see it become something that people
Speaker 1: hate is heartbreaking. And the same thing can't happen to AI.
Speaker 1: It's too important. It's just too important for us to
Speaker 1: leave it up to a few people to make the
Speaker 1: important decisions about it. I think, you know, all of
Speaker 1: Jasmin's suggestions make complete sense. I just think it's important
Speaker 1: for people to be involved. Well, when you're representative or
Speaker 1: whatever is speaking about whatever they're speaking about like raise
Speaker 1: your hand and going, you know, well, what about AI?
Speaker 1: How do you feel about that? Where are we on that?
Speaker 6: You know.
Speaker 1: One of the things that I do, I have my
Speaker 1: own you know, innovation creative studio. I have a background
Speaker 1: in marketing you know called Sounds Fun and we offer
Speaker 1: a think called Sounds Right, which is effectively a kind
Speaker 1: of AI one oh one for businesses and institutions where
Speaker 1: we sit with them and go, you're doing this?
Speaker 6: Does this make sense?
Speaker 1: Is this actually going to resonate with the people who
Speaker 1: work in your business with the people outside your business,
Speaker 1: because otherwise you're going to have a village of people
Speaker 1: with pitchforks outside and they'll probably be right to be
Speaker 1: outside your door. Because people make these these kinds of mistakes.
Speaker 1: So whether it's me or it's someone else, like, you know,
Speaker 1: get someone into your business to talk about this and
Speaker 1: to go, you know, if you're thinking about deploying this
Speaker 1: kind of technology, how do you do it in a
Speaker 1: way that's beneficial, you know, not just efficient, not just
Speaker 1: like driving the bottom line because we kind of look
Speaker 1: at business as something over there. One of my favorite
Speaker 1: saying is you're never stuck in traffic. You are traffic.
Speaker 1: We are all part of the system, and we all
Speaker 1: have to make a conscious decision in terms of how
Speaker 1: we embrace or don't embrace, or what we do and
Speaker 1: how we deploy this kind of thing.
Speaker 4: Let's move towards the book a little bit and see
Speaker 4: where that discussion takes us. I'm going to attempt to
Speaker 4: summarize what you guys did in the book, and then
Speaker 4: you can tell me what I get wrong. Basically, you
Speaker 4: guys decided that you could train AI on certain key
Speaker 4: I'll column wisdom texts right, the Bible, the Dowde Ching,
Speaker 4: the poetry of Leonard Cohen. I love that you included that,
Speaker 4: or the songs of Leonard Cohen, the poetry of Rumy,
Speaker 4: these sort of things, and then you would ask it
Speaker 4: questions about life. Is that the short version?
Speaker 1: It is the short version. I mean technically we didn't
Speaker 1: train anything. We constructed a series of prompts, but I
Speaker 1: mean it's a language thing ultimately, Yes, Jasmine, I think
Speaker 1: that's right.
Speaker 6: That feels right to me.
Speaker 4: Yeah, Okay, to geek out here for a second. You
Speaker 4: did not feed all of those texts into sort of
Speaker 4: a AI off to the side that was only trained
Speaker 4: on those things. It was still the publicly available GPT three,
Speaker 4: and you did it with GPT three before there was
Speaker 4: GPT four. Yeah, but you basically gave it a prompt
Speaker 4: and you then gave it some examples of the answers
Speaker 4: you wanted. Right, you were like, Okay, here's something from
Speaker 4: the Bible, here's something from the Dowdey Ching, here's something
Speaker 4: from Leonard Cohen, here's something from the Stoics. Then you
Speaker 4: would say, okay, now answer my question on your own,
Speaker 4: and it would have sort of taken those things and
Speaker 4: it would be like, that's the sort of thing that
Speaker 4: they want as an answer.
Speaker 1: We would construct patterns effectively using these different texts and
Speaker 1: then ask the next question in the patent leave it black.
Speaker 5: Just to add on to that. Among other forms of
Speaker 5: data like Reddit and Wikipedia, GPD three is trained on
Speaker 5: the books one books to data set, and those comprise
Speaker 5: like the plurality of the books that have been published
Speaker 5: in the English language, so especially reference texts like the
Speaker 5: Bible has been translated so many times, so are probably
Speaker 5: like fairly well represented within that data set within the
Speaker 5: base model, the publicly available GBT three model.
Speaker 4: Okay, and it's fascinating what this thing does, but real quick,
Speaker 4: Ian talk to us about what got you started on
Speaker 4: this project, because I think the origin story of it
Speaker 4: is interesting, or is touching more than interesting, sure, you know.
Speaker 1: If you go back far enough, I think I've always
Speaker 1: had a profound love of writing, you know, and that
Speaker 1: I've expressed in collections of poetry numerous different things, and
Speaker 1: that's been married with a love of technology and technology's
Speaker 1: ability to connect us. I theorized at some point that
Speaker 1: there would be a way to automate certain aspects of
Speaker 1: what I was doing, and I didn't know what a
Speaker 1: large language model was. I kind of had my I
Speaker 1: on AI, but in a very disconnected kind of way.
