How AI Will Change Science Forever - Ep. 43 with Alice Albrecht

AI is going to change science forever. Small scale studies will give way to large scale open data gathering efforts. We’ll shift from seeking broad general theories to making contextual predictions in individual cases. The traditional research paper will change fundamentally. That’s why I had Alice Albrecht on the show. Few people straddle the worlds of science and AI like she does: She holds a Ph. D. in cognitive neuroscience from Yale and is a machine learning researcher with almost a decade of experience. In 2021, she founded re:collect, an app that aimed to augment human intelligence with AI. It was acqui-hired by news curation app SmartNews in September of this year, and she is now the senior director of AI product. We discuss the contours of this new paradigm of science: the growing importance of data in scientific discovery, how AI makes N-of-1 studies imperative—when they’re normally seen as unscientific, the case for big tech to open-source their data for research, and the power of unbundling data from interpretations, in both science and media. Here is a link to the episode transcript. In January of this year, we published Alice’s thesis about how augmenting human intelligence with AI is more effective than attempting to achieve super intelligence through standalone AI systems, and in a happy coincidence, she’s our last podcast guest of 2024. Thank you for listening, and we’ll see you in the new year. In the meantime, this is a must-watch for anyone interested in how AI is changing the future of scientific research. If you found this episode interesting, please like, subscribe, comment, and share! Want even more? Sign up for Every to unlock our ultimate guide to prompting ChatGPT. It’s usually only for paying subscribers, but you can get it here for free. To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Links to resources mentioned in the episode: Alice Albrecht: @AliceAlbrecht The company that recently acquired Alice’s startup: SmartNews The piece Alice wrote for Every about how AI can augment human intelligence: The Case for Cyborgs Every’s product incubations that we discuss in the context of how AI is changing media: Extendable Articles, TLDR

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Dan Shipper?

  Is the substrate of a scientific paper actually the right format to release science in?

• 0:06Speaker 01

  As time goes on, as we get more access to data and compute, scientists do become more competition by nature, even if they never were before. It's useful to publish papers. It's useful to get this information out there. The problem has always been we only publish certain things. And so we're not getting any information around what's failed or what's been tried.

• 0:24Dan Shipper?

  There's all these places in machine learning, in science, in journalism, where the story and the underlying data that were used to create the story were always had to be bundled. And that created a lot of problems. And the solution is not to get rid of stories. We are now in a place where the underlying data is actually probably just as important as the story.

• 0:59Dan Shipper?

  Alice, welcome to the show.

• 1:01Speaker 01

  Thanks for having me.

• 1:02Dan Shipper?

  So for people who don't know, we've been friends for a long time. You are the founder and CEO of recollect.ai, which you recently sold to, what company, what company bought you?

• 1:14Speaker 01

  Smart News.

• 1:15Dan Shipper?

  Smart News. I'm excited to have you on here because you're just one of those people in AI that's actually like been doing it for a really, really long time. And you have like, you've seen many different AI summers and winters.

• 1:31Speaker 01

  Yeah.

• 1:32Dan Shipper?

  And I just find you to be incredibly smart and thoughtful and have built a business in the space that you sold. You said it was an aqua hire. So like, I just want to like, kind of get into like, what happened in the business? You learned about running a company in this era of AI, kind of like what's on your mind now? And I'll say for, for people listening, like, I really liked Recollect as a product. And, and we had not caught up since the, since the aqua hire. So like, we're just, we're going to just do the conversation that we would normally do, just like hanging out together. So welcome to the show. Give people a little bit of an introduction to, to the product and the acquisition process, and then we'll take it from there.

• 2:10Speaker 01

  Yeah, well, I'm excited to be doing this. We have not gotten to catch up, so this is good. So yeah, Recollect was, which is interesting to talk about it in the past tense now, but.

• 2:21Speaker 01

  It feels, I don't know, it's bittersweet. Like, I'm proud of what we did and I'm excited about it, but yeah, it's, I don't know, it's hard.

• 2:30Speaker 01

  Just because I sat with it for so long, which I'll go through, sort of the journey of building in this time of AI and how everything feels really, I don't know, time just feels like it's accelerating faster than it would have if I think if I'd built this company at another time, right? Like, if it was a software company five years ago with no AI, I feel like it would have been a different experience or a different journey. In any case, so Reco Act was really focused on knowledge workers. You got to try out different versions of it over the years.

• 2:57Speaker 01

  But our main goal was, can we take all the stuff that you're consuming? Could we connect it for you in this really personal way, the way your mind would? And then how do we make use of that for knowledge workers? So we started out with this tool and we had a few different interfaces over the years when we worked on it. But that you could recall what you were thinking if you were doing writing or research. This is sort of pre-chat GPT times. And then earlier this year, wow, yeah, this calendar year, we had shifted into like, don't tell us what you're thinking about. Tell us what you want to accomplish and then we'll bring all the materials to you. We'll do the synthesis for you as needed or sort of alter those materials in a way that's useful to you as a knowledge worker.

• 3:38Speaker 01

  And everything we were doing was really aimed at how do we make all this stuff that we have access to accessible and useful for you? With the goal of enabling human intelligence, really rather than the machine intelligence piece of it.

• 3:53Dan Shipper?

  Yeah. And I just resonate so much with that mission or that like

• 4:03Dan Shipper?

  goal.

• 4:05Dan Shipper?

  Like, that's, that's one of the things that I, got me so psyched because I'm a little bit more of like a latecomer to AI than you. Like I, you know?

• 4:15Dan Shipper?

  And so one of the things that like got me psyched about it is, is that I feel like I've been such a nerd for

• 4:26Dan Shipper?

  tools for thought or technologies that expand the way we think or the way we see ourselves or the way that we create things and what we understand ourselves, like all that kind of stuff. And it just feels like there's so much there and you were playing in that space.

• 4:42Dan Shipper?

  And I'm kind of curious, like, yeah, what did you, what did you learn about trying to build a business there that you didn't know before?

