Idea Machines

Benjamin Reinhardt
Idea Machines

Idea Machines is a deep dive into the systems and people that bring innovations from glimmers in someone's eye all the way to tools, processes, and ideas that can shift paradigms. We see the outputs of innovation systems everywhere but rarely dig into how they work. Idea Machines digs below the surface into crucial but often unspoken questions to explore themes of how we enable innovations today and how we could do it better tomorrow. Idea Machines is hosted by Benjamin Reinhardt.

  1. 05/27/2024

    Speculative Technologies with Ben Reinhardt [Macroscience cross-post]

    Tim Hwang turns the tables and interviews me (Ben) about Speculative Technologies and research management.

    30 min
  2. Industrial Research with Peter van Hardenberg [Idea Machines #50]

    02/10/2024

    Industrial Research with Peter van Hardenberg [Idea Machines #50]

    Peter van Hardenberg talks about Industrialists vs. Academics, Ink&Switch's evolution over time, the Hollywood Model, internal lab infrastructure, and more! Peter is the lab director and CEO of Ink&Switch, a private, creator oriented, computing research lab.  References Ink&Switch (and their many publications) The Hollywood Model in R&D Idea Machines Episode with Adam Wiggins Paul Erdós Transcript Peter Van Hardenberg [00:01:21] Ben: Today I have the pleasure of speaking with Peter van Hardenbergh. Peter is the lab director and CEO of Inkin switch. Private creator oriented, competing research lab. I talked to Adam Wiggins, one of inkind switches founders, [00:01:35] way back in episode number four. It's amazing to see the progress they've made as an organization. They've built up an incredible community of fellow travelers and consistently released research reports that gesture at possibilities for competing that are orthogonal to the current hype cycles. Peter frequently destroys my complacency with his ability to step outside the way that research has normally done and ask, how should we be operating, given our constraints and goals. I hope you enjoy my conversation with Peter. Would you break down your distinction between academics and industrialists [00:02:08] Peter: Okay. Academics are people whose incentive structure is connected to the institutional rewards of the publishing industry, right? You, you publish papers. And you get tenure and like, it's a, it's, it's not so cynical or reductive, but like fundamentally the time cycles are long, right? Like you have to finish work according to when, you know, submission deadlines for a conference are, you know, you're [00:02:35] working on something now. You might come back to it next quarter or next year or in five years, right? Whereas when you're in industry, you're connected to users, you're connected to people at the end of the day who need to touch and hold and use the thing. And you know, you have to get money from them to keep going. And so you have a very different perspective on like time and money and space and what's possible. And the real challenge in terms of connecting these two, you know, I didn't invent the idea of pace layers, right? They, they operate at different pace layers. Academia is often intergenerational, right? Whereas industry is like, you have to make enough money every quarter. To keep the bank account from going below zero or everybody goes home, [00:03:17] Ben: Right. Did. Was it Stuart Brand who invented pace [00:03:22] Peter: believe it was Stewart Brand. Pace layers. Yeah. [00:03:25] Ben: That actually I, I'd never put these two them together, but the, the idea I, I, I think about impedance mismatches between [00:03:35] organizations a lot. And that really sort of like clicks with pace layers Exactly. Right. Where it's like [00:03:39] Peter: Yeah, absolutely. And, and I think in a big way what we're doing at, Ink& Switch on some level is trying to provide like synchro mesh between academia and industry, right? Because they, the academics are moving on a time scale and with an ambition that's hard for industry to match, right? But also, Academics. Often I think in computer science are like, have a shortage of good understanding about what the real problems people are facing in the world today are. They're not disinterested. [00:04:07] Ben: just computer [00:04:08] Peter: Those communication channels don't exist cuz they don't speak the same language, they don't use the same terminology, they don't go to the same conferences, they don't read the same publications. Right. [00:04:18] Ben: Yeah. [00:04:18] Peter: so vice versa, you know, we find things in industry that are problems and then it's like you go read the papers and talk to some scientists. I was like, oh dang. Like. We know how to solve this. It's just nobody's built it. [00:04:31] Ben: Yeah. [00:04:32] Peter: Or more accurately it would be to say [00:04:35] there's a pretty good hunch here about something that might work, and maybe we can connect the two ends of this together. [00:04:42] Ben: Yeah. Often, I, I think of it as someone, someone has, it is a quote unquote solved problem, but there are a lot of quote unquote, implementation details and those implementation details require a year of work. [00:04:56] Peter: yeah, a year or many years? Or an entire startup, or a whole career or two? Yeah. And, and speaking of, Ink&Switch, I don't know if we've ever talked about, so a switch has been around for more than half a decade, right? [00:05:14] Peter: Yeah, seven or eight years now, I think I could probably get the exact number, but yeah, about that. [00:05:19] Ben: And. I think I don't have a good idea in my head over that time. What, what has changed about in, can switches, conception of itself and like how you do things. Like what is, what are some of the biggest things that have have changed over that time?