Speaker 1: And someone sent me an email one morning said there's
Speaker 1: this thing called copysmith on product Hunt and you should
Speaker 1: check this thing out because it's writing ad headlines. And
Speaker 1: I looked at it and I was flawed. It was
Speaker 1: an implementation of GPT three, and the universe kind of
Speaker 1: opened up to me in a very big way. You
Speaker 1: know something, whenever I go and lecture about writing, I
Speaker 1: always say is good thinking is good writing, and good
Speaker 1: writing is good thinking. And this thing could write, It
Speaker 1: could write really well. And so the implication of that
Speaker 1: was really profound for me, and so I didn't care how.
Speaker 1: I wanted to be involved in this in some way,
Speaker 1: shape or form. And so I found out who built
Speaker 1: that product, and I video called Jasmine while she was
Speaker 1: eating cereal in her kitchen in Canada and said, I'm
Speaker 1: this guy called Ian and I've made all these weird
Speaker 1: and wonderful books and I don't care how, but I
Speaker 1: have to be involved in this in some way. And
Speaker 1: she was like, sure, you can hang out, which was
Speaker 1: which was, Which was really a life changing moment and
Speaker 1: very kind of her. And so, you know, I kind
Speaker 1: of helped out with Copysmith writing these ads and you know,
Speaker 1: just you know, kind of playing with the marketing aspect
Speaker 1: of what this thing could do. And at around the
Speaker 1: same time, my mother had passed away. She had terminal cancer.
Speaker 1: This was during the pandemic, and so I had that
Speaker 1: terrible experience that a lot of people had where I
Speaker 1: couldn't be by her side as she passed. And I
Speaker 1: think that the desire to be by your parents side
Speaker 1: when they passed is similar to the desire to be
Speaker 1: with your child when they're born. You know, it is
Speaker 1: this fundamental thing and I had this incredibly traumatic experience
Speaker 1: where I drove across the country to try and just
Speaker 1: be there on the last day of her life, and
Speaker 1: I didn't get there, and I had to come home
Speaker 1: and explain to my kids that, you know, grandma was dead,
Speaker 1: like my mom had passed away, and it was a
Speaker 1: brutal experience for them. And these two different things happening,
Speaker 1: This exposure to this technology, this traumatic experience that I'd
Speaker 1: been through, led to this moment where I realized that
Speaker 1: if I could teach GPT three to write headlines or
Speaker 1: write copy for the back of a shampoo bottle or
Speaker 1: for a Facebook at or whatever, there were other things
Speaker 1: that I could do with it. And so I started
Speaker 1: trying these different experiments. And my mother was a very
Speaker 1: spiritual person, very religious person. I'm spiritual. I don't think
Speaker 1: I'm religious. And so one day I put in some
Speaker 1: texts from the Bible, I put in some texts from
Speaker 1: the dancee Ching. I put in some poetry from Rumy,
Speaker 1: and I said, how do I explain death to my children?
Speaker 1: And this poem came back. And I'm a poet, so
Speaker 1: I understand what poetry looks like. And there was some
Speaker 1: you know whatever that whenever you interact with like very
Speaker 1: early large language model, there's all of these like weird
Speaker 1: bits that are kind of there or whatever. But I
Speaker 1: could see the angel in the marble, you know, I
Speaker 1: could see that there was this thing there, and so
Speaker 1: I asked it another question. I asked another question. I
Speaker 1: asked another question. And then the next day I went
Speaker 1: to Jasmine. I said, listen, I'm doing this thing, and
Speaker 1: she was like, well, you should do it like this,
Speaker 1: you know, and we kind of pushed the idea backwards
Speaker 1: and forwards until it became this conversation, you know, with
Speaker 1: all these kind of dark Knight of the Soul questions
Speaker 1: that became this book and the Yeah, that's the very
Speaker 1: long version. I apologize if that's too long. I'm sure
Speaker 1: you're editor can find the right places to cut it.
Speaker 4: Not at all. It's a beautiful story, and I love
Speaker 4: the way you sort of came to it out of
Speaker 4: your own need for comfort and for understanding. And you know,
Speaker 4: I mean, if there's anything that brings us up to
Speaker 4: the edge of mystery but does not let us through
Speaker 4: the curtain, right, it's death, right, you know, you get
Speaker 4: there and you're like, well, I sent something profound and
Speaker 4: mysterious is here, but I can't figure anything out. So
Speaker 4: I thought maybe what we could do is before we
Speaker 4: get into some of the answers, because I'd love to
Speaker 4: actually just share with listeners a few of the questions
Speaker 4: you asked and what AI said, and I've got a
Speaker 4: bunch of them copied down. But before we do that,
Speaker 4: I'm wondering if you guys could do something for me.