• 4:50Speaker 01

  Yeah, I think a couple of different things. When I started, like, I left my full-time gig to work on this in 2019. So things were really different at that point, like Burt and L M O, these early models that come out, it's all an opportunity. The ideation phase on like, okay, we have a capability. What do we build with it? What's useful in the world of this capability? Like, what's possible now that wasn't? That was an interesting process. It took, I'd say, a year and change, probably with COVID too, stuck in there and all of the chaos that surrounded that. But the journey I'm building a business for it, I think, was it had these separate phases. One of them was, I would say, like,

• 5:31Speaker 01

  we have this really nascent technology. Nobody really understands it. I'm trying to explain what it's possible, what's possible to do with it. But the models really weren't quite, this is like GPT two, maybe, like, they weren't amazing yet. Like, I could see potential, but that was hard. I think once the models got a bit better and especially after like ChatGPT launched, everybody woke up and was like, wow, this is really cool. And then we had this middle phase as a company where I still was a bit skeptical about, okay, do we shove this into a product and have it generate things? Do we trust the models to do that in a safe way, in a sane way? And I thought, no. And so we had this middle phase of company building where we were launching a consumer product. We wanted it to be something that was useful for people, but also

• 6:17Speaker 01

  like navigating all of the, I guess, like, oh, we could do that, but is that really in line with what we want to do? Or is that just because like something new came out with the model and we could veer that direction?

• 6:29Speaker 01

  That was interesting to navigate. I think on the whole. This is now like, having gone through the whole cycle. If I had to do it over again, or had the opportunity to do it over again, I would have launched the product much earlier.

• 6:44Speaker 01

  I would have let the messiness be okay with customers and just see what happens with that. I think we did a bit of trying to protect that and say, okay, we want to have this integrity. We want it to be. Yeah, we wanted to feel like a really seamless experience. We don't want them to really feel the weirdness of using these AI tools.

• 7:05Speaker 01

  And then the third phase was really.

• 7:08Speaker 01

  As these tools are way more widely adopted, they're in everything. There's this huge explosion in this space.

• 7:15Speaker 01

  Trying to think about, okay, what is the actual application other than retrieval? Because retrieval was such an easy one. I mean, we were like in, we were doing a lot of retrieval stuff in the beginning. But then what happens after that? Like, once you retrieve a bunch of things, what is useful to do with those things?

• 7:33Speaker 01

  Yeah, so it was, yeah, it was an interesting tripart journey, I'd say.

• 7:38Dan Shipper?

  That's interesting. I definitely, I think that's such a common. Like, thing and it's so hard. It's like, easy to say and hard to like actually internalize is just like. Trip something faster that you're like not as proud of and just see what happens, kind of.

• 7:56Speaker 01

  Yeah.

• 7:57Dan Shipper?

  Be very, can be very scary because obviously like you want to make the best product possible. And it's like a weird thing to be like, sometimes making the best product possible is starting with, is doing the messy thing or whatever. It's just like a weird. It's a weird thing to get, to get into your brain.

• 8:14Dan Shipper?

  I think one of the, one of the things I'm interested in is.

• 8:20Dan Shipper?

  When you started, it was like BERT and maybe GPT two-ish days, like. What made you be like, okay, this is happening now?

• 8:33Speaker 01

  I think they willed it to happen, honestly. Like, even in like going out, we did the typical like Nisi fundraising process too. And so I really was, there was no moment where I was like, okay, yeah, we finally hit it. I was like, no, no, this is going to happen come hell or high water, even if I have to meet this true.

• 8:52Speaker 01

  So, yeah, I feel like it was really convincing other people that like, no, no, no, these embeddings I'm talking about are really, really important.

• 9:01Speaker 01

  Yeah, and that was hard.

• 9:03Dan Shipper?

  Okay. And what do you think about, like, you wrote an article in Every a while ago, and I read it, I edited it a really long time ago, so I only have some of it in my brain. But basically, I just feel like you have a really strong and clear thesis for like, how humans and AI can work together and what that should look like or could look like. And it's a sort of like cyborg hypothesis. And I wonder if you could like lay that out, because I think a lot of people, it's a big question on people's minds, like, where are we going if we're going to use this as tools? Like, how does it work and also keep us being humans and employed?

• 9:48Dan Shipper?

  And then I'd love to talk about like how that has shifted for you over years, building this company.

• 9:55Speaker 01

  Yeah. And it certainly has shifted, which has been interesting to kind of catch myself in between, you know, you say these statements in the world and then something changes and you're like, oh, you have to readdress or reassess the whole situation. So the cyborg piece, I'm still pretty hell bent on. I think this has been my shtick for a long time now, coming from human studies to, you know, working with machines. I have this belief that humans are really kind of amazing creatures and that if we're going to use a technology, it should be to augment them rather than to just build an AGI. Like, the goal for me is not some sort of superintelligence that works without us. It's how do we start to connect this technology to humans? And really finding those touchpoints. I think knowledge is one of those.

• 10:40Speaker 01

  So if we think about. You know, knowledge work in general, a lot of it is consuming, distilling information, understanding, and then really kind of propagating that information out to other people, other other knowledge workers in those systems. And so for me, I think a lot about how can we use AI or machine learning or these other, any of the other technologies to make that process easier for humans so they can do the creative piece. And I think that's where we get to how we all stay employed. I'm a big proponent of, you know,

• 11:16Speaker 01

  lowering the bar, raising the ceiling, I guess. So what's possible, we don't even truly understand quite yet. But like, if we can find the touchpoints where it makes a lot, like it makes that human work a lot easier. And I don't think we're quite there. I don't think chat is the right interface for this. I don't think the models have the right context for it.

• 11:37Speaker 01

  I think, and I say this again and again, like, I still think we're going to need some sort of biometric feedback piece in there to really make this work.

• 11:46Speaker 01

  And in that case, it's about finding where humans are not deficient, but where they kind of struggle from their evolutionary constraints, seeing where the technology can can come together. And it's not like, you know, creating artificial eyes. That's pretty cool. But that's not exactly my thing.

• 12:05Dan Shipper?

  Yeah, that makes sense. I was laughing earlier because I feel like we have this like recurring sitcom or soap opera or something that every time New York Tech Week comes around, we're like on a panel together. And like, the first year, it was like me and you, and it was like right after ChatGPT launched, and it was like a couple of other people. And I won't say who or whatever, but like, there was one person on the panel where me and you were just like looking at each other the whole time. You're like, what does he say?

• 12:32Speaker 01

  What is happening? Yeah.

• 12:35Dan Shipper?

  That was really fun. And then the next year, we were on another panel about AI and creativity or something like that. And we were on different sides of the debate because we were talking about chat interfaces and whether or not chat interfaces are are the future of AI interactions. And and like you said, just now, you think the answer is no. And I was I was defending defending chat. And I'm curious, like, how that position has evolved for you and why, if you could refresh my memory, why you think chat is not the right interface?