[00:05:35] [00:05:35] Peter: So I think a lot of it could be summarized as professionalization. But I, I'll give a little brief history and can switch began because the. You know, original members of the lab wanted to do a startup that was Adam James and Orion, but they recognized that they didn't, they weren't happy with computing and where computers were, and they knew that they wanted to make something that would be a tool that would help people who were solving the world's problems work better. That's kinda a vague one, but You know, they were like, well, we're not physicists, we're not social scientists. You know, we can't solve climate change or radicalization directly, or you know, the journalism crisis or whatever, but maybe we can build tools, right? We know how to make software tools. Let's build tools for the people who are solving the problems. Because right now a lot of those systems they rely on are getting like steadily worse every day. And I think they still are like the move to the cloud disempowerment of the individual, like, you [00:06:35] know, surveillance technology, distraction technology. And Tristan Harris is out there now. Like hammering on some of these points. But there's just a lot of things that are like slow and fragile and bad and not fun to work with and lose your, you know, lose your work product. You know, [00:06:51] Ben: Yeah, software as a service more generally. [00:06:54] Peter: Yeah. And like, there's definitely advantages. It's not like, you know, people are rational actors, but something was lost. And so the idea was well go do a bit of research, figure out what the shape of the company is, and then just start a company and, you know, get it all solved and move on. And I think the biggest difference, at least, you know, aside from scale and like actual knowledge is just kind of the dawning realization at some point that like there won't really be an end state to this problem. Like this isn't a thing that's transitional where you kind of come in and you do some research for a bit, and then we figure out the answer and like fold up the card table and move on to the next thing. It's like, oh no, this, this thing's gotta stick around because these problems aren't gonna [00:07:35] go away. And when we get through this round of problems, we already see what the next round are. And that's probably gonna go on for longer than any of us will be working. And so the vision now, at least from my perspective as the current lab director, is much more like, how can I get this thing to a place where it can sustain for 10 years, for 50 years, however long it takes, and you know, to become a place that. Has a culture that can sustain, you know, grow and change as new people come in. But that can sustain operations indefinitely. [00:08:07] Ben: Yeah. And, and so to circle back to the. The, the jumping off point for this, which is sort of since, since it began, what have been some of the biggest changes of how you operate? How you, or just like the, the model more generally or, or things that you were [00:08:30] Peter: Yeah, so the beginning was very informal, but, so maybe I'll skip over the first like [00:08:35] little period where it was just sort of like, Finding our footing. But around the time when I joined, we were just four or five people. And we did one project, all of us together at a time, and we just sort of like, someone would write a proposal for what we should do next, and then we would argue about like whether it was the right next thing. And, you know, eventually we would pick a thing and then we would go and do that project and we would bring in some contractors and we called it the Hollywood model. We still call it the Hollywood model. Because it was sort of structured like a movie production. We would bring in, you know, to our little core team, we'd bring in a couple specialists, you know, the equivalent of a director of photography or like a, you know, a casting director or whatever, and you bring in the people that you need to accomplish the task. Oh, we don't know how to do Bluetooth on the web. Okay. Find a Bluetooth person. Oh, there's a bunch of crypto stuff, cryptography stuff. Just be clear on this upcoming project, we better find somebody who knows, you know, the ins and outs of like, which cryptography algorithms to use or [00:09:35] what, how to build stuff in C Sharp for Windows platform or Surface, whatever the, the project was over time. You know, we got pretty good at that and I think one of the biggest changes, sort of after we kind of figured out how to actually do work was the realization that. Writing about the work not only gave us a lot of leverage in terms of our sort of visibility in the community and our ability to attract talent, but also the more we put into the writing, the more we learned about the research and that the process of, you know, we would do something and then write a little internal report and then move on. But the process