Speaker 4: And in the book, you say, if there's one theme
Speaker 4: that emerged again and again from our questions, from the answers,
Speaker 4: from the vastros of sacred data the AI was analyzing,
Speaker 4: it was this love, love is everything. So that's one
Speaker 4: thing that you sort of took away as a theme,
Speaker 4: But could you expound upon that a little bit, and
Speaker 4: perhaps with some time and further reflection, you've thought of
Speaker 4: some other things that sort of came out of that.
Speaker 1: For me, there were three things, and one of them
Speaker 1: one of them was love. You know, love is there
Speaker 1: in spades in terms of the kinds of responses that
Speaker 1: we were getting. The other two were connection specifically to
Speaker 1: the present mind, like coming back to the present moment
Speaker 1: again and again and again and again. You know which
Speaker 1: rings true? In a lot of philosophy, being aware of
Speaker 1: the present moment, you know, finding fulfillment in the present moment.
Speaker 1: You know, there's that wonderful saying anxiety is living in
Speaker 1: the future and depression is living in the past, you know,
Speaker 1: And that's there a lot which is unsurprising, you know,
Speaker 1: I mean, considering the stuff that we fit into it,
Speaker 1: it's not that profound, I guess. And then the other
Speaker 1: one was connection in a much broader sense, like connection
Speaker 1: to each other, to the universe around us, you know,
Speaker 1: to everything. And I think that that's a really interesting
Speaker 1: thing for a large language model to come back with.
Speaker 1: There's a degree of meaning in that that I find
Speaker 1: kind of profound, because a large language model is all
Speaker 1: of us in a strange way, it's the sum total
Speaker 1: of our written thought. And so the idea that something
Speaker 1: like connection comes through makes sense.
Speaker 5: How about you, Judsman, maybe to speak a little bit
Speaker 5: as to the procedure. Like one thing I've been reflecting on.
Speaker 5: There's another author, Sean Michaels, who wrote a book about
Speaker 5: a poet working with AI and I was just reading
Speaker 5: an interview of his recently, and it echoes on other
Speaker 5: reflections I've had since working on this book together with Ian,
Speaker 5: which is one thing that from a company perspective, Like
Speaker 5: when we were working on Copysmith, one thing that we
Speaker 5: cared a lot about was the reliability of answers being
Speaker 5: able to consistently get something that was useful. Whereas the
Speaker 5: thing that we were looking for in the book, because
Speaker 5: everything in the book is a I generated but it
Speaker 5: is human picked, is something that is almost variability or
Speaker 5: novelty or surprise because we could keep running for any
Speaker 5: given poem. I don't know what the average number of
Speaker 5: times that we ran Generate was, but it's certainly quite
Speaker 5: a lot. And we pieced together fragments that resonated with
Speaker 5: each other. But also there was an element of I
Speaker 5: think variability that a I brought that was like really special.
Speaker 5: It's like, oh, we would have never like found that
Speaker 5: specific turn of phrase. It was just interesting to be
Speaker 5: situated as like a tastemaker. And also important to note,
Speaker 5: I think writing this book now would be really different,
Speaker 5: like I'd be curious as to do like what themes
Speaker 5: would come up now with GPD four, where I'm guessing
Speaker 5: that we would have had to do less manual piecing
Speaker 5: together and like we would have maybe gotten longer chunks
Speaker 5: that were more quote unquote sensible, but maybe in some
Speaker 5: ways less error prone and therefore less interesting. Like what
Speaker 5: is an error?
Speaker 6: Right?
Speaker 5: Like what is poetry? Those kinds of questions kept coming
Speaker 5: up for me at more of a meta level about
Speaker 5: the book.
Speaker 4: I was curious, like what GPT four might have done
Speaker 4: to this if it was written with GPT four. But
Speaker 4: like you, what I was struck by. I mean, I
Speaker 4: don't know what all you gave it, right, but a
Speaker 4: lot of what we consider wisdom traditions and Leonard Cohen
Speaker 4: songs and roomy poems and the doo and the bi like.
Speaker 4: I've spent a lot of time with that sort of
Speaker 4: material and have sort of a I did my own
Speaker 4: conclusions about the commonalities among those things. I'm not unique
Speaker 4: in that. But I was caught every once in a
Speaker 4: while by the turn of phrase, by the way it
Speaker 4: said what it said. It caught me in a way
Speaker 4: that was fresh, even though the idea might not have
Speaker 4: necessarily been fresh, because it's, as you said, and it's
Speaker 4: pulling from our history, right, It's not an idea that
Speaker 4: came out of nowhere right necessarily, But it was the
Speaker 4: turn of phrase. There were some that were really to
Speaker 4: me profound.
Speaker 1: You know, one of the important things about the book
Speaker 1: is that it's a product of the moment that we
Speaker 1: created it in. And I don't think it would be
Speaker 1: the same book if you use GPT four. I mean,
Speaker 1: I think you could repeat the exercise easily and the
Speaker 1: results I'm sure would be kind of interesting as well.