• 13:10Speaker 01

  Yeah, um, trying to remember the argument I made on this panel. I probably won't be able to recall it exactly. And I'd also love to hear how your position may have changed over time. But so I think language is a super powerful tool, right? We're using it right now to communicate with each other. We read like we consume it in this way. I don't think that it is the most natural way to access things like this. I think we've actually moved farther in this direction, probably since that panel, too, with all of this agent stuff happening, where we have this kind of thing that does work in the background for us, but it intuits what work to do a little bit more. And it accomplishes that without us saying like, hello, robot, please, blah, blah,

• 13:54Speaker 01

  blah, blah, blah. And I think the the only way I've come maybe a little closer to the chat piece is I've used it more. Like, I find myself in new spaces, like where I'm like, OK, I just started a new company. I'm in a new area where I'm thinking through things. It has become a little bit more useful for me to to say like, OK, I'm thinking about this. What are the pros and cons, right? And right now, I have colleagues that are in Japan, where 13 or 14 hours removed. And so it's helpful for me in that sense. It's still not my main interface. So so I think I'm

• 14:38Speaker 01

  doing so I'm doing more coding, too. And so I think like, the main use for me right now is code. And that's sometimes chat.

• 14:46Dan Shipper?

  Cursor or like, what are you doing?

• 14:50Speaker 01

  So I'm coming back to Cursor. I had used it really early on. It was really buggy and I was like, this is ruining my getup. I can't use this. This is terrible. I'm coming back to that. Yeah, so I use VS Code. I have Copilot, all the regular things. I do some chat. I really like the Claude artifacts. That has been a really big game changer for me.

• 15:14Speaker 01

  And this is where maybe my chat argument does hold up. So it generates these, basically like little maps of things, right? Like you can have it through these mermaid diagrams as one of the artifacts. And I think really visually, I draw things out a lot. I'm famous for having this remarkable with me everywhere I go and sharing these, like, I think I put these in one of my articles I published. Like, this is how I think. I don't really think in this chat back and forth situation. So it generating that has been interesting for me too, as a way to collaborate in a non-text way.

• 15:47Dan Shipper?

  That is interesting. Yeah, I guess like, so for me, and when I talk about chat, I would like include like, you know, voice, video, like, and the sort of like back and forth

• 16:04Dan Shipper?

  kind of way. And but maybe, maybe it should be, maybe it's like more properly limited to just text. And I think probably the reason I like chat as an interface is like, I'm just so verbal, like I'm just so worded that it just like makes a lot of sense. But if I try to turn my own personality into like a general truth about like what's good, which I think is typically how things work. If I try to do that consciously,

• 16:35Dan Shipper?

  I think the reason that I, that I like chat as an interface is it allows you to push forward along many different dimensions simultaneously,

• 16:51Dan Shipper?

  where a lot of other software interfaces are like, it's either on or off, or it's like, it's on one axis at a time, or something like that, which is quite useful for refining something.

• 17:09Dan Shipper?

  Sometimes for finding something or for for processes that you're doing where the dimensions that you're improving along are like really well known and really well understood. And it's sort of repetitive.

• 17:26Dan Shipper?

  But then there are a lot of other processes, particularly creative processes where you're like trying to explore space along a bunch of different dimensions. And you don't really know that what the dimensions are beforehand. And I find that to be quite good for that exploration process and for the like refinement process of like, okay, you like produce this thing like now I want you to like push it in this way or whatever. Define what that was before, but now I see it now I know. So a really good example is we've been we've been incubating products inside of every which is really cool. I would love to.

• 18:05Dan Shipper?

  And one of the products that we incubated is this product called Spiral. It helps to automate a lot of like the repetitive creative tasks that you do if you're running a company or you're a marketer or you're a creator. So an example would be like for this podcast, I'll say I have to take this podcast and turn it into a tweet to get the episode out. And that's a very repetitive process because I kind of have like a format that I know works, you know, like I have a good first line and then I have like a couple of bullet points about like what are the key topics that we discuss that I think are interesting or whatever. And I realized that Claude is like really good at doing this. If you give it, if it's a few shot profit, you give it a bunch of examples of like podcast transcripts that turned into tweets, then it can like do that over and over again. And it gets you like 80% of the way there.

• 18:50Dan Shipper?

  So we built Spiral where it's basically like a few shot prompt builder. So you can make a spiral for turning podcast transcripts into tweets, or you can make one for turning blog posts into LinkedIn posts or turning, you know, whatever, release notes into a product announcement or whatever.

• 19:10Dan Shipper?

  And I'm giving you this very, very long-winded explanation because one of the things that we found is like in the in the spiral interface, when you have a spiral, let's say we take my podcast transcripts to tweet example, you just paste your transcript and you press run and then it gives you a bunch of it gives you a bunch of example tweets that you can try. And like one of our biggest pieces of feedback of people wanting an improvement is like, I just want to chat so that I can say like, this example was good, do more of that. You didn't really think that that would be the case. Like, we had ideas for like, maybe we could do sliders, or like, maybe we could

• 19:50Dan Shipper?

  like, really, what you should do is go back into the spiral creation flow and like modify the prompt a little bit and like make the prompt a little better. And what we're finding is like, the natural thing to do is, it's sort of like, you know, you run a company, it's sort of like, when someone that's reporting to you comes to you and it's like, I did the thing that you asked for, and you're like, okay, this is great, but here's a couple of things I need you to do. That's a very natural way to to kind of push something in a sort of multi-dimensional, somewhat unknown space. Does that make sense?

• 20:25Speaker 01

  It totally makes sense. Yeah. And I think that like, using AI today is really good for this, right? Like, there's like, first draft, be it, you know, create the tweet for me, write some code for me, right? Whatever, like, any kind of production thing. In that process, though, right? I think there's this very interesting teacher-student relationship, which shows up, I think, there's some new stuff around this, around training tiny models based on big models, and those are really cool. But I think the interesting, there are like, two interesting pieces there. One is a human in the loop, and this, I think, we do usually agree on, which is like, the human creative piece of this, right? Where you have the judgment and saying, nah, this one's not quite right, but here, change this one. Or like, I can choose from this list.

• 21:07Speaker 01

  Awesome. The the cloud model or whatever you're using doesn't have enough information, or intuition, or something that seems a little bit fuzzier in there, to choose, okay, yeah, this is the best one, just roll with it. I think that's going to continue to be the case.

• 21:24Speaker 01

  And then the other piece in there, though, is this knowledge sharing, right? So in a sense, when the, when Claude or whatever model you're using outputs these tweets through you, you get a little bit of a peek into how it's like, quote unquote, thinking, right? Like, it generated these things. You can see, these it got kind of right, these it got kind of wrong. You could give it feedback, but you're getting insight into that. And then you give it data. It's also, it's not learning in real time with a few shot pieces, right? But maybe at some point it could. And I think the more that you get a system that it knows a little more about what you know and how you, like, I don't even want to call it preferences because it's so squishy.