    47 min
  3. MACROSCIENCE with Tim Hwang [Idea Machines #49]

    11/27/2023

    MACROSCIENCE with Tim Hwang [Idea Machines #49]

    A conversation with Tim Hwang about historical simulations, the interaction of policy and science, analogies between research ecosystems and the economy, and so much more.  Topics Historical Simulations Macroscience Macro-metrics for science Long science The interaction between science and policy Creative destruction in research “Regulation” for scientific markets Indicators for the health of a field or science as a whole “Metabolism of Science” Science rotation programs Clock speeds of Regulation vs Clock Speeds of Technology References Macroscience Substack Ada Palmer’s Papal Simulation Think Tank Tycoon Universal Paperclips (Paperclip maximizer html game) Pitt Rivers Museum   Transcript [00:02:02] Ben: Wait, so tell me more about the historical LARP that you're doing. Oh, [00:02:07] Tim: yeah. So this comes from like something I've been thinking about for a really long time, which is You know in high school, I did model UN and model Congress, and you know, I really I actually, this is still on my to do list is to like look into the back history of like what it was in American history, where we're like, this is going to become an extracurricular, we're going to model the UN, like it has all the vibe of like, after World War II, the UN is a new thing, we got to teach kids about international institutions. Anyways, like, it started as a joke where I was telling my [00:02:35] friend, like, we should have, like, model administrative agency. You know, you should, like, kids should do, like, model EPA. Like, we're gonna do a rulemaking. Kids need to submit. And, like, you know, there'll be Chevron deference and you can challenge the rule. And, like, to do that whole thing. Anyways, it kind of led me down this idea that, like, our, our notion of simulation, particularly for institutions, is, like, Interestingly narrow, right? And particularly when it comes to historical simulation, where like, well we have civil war reenactors, they're kind of like a weird dying breed, but they're there, right? But we don't have like other types of historical reenactments, but like, it might be really valuable and interesting to create communities around that. And so like I was saying before we started recording, is I really want to do one that's a simulation of the Cuban Missile Crisis. But like a serious, like you would like a historical reenactment, right? Yeah. Yeah. It's like everybody would really know their characters. You know, if you're McNamara, you really know what your motivations are and your background. And literally a dream would be a weekend simulation where you have three teams. One would be the Kennedy administration. The other would be, you know, Khrushchev [00:03:35] and the Presidium. And the final one would be the, the Cuban government. Yeah. And to really just blow by blow, simulate that entire thing. You know, the players would attempt to not blow up the world, would be the idea. [00:03:46] Ben: I guess that's actually the thing to poke, in contrast to Civil War reenactment. Sure, like you know how [00:03:51] Tim: that's gonna end. Right, [00:03:52] Ben: and it, I think it, that's the difference maybe between, in my head, a simulation and a reenactment, where I could imagine a simulation going [00:04:01] Tim: differently. Sure, right. [00:04:03] Ben: Right, and, and maybe like, is the goal to make sure the same thing happened that did happen, or is the goal to like, act? faithfully to [00:04:14] Tim: the character as possible. Yeah, I think that's right, and I think both are interesting and valuable, right? But I think one of the things I'm really interested in is, you know, I want to simulate all the characters, but like, I think one of the most interesting things reading, like, the historical record is just, like, operating under deep uncertainty about what's even going on, right? Like, for a period of time, the American [00:04:35] government is not even sure what's going on in Cuba, and, like, you know, this whole question of, like, well, do we preemptively bomb Cuba? Do we, we don't even know if the, like, the warheads on the island are active. And I think I would want to create, like, similar uncertainty, because I think that's where, like, that's where the strategic vision comes in, right? That, like, you have the full pressure of, like, Maybe there's bombs on the island. Maybe there's not even bombs on the island, right? And kind of like creating that dynamic. And so I think simulation is where there's a lot, but I think Even reenactment for some of these things is sort of interesting. Like, that we talk a lot about, like, oh, the Cuban Missile Crisis. Or like, the other joke I had was like, we should do the Manhattan Project, but the Manhattan Project as, like, historical reenactment, right? And it's kind of like, you know, we have these, like, very, like off the cuff or kind of, like, stereotype