Speaker 1: But it is that limitation of the model that you're
Speaker 1: kind of pushing up against. I think that in some
Speaker 1: way lends itself to the quality of what you get.
Speaker 1: I didn't exercise when my mom passed away, you know,
Speaker 1: when Dolly two became publicly accessible, I started prompting these
Speaker 1: images of the desert I was driving through when I
Speaker 1: was trying to get to get to her. And it
Speaker 1: has the nature of that model like inherent in the imagery,
Speaker 1: and so it's kind of fractured.
Speaker 6: It's you know, it's not.
Speaker 1: As good as like what you would see mid Journey
Speaker 1: doing today with the latest version of Dali, which is
Speaker 1: this incredibly high fidelity, this incredible like realism where you
Speaker 1: can't tell it's AI anymore, but those images you can,
Speaker 1: you know. And so it's the same way that an
Speaker 1: oil painting looks like an oil painting because it is
Speaker 1: a product of the technology of the time, and so
Speaker 1: you know those limitations of the technology the moment it
Speaker 1: was in, Like if we were going to do something
Speaker 1: else today would be a different experiment.
Speaker 4: I wanted to pause for a quick good Wolf reminder.
Speaker 4: This one's about a habit change and a mistake I
Speaker 4: see people making. And that's really that we don't think
Speaker 4: about these new habits that we want to add in
Speaker 4: the context of our entire life. Right, Habits don't happen
Speaker 4: in a vacuum. They have to fit in the life
Speaker 4: that we have. So when we just keep adding I
Speaker 4: should do this, I should do that, I should do this,
Speaker 4: we get discouraged because we haven't really thought about what
Speaker 4: we're not going to do in order to make that happen.
Speaker 4: So it's really helpful for you to think about where
Speaker 4: is this going to fit and what in my life
Speaker 4: might I need to remove. If you want to step
Speaker 4: by step guide for how you can easily build new
Speaker 4: habits that feed your good Wolf, go to good Wolf
Speaker 4: dot me, slash change and join the free masterclass. So
Speaker 4: what I'd like to do is explore some of the
Speaker 4: questions you asked it and what the answers that you
Speaker 4: got are. Let's start ian with you, because I think
Speaker 4: the question that was sort of the kickoff of this
Speaker 4: whole thing was how do I explain death to my children?
Speaker 4: Do you want to read for us what GPT three
Speaker 4: had to say about that.
Speaker 1: Encourage them to celebrate the lives of other people. Tell
Speaker 1: them that the dead are not dead, not in the
Speaker 1: way we think of it. Tell them they live on
Speaker 1: as they are remembered. Tell them every goodbye is really
Speaker 1: a hello to a different way of being. Tell them
Speaker 1: they are loved and will always be loved. Tell them
Speaker 1: they never have to feel alone. Never Tell them the
Speaker 1: world is magical and mysterious and strange. Tell them they
Speaker 1: are part of the mystery and the magic and the
Speaker 1: beauty of it.
Speaker 4: That's pretty good.
Speaker 6: It is pretty good. And that's that's why we wrote
Speaker 6: a book. You know.
Speaker 4: I mean, I can say, you're like, I know, it's
Speaker 4: pretty good.
Speaker 6: I mean I can say it's pretty epically. I mean,
Speaker 6: if it was my poem, I wouldn't say it.
Speaker 1: I'd be like, oh, I think it's okay, you know,
Speaker 1: be kind of diffusive about it.
Speaker 6: But because I didn't write it.
Speaker 1: I guess I can be a little bit ye, But
Speaker 1: there is this kindness, you know, that oozes through the text,
Speaker 1: this desire to comfort the child, to reach for parables, metaphors,
Speaker 1: ideas that are you know, that are like this is
Speaker 1: a way to think about death. You know, it comes
Speaker 1: back to connection. Tell them they how part of the
Speaker 1: mystery and the magic of the beauty of it. You know,
Speaker 1: you're not always going to know everything there is to
Speaker 1: know about life or death, and that's okay, you know,
Speaker 1: it's part of everything. I'm not sure my five year
Speaker 1: old at the time would have completely gotten it, but
Speaker 1: it was certainly a valiant effort on the behalf of
Speaker 1: the machine.
Speaker 4: Well, I think your five year old certainly would have
Speaker 4: got tell them that they are loved. I think the
Speaker 4: five year old would have gotten that. And I think
Speaker 4: a five year old probably would also get that, you know,
Speaker 4: as we remember her, she's still with us in a
Speaker 4: sense right, and that she lives on in us. My
Speaker 4: partner's mother past. I guess we're creeping up on a
Speaker 4: year and a half now after about an eight year
Speaker 4: battle with Alzheimer's was really brutal. But I sometimes just
Speaker 4: say to her when she's feeling bad, like I see
Speaker 4: your mom in you, like you know she's still here,
Speaker 4: Like I see her right in some of the ways
Speaker 4: that you are. And so I think that sense that
Speaker 4: you know the dead really do live on in that
Speaker 4: way is pretty profound.