• 22:07Speaker 01

  You can't like learn these preferences easily.

• 22:10Speaker 01

  But the more that you and the model can get to a shared understanding of what the other thing knows and can fill in the blanks, right? You can say like, oh, yeah, you wrote that one, but you forgot or you didn't know or, by the way, actually add this piece because this is critical in there.

• 22:27Speaker 01

  So I think it, this, this, this way of interacting and not even to me. I think the original like chat interface of chat GPT where people are like, yeah, I can talk to the thing and everyone like chatbots. This is already beyond that when it creates artifacts, right? It's not a conversation. It's not like, hey, I think you should generally do, it's like, remember, here's your thing. Here is your output.

• 22:47Speaker 01

  And that's gotten really interesting.

• 22:49Dan Shipper?

  Yeah, that is, that is interesting. Yeah, we definitely like, I was arguing against chat interfaces and then the product that I built was like a chatless interface.

• 22:59Dan Shipper?

  Uh, which is, you know, like, I think, I think, yeah, it's your point, like, um, that, like, reducing that down is actually like, it can be helpful. Um, and then, you know, we, we need to bring it back in some form, but it's, you know, there, there, I don't know, there's, there's interesting, there's interesting trade offs to that. And, and the form that we're bringing it back is definitely not a totally general interface. And that's the only way that it. It can compete with Claude, because you can do the same thing in Claude. It just has to be very specific to your, um, to your purposes.

• 23:30Dan Shipper?

  Um, I, I've been thinking about doing a thing and something about the shape of it makes me feel like you would be into it, or like have interesting thoughts on it and whether it would work and what I should think about. Um, so I'd love to, I'd love to talk about it. You said something earlier that, that reminded me of it, which, which, but I forgot what it was. So let me just, let me just lay it out for you. Um, I'm pretty curious, so, okay. I don't, I don't know whether, whether we've talked about this before, um, but I have OCD. One of the things I've been thinking about or, or lightly trying is I wanted to see if it was possible to, I wear a Whoop, so you take my Whoop data and be able to label from the, from the graphs,

• 24:15Dan Shipper?

  whether or not I will experiencing OCD on a particular day.

• 24:20Dan Shipper?

  And so far, my answer to that is like, kinda maybe, but like, it actually probably needs more context to know.

• 24:30Dan Shipper?

  Um, uh, cause like what a spike means on a, on a stress graph in a Whoop like can mean a lot of different things depending on, you know, what's going on in the background. And what's really cool about these models is now they, they can, they know enough to like be able to take the context and use that. And so I'm not far enough along, um, yet. Like another thing I'm going to try, um, is I'm starting to do daily like two-minute video journals. And there's this like emotion labeling AI called Hume. And I'm, I'm going to see that, that can label it. So I don't know that there's, there's some interesting things there where I think it actually, I, I feel pretty confident there's some combination of data where I can get from data to label, um, to be like, yes, like he's having OCD symptoms today or no, he's not.

• 25:14Dan Shipper?

  And when I'm, once I'm there, the big question to me is will it be possible to predict, let's say a day in advance, whether or not I will start to experience OCD, or whether or not I'm in a sort of OCD phase, like whether it'll go away.

• 25:32Dan Shipper?

  Cause I think once you can predict it, opens up lots of interesting things.

• 25:38Dan Shipper?

  And so my thought for how to do this is to basically do a bounty and do like a 10, 10 grand. If you can predict my OCD, here's a data set. Um, and let anybody, cause everyone, you know, you, you can have, you can use O1 Pro for $200. Anyone can like do this basically now in a way that they couldn't before. And like A, see if that works and then B, maybe build that into like a sort of Kaggle like, um, but we're now, I mean, I was a data scientist, right? And there's probably a lot of things that, that you would be into about this, but like, uh, or have, have thoughts on.

• 26:19Dan Shipper?

  Um, but the thing that I'm kind of interested in is like, I think making predictions about, and maybe we talked about this a bit, but I think making predictions about whether or not I'll have OCD is a form of science, but it's a form of science that like, a scientist would never do, cause it's like an N of one experiment and you're not actually looking for causal explanation, you're just predicting. Um, so it's like completely taboo to like the establishment of like, you know, research, but it's completely incredibly useful. And I think now doable, where I think we should be doing, be doing a lot more of these kind of N of one things.

• 27:01Dan Shipper?

  Um, and, and pursuing more predictions over, um, over like, like underlying scientific causal explanations. And I just thought that that would get your brain going and you'd have interesting things to say.

• 27:13Speaker 01

  Totally. Yeah. I appreciate you reining in too, cause I'm like, I have all of these pieces I can talk through. Oh, yeah. I think the N of one thing is interesting because like, if you came to me and you're like, I am in great pain from this, right? Like you were like, I am suffering. And as my friend, I would be like, gosh, how do I help you? And if I thought I could slap together a model, you got your move data, great. Like it'll take me whatever amount of time, but like, I could just do this. What I, I wouldn't actually go to one of the large language models straight away. Like that, like my brain would say, we need a predictive model. We need to know, understand the data sources. So I think it would be an interesting combination of looking at the actual like

• 27:55Speaker 01

  research, research, right? So like, there is research out there on OCD. I'm not an OCD researcher.

• 28:01Dan Shipper?

  No physiological, as far as I can tell, there's no physiological marker research.

• 28:06Speaker 01

  Fascinating. That is actually the most interesting.

• 28:09Dan Shipper?

  And I did, I did buy an at-home EEG to see if I could do stuff that's all, it's all like MRI stuff. So it's not, it's not usable by me.

• 28:18Speaker 01

  That's so interesting. That's like a whole other podcast in and of itself. Like, why do we not have biological correlates for a thing that is fairly common in the population and probably has like at least a heart rate difference? Like, something has to change.

• 28:31Dan Shipper?

  That's true for pretty much all mental, uh, mental illness is there are no bio, bio, biomedical markers, biophysical markers. If it is, it goes from like in psychiatry to being neurology. So like, like certain things used to be like mental illnesses that are now just neurological issues.

• 28:52Dan Shipper?

  So yeah, it's, it's very, uh, like, all of psychology is basically built on self-report, as far as I can tell.

• 28:59Speaker 01

  I know, I know. It's true. Yes, I believe that also. So, okay, I could make some like easy predictions off the bat and go, okay, heart rate data probably important as a predictive measure of this. If you can give me labels, right? It's a question of how many labels do you need to make this work? Is it 700? And then you're suffering for a really extended period of time. If we think about like, I don't, you know, how long these episodes might last. And so I think if I were in a certain, I might actually, the way I would use these models though, is to like, A, help come through literature and say, okay, I'm looking for this like pretty specific information. How do you help me jump into a field that like I'm, it's not mine, but it's adjacent to one I know enough.