visions of how these historical events occur. And they're very stylized. Yeah, exactly, right. And so the benefit of a reenactment that is really in detail Yeah. is like, oh yeah, there's this one weird moment. You know, like that, that ends up being really revealing historical examples. And so even if [00:05:35] you can't change the outcome, I think there's also a lot of value in just doing the exercise. Yeah. Yeah. The, the thought of [00:05:40] Ben: in order to drive towards this outcome that I know. Actually happened I wouldn't as the character have needed to do X. That's right That's like weird nuanced unintuitive thing, [00:05:50] Tim: right? Right and there's something I think about even building into the game Right, which is at the very beginning the Russians team can make the decision on whether or not they've even actually deployed weapons into the cube at all, yeah, right and so like I love that kind of outcome right which is basically like And I think that's great because like, a lot of this happens on the background of like, we know the history. Yeah. Right? And so I think like, having the team, the US team put under some pressure of uncertainty. Yeah. About like, oh yeah, they could have made the decision at the very beginning of this game that this is all a bluff. Doesn't mean anything. Like it's potentially really interesting and powerful, so. [00:06:22] Ben: One precedent I know for this completely different historical era, but there's a historian, Ada Palmer, who runs [00:06:30] Tim: a simulation of a people election in her class every year. That's so good. [00:06:35] And [00:06:36] Ben: it's, there, you know, like, it is not a simulation. [00:06:40] Tim: Or, [00:06:41] Ben: sorry, excuse me, it is not a reenactment. In the sense that the outcome is indeterminate. [00:06:47] Tim: Like, the students [00:06:48] Ben: can determine the outcome. But... What tends to happen is like structural factors emerge in the sense that there's always a war. Huh. The question is who's on which sides of the war? Right, right. And what do the outcomes of the war actually entail? That's right. Who [00:07:05] Tim: dies? Yeah, yeah. And I [00:07:07] Ben: find that that's it's sort of Gets at the heart of the, the great [00:07:12] Tim: man theory versus the structural forces theory. That's right. Yeah. Like how much can these like structural forces actually be changed? Yeah. And I think that's one of the most interesting parts of the design that I'm thinking about right now is kind of like, what are the things that you want to randomize to impose different types of like structural factors that could have been in that event? Right? Yeah. So like one of the really big parts of the debate at XCOM in the [00:07:35] early phases of the Cuban Missile Crisis is You know, McNamara, who's like, right, he runs the Department of Defense at the time. His point is basically like, look, whether or not you have bombs in Cuba or you have bombs like in Russia, the situation has not changed from a military standpoint. Like you can fire an ICBM. It has exactly the same implications for the U. S. And so his, his basically his argument in the opening phases of the Cuban Missile Crisis is. Yeah. Which is actually pretty interesting, right? Because that's true. But like, Kennedy can't just go to the American people and say, well, we've already had missiles pointed at us. Some more missiles off, you know, the coast of Florida is not going to make a difference. Yeah. And so like that deep politics, and particularly the politics of the Kennedy administration being seen as like weak on communism. Yeah. Is like a huge pressure on all the activity that's going on. And so it's almost kind of interesting thinking about the Cuban Missile Crisis, not as like You know us about to blow up the world because of a truly strategic situation but more because of like the local politics make it so difficult to create like You know situations where both sides can back down [00:08:35] successfully. Basically. Yeah [00:08:36] Ben: The the one other thing that my mind goes to actually to your point about it model UN in schools. Huh, right is Okay, what if? You use this as a pilot, and then you get people to do these [00:08:49] Tim: simulations at [00:08:50] Ben: scale. Huh. And that's actually how we start doing historical counterfactuals. Huh. Where you look at, okay, you know, a thousand schools all did a simulation of the Cuban Missile Crisis. In those, you know, 700 of them blew [00:09:05] Tim: up the world. Right, right. [00:09:07] Ben: And it's, it actually, I think it's, That's the closest [00:09:10] Tim: thing you can get to like running the tape again. Yeah. I think that's right. And yeah, so I think it's, I think it's a really underused medium in a lot of ways. And I think particularly as like you know, we just talk, talk like pedagogically, like it's interesting that like, it seems to me that there was a moment in American pedagogical history where like, this is a good way of t