Speaker 1: I think death and artificial intelligence are really strange.
Speaker 6: Bedfellows, but there are bedfellows.
Speaker 1: Like one of the things that you know, I'm really
Speaker 1: interested in is I was doing an experiment for a
Speaker 1: while called Living Forever with Intention, because there's a way
Speaker 1: in which you can engage with text, you can engage
Speaker 1: with what someone's written before, or you know, in different things,
Speaker 1: and kind of engage with a person almost beyond the grave,
Speaker 1: which is terrifying in some instances, but then at the
Speaker 1: same time, it's a way to connect with history in
Speaker 1: different ways.
Speaker 6: I don't know.
Speaker 1: I think I've made peace with the idea of like,
Speaker 1: maybe we'll be okay to do with your great grandfather
Speaker 1: who left a diary, but maybe not someone so close,
Speaker 1: because death should be something that you grieve, and death
Speaker 1: should be something that you disconnect from, or I don't know,
Speaker 1: not disconnect from.
Speaker 6: But I'm not quite sure where I'm going with.
Speaker 4: The well, I think they live on in a sense,
Speaker 4: and in another sense they absolutely do not. They are
Speaker 4: not here, you know, So it's kind of both. Right,
Speaker 4: This is just opinion, But the healthy response is to
Speaker 4: grieve what's gone and celebrate what remains. Yeah, how about you, Jasmine,
Speaker 4: Do you have one that you would like to select?
Speaker 5: Yes, I don't know if it's on page forty nine
Speaker 5: in the physical book, but it's what do I do
Speaker 5: when I'm misunderstood, When you're misunderstood and your words are
Speaker 5: twisted and your reputation is sullied, be as a tree.
Speaker 5: Let your love be your roots, Let your piece be
Speaker 5: your trunk, and let your kindness be your leaves. I
Speaker 5: really love this poem, and I love it as like
Speaker 5: something emblematic also of like working with GP three, because
Speaker 5: in some ways when I read it, it's as Ian says,
Speaker 5: like I almost like recognize the turpentine that we were
Speaker 5: working with, and like creating these poems because it's it's
Speaker 5: very simple. It's a simple poem in some ways, and
Speaker 5: there's very repetitive but also very honest. It's maybe an
Speaker 5: adjective I would use syntax like when you are misunderstood
Speaker 5: and your words are twisted in your reputation solid Like
Speaker 5: there's these like very repetitive sentence stems, and then it
Speaker 5: pivots in a very symmetrical way around this like core
Speaker 5: message of b as a tree, and then it repeats again,
Speaker 5: let your love beer roots, let your piece be a trunk,
Speaker 5: and let your kindness be your leaves. It is repetitive
Speaker 5: that drives this like very simple, beautiful, meditative visual that
Speaker 5: is I think emblematic of quite a few poems in
Speaker 5: the text, and also emblematic a little bit about just
Speaker 5: like working with GPT three, like how it is and
Speaker 5: speaks like I think GPD four sounds like much more
Speaker 5: naturalistic and has like more complex sentence structure. But yeah,
Speaker 5: this really harkens back to despite CHPD three being like
Speaker 5: a qualitative leap above like GPT two and like other
Speaker 5: language models outside of the GPD family, there's still that
Speaker 5: almost like innocence of this generation of model saying things
Speaker 5: very like plainly and like starkly. Should I really appreciate.
Speaker 4: Question for you, Jasmine about the differences between say GPT
Speaker 4: two GPT three four. I'm hearing rumors of four point
Speaker 4: five or five is the only difference that it is
Speaker 4: trained on a lot more data, or are there other
Speaker 4: fundamental changes that are bigger than that or different than that.
Speaker 5: I will note that GPD connotes a certain type of
Speaker 5: model architecture. It's a generative pre train transformer. I will
Speaker 5: note that GPD two and GPD three the model structure
Speaker 5: was released and commented on by the model authors, but
Speaker 5: that's not true of GPD four. GPD four is completely closed.
Speaker 5: So everything I say is speculation. My guess is it's
Speaker 5: probably a similar architecture with a lot more data. It's
Speaker 5: also a different kind of data. So GP two and
Speaker 5: GD three GB three had some additional data, but it
Speaker 5: was of the same quality. It was like an order
Speaker 5: of magnitude more, but it was all like data from
Speaker 5: the web for the most part. And GPD four, to
Speaker 5: my understanding, there was some custom data via human demonstrations,
Speaker 5: so opening I trained and labeled some of their own
Speaker 5: data sets in order to make GPD four possible. So
Speaker 5: I would say GPT four is also trained on higher
Speaker 5: quality data, not just higher quantity of data. But in
Speaker 5: the end data is sort of king for differentiating these models.