• 29:43Speaker 01

  But then also help me build the model, right? Like, help me think about how to build this predictive model. Here's the data that I've got or the type of data. Um, help me not build an LLM, but build a model that has predictive power that is well beyond what the LLM would be able to do.

• 30:00Speaker 01

  Right? Because the LLM isn't really trained to be a predictive model. It's trained to predict the language and it has reasoning capabilities from that. But there's lots of other models out there that would take all this data, normalize it, put it together and say, okay, now we can build a basic predictive model, which would be a great taggle exercise in and of itself, right? If they, if, I don't think it exists, but. So I think the piece that's interesting is translating your, uh, like if you have a video sort of like daily, I don't know, check in or something like translating that into something that is useful for this kind of model. Um, Fei-Fei Lee did some really interesting work a really long time ago. Um, she's sort of like the mother of AI, I guess we can call her. I don't know if she

• 30:44Speaker 01

  would care for that label, but she had a fascinating paper and it's really long time ago now, but it was taking different data inputs. So it was video data, like people talking and they could predict depression, um, or the onset of a depressive episode. And it was the facial expression. It was the cadence of their voice, uh, the tone, like all of this data lives in this. And I don't know that there's anything correlated for OCD. Um, but I think you could use these models to sort of transduce that information into some meaningful signal for a different model.

• 31:18Dan Shipper?

  Yeah, I think you're right. So basically with Hume, the cool thing about Hume is it, um, it turns your, um, it turns your video into the equivalent of like emotion embeddings.

• 31:31Speaker 01

  Mm hmm.

• 31:32Dan Shipper?

  Which is pretty cool. Um, and so each frame, it like for your voice and your facial movements and whatever, it like outputs an embedding that represents what it thinks where you are in the emotional embedding space. And so my guess is that can be used and fed into some sort of predictive model, but I don't know.

• 31:50Speaker 01

  Yeah. And if you had it over time, right? So if this model ran frequently enough that you get like time series data that's meaningful, even if it was once every 10 minutes or something and you could aggregate over your group data, you could, you could bring it, I could totally see how you could put these pieces together and then say, okay, I can label for you when I'm having these episodes. I can label for you when I feel like if there is a, if you have a sense when they're starting to come on versus fully in the middle of this versus it's at the tail end. Um, I think it's totally possible.

• 32:20Dan Shipper?

  What you have so far is a more of a, like, I built this little app for myself where I do retrospective assessments. So that in the morning, I'm like retrospective, like, how was I yesterday? Like, what were my symptoms if I had any? And then I upload a screenshot of my Oop graph and then I do a two-minute, like, check-in of, I don't say how I'm feeling, but I just talk into the camera about like what I'm gonna do that day or something like that. Cause I don't wanna like give away the label. So you're saying maybe I should be doing more like momentary type data gathering to like if I'm in the middle of something, like try to catch it if I can, you know?

• 32:55Speaker 01

  Yeah, yeah. Without aggravating the symptoms, obviously, right? Cause I can see how that could spiral. Not like a fun time, but like, I don't really wanna get into a weird thing where like, what if I can like keep doing this thing?

• 33:07Dan Shipper?

  Well, one of the funny is like the treatment for OCD, one of the big treatments is exposure. So like, um, I can just, it's for science, it's exposure for science.

• 33:18Speaker 01

  Talk it up to exposure therapy. Oh, gosh, yeah, yeah. But like, I think if I was gonna model any kind of like, I don't know, psychological thing that was happening periodically, I would want the data from like right before, in the middle, so you've confirmed it's true, right? And then at the end and see, okay, like, what are the shifts and changes that happen?

• 33:40Speaker 01

  And then I think what's interesting with the models also is like, you could get other contextual data that you don't have, right? Like, location is an obvious one, like, where are you generally in the world?

• 33:51Dan Shipper?

  My calendar.

• 33:52Speaker 01

  Your calendar, yep. Who are you talking to? What are you doing? What is your workload? We've got all these sleep predictors, right? Like the Oop is good for saying like, did you sleep well the night before? It does all these other analyses.

• 34:04Dan Shipper?

  How would I get that into a non LLM though? Like, let's say I have a calendar, like, let me tell you what I've been doing, cause it's probably, you're probably gonna laugh, cause it's so dumb, but like, what I've been doing so far is like just taking the images and the labels and just like throwing them into Claude in O1 and being like, based on these, here are labeled images. I want you to like come up with a set of rules to like classify whether or not I'm having OCD or not. And it's currently not very good.

• 34:34Speaker 01

  I wouldn't expect it to be, I would be pretty shocked if it was like you might even open up like a portal into some other like whole other research field. Yeah, it would actually be fascinating if it worked. So what I would say is what you need for predictive model, right? Is you need to get all of these signals as features. And then for like a something that has temporal dynamics to it, right? Like you can get, I think you can get it off the loop. Um, I think I tried at some point, but if you can get the.

• 35:01Dan Shipper?

  The daily summary, but not the momentary statistics. You have to like, I think I created data pipeline because I could just take a screenshot and then feed it into Claude and Claude will be like, here's what the uh like make an SBG that like matches the line of the loop graph and then turn that into time series or something. I think.

• 35:18Speaker 01

  Time series. Yeah, that's actually really cool. I didn't think of that. Uh, so that's very clever as a way to get this data. So what I would say is like, you need temporal data that's fairly time locked. And then you need to convert all of these pieces into features, right? So you have your video that you're taking at a certain time. And so like you would need to do some processing on the video to pull out and you can use human, it sounds like to do that. Um, but pull that out, have the timestamp associated with it, have the actual time series of the loop data. Have like as much of that sort of queued up to these time pieces. And then those are features that go into a basic classifier. Um, and it depends on the Tesla, you can, there's lots of different ways to classify data. But all you're doing then is, is like providing information to the model, a non LLM model.

• 36:03Speaker 01

  Um, to say like this or this, right? And you kind of have a binary classification unless you do predict like, oh, it's oncoming, it's happened, it's fading, right? Like if you, and that's, that's also not a terribly difficult classification task, right? My bet is that if you asked Claude, if you said like, hey, I've got this data, turn it into a time series for me. Here is my video data with timestamps, like translate that into features. And then write probably in Python, like a model for me, like a predictive model, basic one. And help me plug these features in like, I think you might be able to get somewhere with that. I think the problem is asking an LLM to do the predictive work because it's not really set up to do that. If it didn't execute other models.