    57 min
  4. Idea Machines with Nadia Asparouhova

    10/03/2022

    Idea Machines with Nadia Asparouhova

    Nadia Asparouhova talks about idea machines on idea machines! Idea machines, of course, being her framework around societal organisms that turn ideas into outcomes. We also talk about  the relationship between philanthropy and status, public goods and more.  Nadia is a hard-to-categorize doer of many things: In the past, she spent many years exploring the funding, governance, and social dynamics of open source software, both writing a book about it called “Working in Public” and putting those ideas into practice at GitHub, where she worked to improve the developer experience. She explored parasocial communities and reputation-based economies as an independent researcher at Protocol Labs and put those ideas into practice as employee number two at Substack, focusing on the writer experience. She’s currently researching what the new tech elite will look like, which forms the base of a lot of our conversation.  Completely independently, the two of us came up with the term “idea machines” to describe same thing — in her words: “self-sustaining organisms that contains all the parts needed to turn ideas into outcomes.” I hope you enjoy my conversation with Nadia Asparouhova.  Links Nadia's Idea Machines Piece Nadia's Website Working in Public: The Making and Maintenance of Open Source Software Transcript [00:01:59] Ben: I really like your way of, of defining things and sort of bringing clarity to a lot of these very fuzzy words that get thrown around. So, so I'd love to sort of just get your take on how we should think about so a few definitions to start off with. So I, in your mind, what, what is tech, when we talk about like tech and philanthropy what, what is that, what is that entity. [00:02:23] Nadia: Yeah, tech is definitely a fuzzy term. I think it's best to find as a culture, more than a business industry. And I think, yeah, I mean, tech has been [00:02:35] associated with startups historically, but But like, I think it's transitioning from being this like pure software industry to being more like, more like a, a way of thinking. But personally, I don't think I've come across a good definition for tech anywhere. It's kind, you know? [00:02:52] Ben: Yeah. Do, do you think you could point to some like very sort of like characteristic mindsets of tech that you think really sort of set it. [00:03:06] Nadia: Yeah. I think the probably best known would be, you know, failing fast and moving fast and breaking things. I think like the interest in the sort of like David and gly model of an individual that is going up against an institution or some sort of. Complex bureaucracy that needs to be broken apart. Like the notion of disrupting, I think, is a very tech sort of mindset of looking at a problem and saying like, how can we do this better? So it, in a [00:03:35] weird way, tech is, I feel like it's sort of like, especially in relation, in contrast to crypto, I feel like it's often about iterating upon the way things are or improving things, even though I don't know that tech would like to be defined that way necessarily, but when I, yeah. Sort of compare it to like the crypto mindset, I feel like tech is kind of more about breaking apart institutions or, or doing yeah. Trying to do things better. [00:04:00] Ben: A a as opposed. So, so could you then dig into the, the crypto mindset by, by contrast? That's a, I think that's a, a subtle difference that a lot of people don't go into. [00:04:10] Nadia: Yeah. Like I think the crypto mindset is a little bit more about building a parallel universe entirely. It's about, I mean, well, one, I don't see the same drive towards creating monopolies in the way that and I don't know if that was like always a, you know, core value of tech, but I think in practice, that's kind of what it's been of. You try to be like the one thing that is like dominating a market. Whereas with crypto, I think people are [00:04:35] because they have sort of like decentralization as a core value, at least at this stage of their maturity. It's more about building lots of different experiments or trying lots of different things and enabling people to sort of like have their own little corner of the universe where they can, they have all the tools that they need to sort of like build their own world. Whereas the tech mindset seems to imply that there is only one world the world is sort of like dominated by these legacy institutions and it's Tech's job to fix. Those problems. So it's like very much engaged with what it sees as kind of like that, that legacy world or [00:05:10] Ben: Yeah, I, I hadn't really thought about it that way. But that, that totally makes sense. And I'm sure other people have, have talked about this, but do, do you feel that is an artifact of sort of the nature of the, the technology that they're predicated on? Like the difference between, I guess sort of. The internet and the, the internet