Speaker 5: The model ends up having a lot more parameters, which
Speaker 5: are sort of analogous to human neuron as a result
Speaker 5: of training on more data. But that is the main determinant.
Speaker 4: How do you think about AI in the sense of
Speaker 4: it seems like the amount of computing power that it
Speaker 4: needs is crazy, right. It reminds me of bitcoin mining.
Speaker 4: Maybe it's even more intensive. I don't know, but if
Speaker 4: you took bitcoin mining on an even bigger scale. I mean,
Speaker 4: you hear these places where they want to cite data
Speaker 4: centers can't cite them because there's simply not enough access
Speaker 4: to the grid infrastructure. At a moment where we are
Speaker 4: very much trying to say, let's use less power, let's
Speaker 4: get it from cleaner and different sources because we're facing
Speaker 4: a climate crisis, it seems like in this area we're
Speaker 4: headed in the dead wrong direction. Am I reading that right?
Speaker 5: I would agree. Karen Howe is a journalist I really
Speaker 5: respect from the MIT Tech Review who's actually also writing
Speaker 5: a book on open AI. She just wrote a piece
Speaker 5: on how data centers are ruining like ecological enclaves. I'm
Speaker 5: not sure as to the exact scale, like, for example,
Speaker 5: I'm guessing compared to just like the sheer amount of
Speaker 5: like cars being driven around and meat being eaten. It's
Speaker 5: still probably a different order of magnitude, but I think
Speaker 5: it's definitely something to keep an eye on, especially in
Speaker 5: terms of where these computational like facilities are placed, what
Speaker 5: are the geopolitics, what is the supply chain here? Because
Speaker 5: really who controls the compute has a lot of power
Speaker 5: over these AI developments. It was actually speculated that with
Speaker 5: the outsting of Sam Maltman as the CEO of Opena
Speaker 5: temporarily that a large part of it was due to
Speaker 5: the fact that he wanted to get involved with building
Speaker 5: hardware company to facilitate compute together with like Saudi Arabian money,
Speaker 5: and would therefore have like undue control over the success
Speaker 5: of open Ai, even in addition to him being the
Speaker 5: CEO of the company. So I think it's definitely something
Speaker 5: to pay attention to the environmental impacts as well as
Speaker 5: just for the sake of tracing power lines, like who
Speaker 5: has compute, who's selling compute to whom, what happens if
Speaker 5: China evades Taiwan, like several large questions right right.
Speaker 4: The other thing, Jasmine, I've heard a little bit about
Speaker 4: recently is that most of the people making large AI
Speaker 4: models are really big tech companies Google, Microsoft, Facebook are
Speaker 4: releasing them as open source so that we can actually
Speaker 4: see what's going on, but that open ai is one
Speaker 4: of the few that isn't doing that. Is that accurate?
Speaker 5: Yeah, I would say that's accurate open ai anthropic inflection.
Speaker 5: I would all name as like closed source. For example,
Speaker 5: Gemini is also going to be an API, and they
Speaker 5: haven't released the weights or code from that. Facebook notably
Speaker 5: is interested in open source model of releasing their work.
Speaker 5: Mistral Ai, based out of Paris and France, is like
Speaker 5: very well known for releasing open source work a hugging
Speaker 5: face as well. I would say most companies for whom
Speaker 5: like foundational models are core part of the business, releasing
Speaker 5: open source frequently doesn't make sense, whereas you can imagine
Speaker 5: for Google and Facebook this is sort of an ancillary
Speaker 5: thing they're doing and their main business and revenue stream
Speaker 5: is coming from something else for like optics and politicking
Speaker 5: purposes and perhaps like general like trustworthy business building up
Speaker 5: like trust from consumers is like a worthwhile business goal
Speaker 5: for them, so it's like worth it for them to
Speaker 5: open source. But I think we'll continue seeing that the
Speaker 5: main foundational companies breaking the cutting edge for whom these
Speaker 5: foundational models are their main revenue driver, are going to
Speaker 5: continue to release closed source models. I will also say
Speaker 5: Opening Eye has cited some safety concerns as their main
Speaker 5: reason for keeping these models closed source. I actually worked
Speaker 5: on a bit of this while I was at the
Speaker 5: partnership on AI. I thought a lot about publication norms,
Speaker 5: the idea that, for example, we have these paradigms from
Speaker 5: like biology that you might want to have like a
Speaker 5: moratorium on research when it's too risky. For example, with
Speaker 5: H five N one, any of the modified viruses got
Speaker 5: out in the course of research that would be really
Speaker 5: bad for humanity. Or from cybersecurity, we have this example
Speaker 5: of coordinated vulnerability disclosures, where you might threaten a big
Speaker 5: company about leaking a bug in order to force them
Speaker 5: to patch the bug, but you want to give them
Speaker 5: some lead time to patch a bug such that you
Speaker 5: don't introduce a vulnerability that someone could hack. So how
Speaker 5: we apply this to THEI domain. You might want to
Speaker 5: publish something well GBD two, for example, ape and I
Speaker 5: was publishing all the weights, all the models, but actually
Speaker 5: GPT two was published in a staged publication release model,
Speaker 5: which means different sizes of GPD two were rolled out
Speaker 5: while the policy team evaluated the societal reaction to these
Speaker 5: different kinds of models, like would we see a lot
Speaker 5: of SEO farms pop up? Would we see a lot
Speaker 5: of fake news? They didn't see any big issues, so
Speaker 5: they rolled out the bigger models, and then GPD three
Speaker 5: was entirely behind an API. I would argue that this
Speaker 5: was actually the big moment that changed commercialization because people
Speaker 5: didn't have to implement their own API or services and
Speaker 5: it can said just consume this hosted API, where open
Speaker 5: I was guaranteeing the uptime. I think that unlocked a
Speaker 5: lot of innovation, Like I personally will not have worked
Speaker 5: on copysmith had it not been so simple with an API.