• 36:48Dan Shipper?

  That's interesting. What about, um, because I mean, you can use an LLM as like a basic classifier model. So you're just saying that it's like classifying a different kind of, um, a different kind of sequence. And so the, the features that it's going to pay attention to in a text sequence are just going to be different from the features that pays attention to in like a, you know, OCD kind of more sequence.

• 37:12Speaker 01

  Yeah. And depending on like, I know there like there's multimodal aspects of models now. I actually don't know that LLMs are the best for like classifying certain things. And so like even now, I'm still saying, okay, third topic is a different kind of model. It's a large model, but it's not OpenAI or N-topics models, right? Like models that are specific to pulling out tags or categories or things like that from text data are actually still a little better than just throwing it into the big model. Um, they're more specialized. So that might change as these models get better, but I don't think that these are in and of themselves better than

• 37:57Speaker 01

  like, um, other machine learning models that are really meant to classify, especially time series data.

• 38:04Dan Shipper?

  That's very helpful.

• 38:05Speaker 01

  Yeah.

• 38:05Dan Shipper?

  I'm like, basically feel like, like what I feel like I'm doing right now, which I didn't even realize I'm doing is I'm like, trying to kill a mosquito with a rocket launcher or something like that. It's like,

• 38:14Speaker 01

  It's a beautiful, yeah, totally. But it, it can help you build the right ones, which is cool.

• 38:19Dan Shipper?

  Yeah, yeah, yeah. That's interesting. Um, well, I guess I want to, um, maybe pull us out of the like specific details of this particular, um, engineering problem and more to the like higher level of like, I'm thinking about how this might change science and how this might change how we do science that I'm curious for your thoughts there.

• 38:39Speaker 01

  Yeah. I think it's already probably changed fundamentally how we do science from the knowledge standpoint, right? Being able to comb through all of that information and pull things out, like has saved graduate students and postdocs, makes people like countless years of their, I don't know, it must like that, that part feels incredible to me. Um, so that in one way is how we change science. The second way will be simulating. So.

• 39:06Speaker 01

  The data is huge to train these models. We have all the hardware for these things and they're getting good at simulating data, good enough. Like if you have a solid existing data set, you can say simulate lots more data like this.

• 39:20Speaker 01

  And that is incredible if you're trying to like, understand the possibility space as a scientist.

• 39:29Speaker 01

  And, and kind of window that down to, okay. And it's hard for humans to do, right? We can make predictions, we can synthesize data, but, but keeping in mind, all of these different kinds of possible future states is really, really, really hard.

• 39:45Speaker 01

  So I think it'll change science in that way. I don't think we're going to lose scientists. I don't think we'll have AI scientists, really. I think there is a problem around, uh, like, I think humans still do a lot of the hypothesis generation, which is a lot of the science.

• 40:01Speaker 01

  And still thinking critically about, okay, what are we, like, what kind of data are we even trying to get to, like, understand this, like, to even get started on this, this space or this hypothesis?

• 40:14Dan Shipper?

  That's interesting. I mean, that's, that's sort of like where, what, what I've been thinking about is like, okay, in a world where, um, the data is actually like, the really important and like rare thing, and you want to get really good data, is the substrate of a scientific paper actually the right format to release science in? Like, for example, there's a lot of like, open data type, type pushes right now. And, and is the idea of doing like a 16-person study actually at all interesting? Or should the project of science be to gather as much, um, as much good data sets about

• 40:58Dan Shipper?

  problems that we care about as we can and to aggregate them and then allow any scientist to build models on them rather than writing papers about them? I mean, papers are fine, but like, in general, like, hmm, there's a huge overproduction of papers and huge underproduction of like usable, good data.

• 41:16Dan Shipper?

  And, um, and, and then if, if, if it moves to production of good data and, and, and basically building good predictive models, then like, For me, it like it feels like and I've been on this like this soapbox for a while like for me like I feel like we should be actually going for predictions first before we do the causal explanation stuff, especially for things like psychology or psychiatry or whatever, where the causal explanations are really, really, really, really, really complicated. Because if you can high depression or predict my OCD or predict what intervention is going to work, it's like life changing and and and if you have a good enough predictor, like maybe the explanations are in

• 41:59Dan Shipper?

  there somewhere. So that's my that's kind of where I've been thinking about like, okay, science may change and it may have to change in this particular way where it looks a lot more like engineering. It looks a lot more like data gathering and model building. And and maybe a lot more like, and this, I think you'll have opinions on which I would really like, maybe a lot more like the shift that we went through from symbolic AI to sub symbolic AI, maybe that shift where, you know, we were trying to find rules and logical, this is not for you, it's for people listening, rules and like logical formulas like define what intelligence or intelligent decisions what was. And then we

• 42:41Dan Shipper?

  were like, actually, why don't we just throw a bunch of data at a model and like, it'll just figure it out. That shift, maybe we need to apply that to the rest of science and a lot of other areas of the world. And and that would actually be really, really helpful.

• 42:58Speaker 01

  I like the analogy they're drawing between sort of like the production of papers right now as being the more symbolic approach where we've got this really specific thing that somebody's asking a question about. They gather small data usually, and it really depends like, science is such a broad field. So I also don't want to like shoot myself in the foot with like, well, there's so many kinds of science. My my space that I used to work in is like small end studies, right? Like, you know, maybe 10, 20 people. It's not huge. Drawing conclusions from that is hard and it's hard to replicate. But if we say, okay, the like more symbolic pieces of this are like rules based. And we're saying we've learned something really, really specific about this very narrow thing. And now we can create a rule around that. And then the next person comes along and says, like, I follow this rule because you published a paper moving up.

• 43:43Speaker 01

  So I think a couple of things in there. The I do think we should be publishing a lot more data. I think that the data asymmetry between what people can use to train models, let's say like the large language models, right? Like all this text on the internet. That was a huge unlock now that we have the compute power to deal with that amount of data. We have the compute power though. So if people release data sets in other domains, it would be potentially like this very like synergistic and like, um, maybe sort of like almost not a linear, but maybe like a log scale improvement and things, right? Like the more we get this combinatorial power of lots of different pieces of this broader data set that everybody gets a chance to see, right?

• 44:26Speaker 01

  And so I do think like as time goes on, as we get more access to data and compute, scientists do become more computational by nature, even if they never were before, right? They have access to the combinatorial like tools in their toolbox to answer questions. And then I still think it's useful to publish papers. Like it's useful to get this information out there. The problem has always been we only publish certain things. And so we're not getting any information around what's failed or what's been tried. And so we really are only getting a very weird and skewed slice of science, as it were. So I don't think we need fewer papers. We might need more papers. And if we have a tool to help us sift through those, maybe it doesn't matter.