of, of like SAS and servers and then the [00:05:35] internet of like blockchains and distributed things. [00:05:38] Nadia: I mean, it's weird. Cause if you think about sort of like early computing days, I don't really get that feeling at all. I'm not a computer historian or a technology historian, so I'm sure someone else has a much more nuanced answer to this than I do, but yeah. I mean, like when I think of like sixties, computer or whatever, it, it feels really intertwined with like creating new worlds. And that's why like, I mean, because crypto is so new, it's maybe. It, we can only really observe what's happening right now. I don't know that crypto will always look exactly like this in the future. In fact, it almost certainly will not. So it's hard to know like, what are, it's like core distinct values, but I, I just sort of noticed the contrast right now, at least, but probably, yeah, if you picked a different point in, in text history, sort of like pre startups, I guess and, and pre, or like that commercialization phase or that wealth accumulation phase it was also much more, I guess, like pie this guy. Right. But yeah, it feel, it feels like at least the startup mindset, or like whenever that point of [00:06:35] history started all this sort of like big successes were really about like overturning legacy industries, the, yeah. The term disruption was like such a buzzword. It's about, yeah. Taking something that's not working and making it better, which I think is like very intertwined with like programmer mindset. [00:06:51] Ben: It's yeah, it's true. And I'm just thinking about sort of like my impression of, of the early internet and it, and it did not have that same flavor. So, so perhaps it's a. Artifact of like the stage of a culture or ecosystem then like the technology underlying it. I guess [00:07:10] Nadia: And it's strange. Cause I, I feel like, I mean, there are people today who still sort of maybe fetishizes too strong, a word, but just like embracing that sort of early computing mindset. But it almost feels like a subculture now or something. It doesn't feel. yeah. I don't know. I don't, I don't find that that's like sort of the prevalent mindset in, in tech. [00:07:33] Ben: Well, it, it feels like the, the sort of [00:07:35] like mechanisms that drive tech really do sort of center. I mean, this is my bias, but like, I feel like the, the way that that tech is funded is primarily through venture capital, which only works if you're shooting for a truly massive Result and the way that you get a truly massive result is not to build like a little niche thing, but to try to take over an industry. [00:08:03] Nadia: It's about arbitrage [00:08:05] Ben: yeah. Or, or like, or even not even quite arbitrage, but just like the, the, to like, that's, that's where the massive amount of money is. And, and like, [00:08:14] Nadia: This means her like financially. I feel like when I think about the way that venture capital works, it's it's. [00:08:19] Ben: yeah, [00:08:20] Nadia: ex sort of exploiting, I guess, the, the low margin like cost models. [00:08:25] Ben: yeah, yeah, definitely. And like then using that to like, take over an industry, whereas if maybe like, you're, you're not being funded in a way [00:08:35] that demands, that sort of returns you don't need to take as, as much of a, like take over the world mindset. [00:08:41] Nadia: Yeah. Although I don't think like those two things have to be at odds with each other. I think it's just like, you know, there's like the R and D phase that is much more academic in nature and much more exploratory and then venture capital is better suited for the point in which some of those ideas can be commercialized or have a commercial opportunity. But I don't think, yeah, I don't, I don't think they're like fighting with each other either. [00:09:07] Ben: Really? I, I guess I, I don't know. It's like, so can I, can I, can I disagree and, and sort of say, like, it feels like the, the, the stance that venture type funding comes with, like forces on people is a stance of like, we are, we might fail, but we're, we're setting out to capture a huge, huge amount of value and like, [00:09:35] And, and, and just like in order for venture portfolios to work, that needs to be the mindset. And like there, there are other, I mean, there are just like other funding, ways of funding, things that sort of like ask for more modest returns. And they can't, I mean, they can't take as many risks. They come with other constraints, but, but like the, the need for those, those power law returns does drive a, the need to be like very ambitious in terms of scale. [00:10:10] Nadia: I guess, like what's an example of something that has modest financial returns, but massive social impact that can't be funded through philanthropy and academia or through through venture capital [00:10:29] Ben: Well, I mean, like are, I mean, like, I think that there's, [00:10:35] I think that, that, that, [00:10:38] Nadia: or I guess it