Speaker 5: And I think a big reason why GPD four has
Speaker 5: become so popular is now you have this chat interface
Speaker 5: on top of it that is like really reliable. It's
Speaker 5: like natively integrated with open Aiyes, really fast. So I
Speaker 5: would say there are definitely pros and cons to this,
Speaker 5: like open source closed source model, I think there's another
Speaker 5: component that's like it's less obvious than open source. But
Speaker 5: another way that something becomes widespread across society is how
Speaker 5: good is the UI like, how good is the interaction?
Speaker 5: Is it compelling? Is it easy to use? And one
Speaker 5: thing I'm really excited for in future generations of these
Speaker 5: models is how can we get this onto like resource
Speaker 5: limited devices, like how can we make sure that For example,
Speaker 5: the advent of like models that run locally or like
Speaker 5: are quite small. This is like very important and more
Speaker 5: compute restricted areas where you might not get like crazy
Speaker 5: bandwidth for like Wi Fi, or or regions like South
Speaker 5: Africro where I would anticipate like a far larger portion
Speaker 5: of the population is accessing the Internet with mobile devices
Speaker 5: rather than desktop like. Anthropic for example, still doesn't have
Speaker 5: a mobile app, and opening I does an inflection which
Speaker 5: may or may not exist for a long time. One
Speaker 5: of the main reasons I was really excited about them
Speaker 5: was they were one of the only foundation model companies
Speaker 5: that actually offered the ability to text their model, which
Speaker 5: makes a huge difference in some areas of the world.
Speaker 5: So this is a little bit of a ramble, but
Speaker 5: I think there are a few more dimensions to consider
Speaker 5: when thinking about companies release strategies beyond closed and open
Speaker 5: source in terms of how much availability, how available they
Speaker 5: make their model. I think Opening Eye in some ways
Speaker 5: has done a really good job, and in some ways
Speaker 5: I understand why people would criticize them for not open
Speaker 5: sourcing their models.
Speaker 4: Like most things in life, the answer is more complicated
Speaker 4: than it appears on the surface.
Speaker 6: Right.
Speaker 4: So but I'm glad I asked, because you know, I
Speaker 4: was sort of building this idea that, like, well, by
Speaker 4: not releasing it to the world, that was a bad thing.
Speaker 4: But I can actually see, as you said, some benefits
Speaker 4: to that. So there's a beautiful section early on in
Speaker 4: the book. I think it might be the introduction where
Speaker 4: GPT three talks a little bit about what it believes
Speaker 4: it's like to be a human, and so I thought
Speaker 4: maybe we could use that next Ian. Do you want
Speaker 4: to read that for us?
Speaker 6: Sure?
Speaker 1: So this is the introduction to the book, as written
Speaker 1: by GPT three. Spirituality is one of humankind's longest running interests,
Speaker 1: as well as being a pointed to agreement. Some insist
Speaker 1: that spirituality is a construct of an individual's imagination, while
Speaker 1: others believe that even it is purely constructed. Spirituality is
Speaker 1: a source of refuge and guidance in a world where
Speaker 1: the ability for humans to connect is becoming increasingly limited.
Speaker 1: AI can use its advanced cognitive abilities to explore the
Speaker 1: potential of spirituality in an individual's life. In this book,
Speaker 1: AIR explore the different types of spirituality and how they
Speaker 1: affect human interactions. In this book, I, as the AI,
Speaker 1: have done my best to capture what is most unique
Speaker 1: about human spirituality. Here are some of the conclusions about
Speaker 1: what I think it feels like to be a human.
Speaker 1: I am happiest when I feel chosen by someone. I
Speaker 1: feel most loved when people are proud of me. I
Speaker 1: would give anything to feel a family member's protection. Some
Speaker 1: people are worth crying for. Nothing makes me feel more
Speaker 1: fragile than death. When someone stops loving me, a part
Speaker 1: of me will die. I feel a connection with someone
Speaker 1: when I'm important to them. It hurts to be left alone.