• 45:10Speaker 01

  But also, if you do make data available, there are people that are able to use it for lots of different things that you may never have thought of. The hard part at speaking as a former scientist comes in that the way that you chose to collect that data is incredibly important, right? What were the, like, what was the setting you were in? What were, like, all the way down to, like, the refresh rate of the monitor often?

• 45:37Speaker 01

  So making this transferable is hard.

• 45:39Dan Shipper?

  That's the thing is like, I feel like there's this big push, like, basically, there's a big push to generalization. That's the whole object of science as we've, as we've construed it because of, basically because of Newton, and like, you know, like, in general. I think what we find in, like, cognitive science or psychology or whatever, like, it's so contextual that every time people have claimed to, like, have something really, really generalized, not every time, but like, a lot of the time, it's like, much weaker than we thought, and like, it's not as reliable as we thought, and, and then people get angry at scientists, and it's like, well, maybe we're setting like, a, a super, like, an

• 46:23Dan Shipper?

  almost impossible task, and we're like, asking the wrong question. And that's why I think the kind of end of one thing is more interesting, because it's like, yeah, sure, like, the context really matters, great, because I'm one person, like, please, like, I don't, I'm in, I'm in my context, like, solve for my context, and worry about the generalization later.

• 46:42Dan Shipper?

  And the other thing that this made me think of, I was talking about, like, papers and, and, and data sets, is, one of the nice things about shifting away from a paper as, as the thing that you publish, is that a paper, you don't publish papers that, that don't, the hypothesis doesn't bear out, but if you're just publishing the data set, as long as the data is quality, you'll publish it, and then the model is like, you unbundle the model, which is like, the kind of conclusion part from the data, which I think is kind of interesting.

• 47:13Dan Shipper?

  And then it also, if, if, if the thing that's really important is the data, like, It's really stupid that researchers have to get grants to study like undergrad, like five undergrads, when like Facebook has like all the data that you would ever need. And this is a family show, so we'll bleep that out. But like, what I think is that companies should establish data trusts, where they can, they will donate their data for qualified researchers to like, ask questions and find out the answers, like that's all sitting there. It's just, we just need to use it.

• 47:51Speaker 01

  Yeah, and I think to be fair, there's some of this that does happen, right? Like, and this is the big argument around keeping open source models, right? Like academics can use these open source models and they can build on them and they, and they can do awesome things with them. The, the more you close those down, the more we screw academic, or we mess with academics on this point.

• 48:08Speaker 01

  But I think, yeah, like I think establishing a data test makes a ton of sense. I think like scientists can ask the right questions. Pushing everything towards a general space doesn't really work. I don't think it works with AI either. Like to bring it back to this sort of modeling that we're talking about now. And I think the power is not choosing open data sets over papers, but the combination. So for me, so something that like I am thinking about a lot right now is from news articles, how do we deeply understand that information? And then how do we build things on top of that? I think with academic papers, it's similar. How do I deeply understand, like not the conclusions, maybe not the, like the methods are important. But if I were to take all of this information out of this paper,

• 48:52Speaker 01

  if I had access to the data in combination with that, that would be a really like another level of understanding. And even alternative hypothesis generation and testing I could do possibly, depending on the data set.

• 49:06Dan Shipper?

  Have I shown you extendable articles?

• 49:08Speaker 01

  Oh, I think I saw it. It came out this week.

• 49:12Dan Shipper?

  It came out last week.

• 49:12Speaker 01

  Last week. Okay. Yeah, I started poking around with it. It looks super cool.

• 49:16Dan Shipper?

  Is it cool?

• 49:17Speaker 01

  It's super cool.

• 49:18Dan Shipper?

  On your point where for people who haven't seen it, like we built this little tool where when we publish an article on every that has a lot of original interviews and research, you can basically read the article and then we also make all of the sources, all of the hours of interviews and articles and whatever that we conducted and found and read. We make that available as a little chatbot. So you can kind of like go through and form your own opinion. I guess, is that sort of what you're talking about?

• 49:43Speaker 01

  Kind of, yeah. So like if I could take, if every news article came with that, it would be incredible, right? And this is like, there is a thread through here, actually, the stuff we were building with Recollect, where we were taking all of these pieces of information, like articles you could read, whatever it was, connecting those and then generating something on top of that. We had a line back to what, where, what was the, what were the pieces that went into this, right? And now on the other side of that, I'm trying to understand news articles that are coming through for the work I'm doing at Smart News. But like, the deeper understanding of that that I get right now comes from how these articles are interconnected. And I'm, I'm creating that in the models then later. But with what you've made with this, like you're giving me,

• 50:27Speaker 01

  you know, what is connected to this or like, how did you get to write this article, right? So I feel like it comes from a couple of different angles, but the more, yeah, the more you get those pieces, the richer the understanding becomes. And I think the more interesting things we can do with that.

• 50:40Dan Shipper?

  That is interesting. To generalize,

• 50:44Dan Shipper?

  I feel like one of the patterns that we're pulling out is like,

• 50:49Dan Shipper?

  and this is something that I've written about before, but like in only more specific context, but like there's all these places in machine learning, in science, in journalism, where for whatever reason, the like, the story and the underlying data that were like used to create the story were always, always had to be bundled. And that created a lot of problems. And the solution is not to. Not to get rid of stories, the stories are like also very, very important. But it is just like, um, we are now in a place where the underlying data is actually probably just as important as the story because it's now way more discoverable and legible than

• 51:32Dan Shipper?

  it was before without the, without the authorial perspective. And perspective is still important, but like it's important to present it with this other thing and we'll, we'll make way more progress as a society if we do that.

• 51:47Speaker 01

  Yeah, I think that's absolutely right. Yep. I think if we can, like from what you're saying, which I am on board with, um, you as the story creator, you as the writer, you as the narrator, you as the scientist, right, writing this paper, these are all ways of conveying information. It's a story in and of itself. You have an interpretation of a thing. And if you provide the interpretation and the pieces that you went through that got you there, whatever those are, they could be other articles, they could be thoughts you put together, they could be conversations you had with other people. That, all of that information, then somebody could then generate their own separate story from it, um, in a much quicker way too, than if they had, you know, I gotta read the entire

• 52:29Speaker 01

  reference section of this paper and I gotta comb through all of your last paper and whatever.

• 52:34Dan Shipper?

  It's no longer necessary. We don't need to do that. And that's so good because like, how much time have you wasted trying to like, understand some like crazy paper that like is really important, but like you have to understand five background things or read it and like, that's not a thing anymore. It's crazy.

• 52:50Speaker 01

  Yeah, it is crazy and it's exciting. I'm thrilled.

• 52:55Dan Shipper?

  All right, we're, we're almost at time. Is there anything else that you wanted to talk about or anything else on your mind before we end?

• 53:01Speaker 01

  Yeah, I feel like this last conversation is something that would be fun to do more on, but I feel like we won't get the time, like this, like where, how does this intersect with the stuff you're doing every, like now I'm thinking about this media news space. Like I think there's like such a, there's not been much change in this space, which I thought would happen with AI, but I think it might be coming and I'm not sure.

• 53:24Dan Shipper?

  Yeah, let's talk about it. So, so the question, the question, just to make sure I understand is like, how does this like kind of unbundling a story and data, um, uh, that we think is interesting in science and also in media, how does that affect media? How does it affect how I'm running every, um, that, that's sort of, that's what you're asking about?

• 53:43Speaker 01

  Yeah, and like I think maybe one level up from that broader question is we've got all the stuff that's happened in the last couple of years with AI. A lot of it is text heavy, story heavy. We haven't seen a fundamental shift in like media. Well, we've seen like lots of generated articles and stuff and people have mixed feelings on those, but we haven't seen a huge shift and it must be burgeoning.

• 54:07Dan Shipper?

  Yeah, I think it's coming. And obviously like we're to some degree trying to invent it. So like I have some specific opinions, but like, I don't know if I'm right. I think I in general have an opinion on like where it will go and like a general opinion. And then like a specific, some specific bets that we're making. So like, one of those bets is this sort of extendable article thing, which I think in its current form is like, not good enough. And it's like, it's really interesting conceptually, but it's not saying that like a lot of readers are going to use all the time. And I want to get there and sort of try to make a standard. Another thing, we've created a new synthetic show from every called TLDR. And the, and TLDR is a three to five minute AI generated podcast about your company.

• 54:52Dan Shipper?

  So about meetings that you missed. And so, and it's, it's, it's done with all of the like writing and taste and like storytelling ability of the writers and producers at every, but it's about your company. And we take meetings recordings and turn that into podcasts so that if you miss a meeting, you know what's going on. And it's like the first of hopefully many shows that we'll end up doing. So TLDR is about meetings you missed. There might be like a how it's made type show that like talks about like a big product launch that you did, or like, you know, a Sunday strategy catch up where it's like, here's all the stuff that happened this week as you're like, you know, reading your morning,

• 55:32Dan Shipper?

  reading like, not reading your morning coffee, drinking your morning coffee, stuff like that. So, so that's, that's another thing, which is like, as storytelling gets cheaper, I actually don't think it replaces other kinds of storytelling. Like, we're still going to watch movies, but it means that we can tell stories in places where it would be too expensive to tell them otherwise before. So like, no one's, no NPR producer is going to want to make a show about like most internal meetings, but now that doesn't matter. Have the, every company has a story and you can have AI tell that story. And that's, I think, really cool. So that's enough, that's a, you know, place that we're kind of excited about.

• 56:15Speaker 01

  Yeah. Oh, that's so fun. And I've been following the Notebook LLM stuff too, in terms of the podcasts they're creating. I love the idea of saying, no one would bother to create this. Now we can, right? Like, no one's gonna sit in your meetings or whatever and be like, you know, uh, now you're up first. That is really, really a neat application of that. I've been thinking a lot about accessibility of stories and how do you, how do you craft a story? That is maybe distilled or shorter, but doesn't lose some of the characteristics of, you know, what you're trying to actually say. But, you know, either translate that to another language or make it audio, make it video, make it something that somebody that wouldn't actually like,

• 56:57Speaker 01

  maybe they have morning coffee, maybe they don't, but like, maybe they don't read, right? Like, they don't want to sit and read something. So I'm pretty excited about, like, I think that'll be a fundamental shift in terms of the cheapness of producing that piece.

• 57:11Dan Shipper?

  I agree. I mean, the thing, uh, like, there are all these classic books that, like, can become more readable, more interesting, more visual, more audio, like, like, uh, I've been, I've been rewriting, uh, this platonic dialogue as a narrative nonfiction. And threw it into Sora and like, it made a movie out of it. And I was like, this is amazing.

• 57:34Speaker 01

  Oh, my God. That's really cool.

• 57:37Dan Shipper?

  Yeah, there's so much culture that like is inaccessible because it's like, the only person that gets to translate it is someone who like studied that for like 50 years. Like, that's important, but it'd be nice if there was like 50 other translations that are more accessible for a particular kind of person.

• 57:53Speaker 01

  Yep. Yeah. And then like doing the work of creating a story like, um, Something we've been thinking about a lot recently is like local news, like very local things that happen. Nobody does the work. Like you don't have a ton of journalists throughout there, right, that are doing this. It's kind of suffering, but you still need this information in a way that is a story. And so like, I think this piece, it's like, it's, it's on the meaning piece, but kind of, right? Like if it's the stuff that's happening to you in your little, like wherever, whatever sphere you're encompassing here. And then, like, yeah, I, I am very excited about like underserved spaces, I guess. Like, this is the thing where I feel like we can make an actual impact. Like, this is the

• 58:35Speaker 01

  thing where we get people access to information that didn't happen before. And that's huge.

• 58:40Dan Shipper?

  It goes back to what you said earlier about, oh, it's raising the bar and lowering the, raising the searing and lowering the bar. And like, lowering the bar for storytelling, I think is really important.

• 58:48Speaker 01

  Yeah. And story consumption. Right? Yeah. So I'm, I'm excited. I think we're gonna, I think 2025 for me is, is hopefully gonna be this year where we see the application of these things. And like, your products coming out, right? I'm excited. You're incubating these things now. That's super exciting.

• 59:03Dan Shipper?

  I love that. I hope, I hope that's true. I'm excited for this year too. And I'm really glad that we got to chat. Thank you so much for coming on.

• 59:10Speaker 01

  Yeah, it was good to see you.

• 59:19Speaker 02

  Oh my gosh, folks. You absolutely, positively have to smash that like button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure, unadulterated knowledge bombs about ChatGPT. Every episode is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat, craving for more. It's not just a show, it's a journey into the future with Dan Shipper as the captain of the spaceship. So do yourself a favor, hit like, smash subscribe, and strap in for the ride of your life. And now, without any further ado, let me just say, Dan, I'm absolutely, hopelessly in love with you.