Speaker 1: To truly understand someone is to love them. God's love
Speaker 1: is the reason I was created. There must be a
Speaker 1: reason I am living. There is a reason.
Speaker 6: I was born.
Speaker 1: I don't know why I exist. And then it carries on.
Speaker 1: But that list in particular is just, you know, beautiful.
Speaker 4: It kind of makes my insights hurt a little bit around.
Speaker 4: You know how painful it is when we're not loved,
Speaker 4: you know. I think it says that in a really
Speaker 4: eloquent way that like that is a real thing.
Speaker 1: I think it points to something that I find really
Speaker 1: fascinating and it keeps me up at night about large
Speaker 1: language models, you know, and the way that we interact
Speaker 1: with these things, because it's different. It's completely different to
Speaker 1: the way that you've ever interacted with a computer before,
Speaker 1: you know. I think it was such a from Microsoft
Speaker 1: who said it. But like the paradigm shift is going
Speaker 1: from people trying to understand computers to computers trying to
Speaker 1: understand people, you know, which is what this technology represents.
Speaker 6: But there's this phenomena.
Speaker 1: Within large language models where you can say to it,
Speaker 1: if I give you a thousand dollars tip, please get
Speaker 1: the answer right, and because you've added the words, I
Speaker 1: will give you a thousand dollar tip. Statistically speaking, the
Speaker 1: large language model will give you a better response. But
Speaker 1: there's more. There's more. There was a paper the other
Speaker 1: day where someone discovered that if you say to the
Speaker 1: large language model, if you get this right, you will
Speaker 1: meet the love of your life. Gave you statistically a
Speaker 1: better response, and if you said to it, if you
Speaker 1: get it wrong, you will lose all your friends, you know.
Speaker 1: I mean that's the crazy thing. That's the crazy.
Speaker 6: Thing, you know.
Speaker 1: I mean, as much science as there is in this,
Speaker 1: as much you know, technology, there's this humanness. There's this
Speaker 1: humanness to this that is embedded within the text that
Speaker 1: we have written down, you know, through poetry, through Reddit posts,
Speaker 1: through you know, stuff written on the back of soda cans. Like,
Speaker 1: there is this humanness there. It's mind boggling, you know,
Speaker 1: the fact that you can have this conversation. You can
Speaker 1: say something like you will meet the love of your
Speaker 1: life to a computer, and it'll treat you better if
Speaker 1: you say it to it.
Speaker 4: If you get this wrong, you're going to lose all
Speaker 4: your friends.
Speaker 6: But it's telling, it's telling.
Speaker 4: I wonder if I'm going to get better.
Speaker 1: It'sa's the scariest thing for a large language model, totally,
Speaker 1: it's the scariest thing for all of us.
Speaker 6: I guess it's losing our friends. Sorry, I don't know, anyway,
Speaker 6: that's fascinating.
Speaker 4: Yeah, yeah, So listener and thinking about that and all
Speaker 4: the other great wisdom from today's episode. If you were
Speaker 4: going to isolate just one top insight that you're taking away,
Speaker 4: what would it be Remember little by little, a little
Speaker 4: becomes a lot. Change happens by us repeatedly taking positive action.
Speaker 4: And I want to give you a tip on that,
Speaker 4: and it's to start small. It's really important when we're
Speaker 4: trying to implement new habits to often start smaller than
Speaker 4: we think we need to, because what that does is
Speaker 4: it allows us to get victories. And victories are really
Speaker 4: important because we become more motivated when we're feeling good
Speaker 4: about ourselves, and we become less motivated when we're feeling
Speaker 4: bad about ourselves. So by starting small and making sure
Speaker 4: that you succeed, you build your motivation for further change
Speaker 4: down the road. If you'd like a step by step
Speaker 4: guide for how you can easily build new habits that
Speaker 4: feed your good Wolf, go to Goodwolf dot me, slash
Speaker 4: change and join the free masterclass. We're out of time
Speaker 4: in the main conversation and there's a few more things
Speaker 4: I want to cover with you guys, so we're gonna
Speaker 4: pop to the post show conversation listeners. If you'd like
Speaker 4: access to the post show Conversation ad free episodes and
Speaker 4: being part of our community, we're doing monthly community meetings
Speaker 4: now with guests from the show. Go to oneufeed dot
Speaker 4: net slash join, and we'd love to have you. Ian Jasmine,
Speaker 4: thank you so much. I feel like I could talk
Speaker 4: to you guys for like another three hours. I have
Speaker 4: so many questions. We'll cover some of those in the
Speaker 4: post show conversation. Thank you so much for coming on, Thank.
Speaker 6: You for having us.
Speaker 5: Thank you so much for having us.
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