    56 min

  5. 09/01/2022

    Institutional Experiments with Seemay Chou

    Seemay Chou talks about the process of building a new research organization, ticks, hiring and managing entrepreneurial scientists, non-model organisms, institutional experiments and a lot more! Seemay is the co-founder and CEO of Arcadia Science —  a research and development company focusing on underesearched areas in biology and specifically new organisms that haven't been traditionally studied in the lab.  She’s also the co-founder of Trove Biolabs — a startup focused on harnessing molecules in tick saliva for skin therapies and was previously an assistant professor at UCSF.  She has thought deeply not just about scientific problems themselves, but the meta questions of how we can build better processes and institutions for discovery and invention. I hope you enjoy my conversation with Seemay Chou   Links Seemay on Twitter (@seemaychou) Arcadia's Research Trove Biolabs Seemay's essay about building Arcadia  Transcript [00:02:02] Ben: So since a lot of our conversation is going to be about it how do you describe Arcadia to a smart well-read person who has never actually heard of it before? [00:02:12] Seemay: Okay. I, I actually don't have a singular answer to this smart and educated in what realm. [00:02:19] Ben: oh, good question. Let's assume they have taken some undergraduate science classes, but perhaps are not deeply enmeshed in, in academia. So, so like, [00:02:31] Seemay: enmeshed in the meta science community.[00:02:35]  [00:02:35] Ben: No, no, no, no, but they've, they, they, they, they they're aware that it's a thing, but [00:02:40] Seemay: Yeah. Okay. So for that person, I would say we're a research and development company that is interested in thinking about how we explore under researched areas in biology, new organisms that haven't been traditionally studied in the lab. And we're thinking from first principal polls about all the different ways we can structure the organization around this to also yield outcomes around innovation and commercialization. [00:03:07] Ben: Nice. And how would you describe it to someone who is enmeshed in the, the meta science community? [00:03:13] Seemay: In the meta science community, I would, I would say Arcadias are meta science experiment on how we enable more science in the realm of discovery, exploration and innovation. And it's, you know, that that's where I would start. And then there's so much more that we could click into on that. Right. [00:03:31] Ben: And we will, we will absolutely do that. But before we get there I'm actually really [00:03:35] interested in, in Arcadia's backstory. Cuz cuz when we met, I feel like you were already , well down the, the path of spinning it up. So what's, there's, there's always a good story there. What made you wanna go do this crazy thing? [00:03:47] Seemay: So, so the backstory of Arcadia is actually trove. Soro was my first startup that I spun out together with my co-founder of Kira post. started from a point of frustration around a set of scientific questions that I found challenging to answer in my own lab in academia. So we were very interested in my lab in thinking about all the different molecules and tick saliva that manipulate the skin barrier when a tick is feeding, but basically the, the ideal form of a team around this was, you know, like a very collaborative, highly skilled team that was, you know, strike team for like biochemical, fractionation, math spec, developing itch assays to get this done. It was [00:04:35] not a PhD style project of like one person sort of open-endedly exploring a question. So I was struggling to figure out how to get funding for this, but that wasn't even the right question because even with the right money, like it's still very challenging to set up the right team for this in academia. And so it was during this frustration that I started exploring with Kira about like, what is even the right way to solve this problem, because it's not gonna be through writing more grants. There's a much bigger problem here. Right? And so we started actually talking to people outside of academia. Like here's what we're trying to achieve. And actually the outcome we're really excited about is whether it could yield information that could be acted on for an actually commercializable product, right. There's like skin diseases galore that this could potentially be helpful for. So I think that transition was really important because it went from sort of like a passive idea to, oh, wait, how do we act as agents to figure out how to set this up correctly? [00:05:35] We started talking to angel investors, VCs people in industry. And that's how we learned that, you know, like itch is a huge area. That's an unmet need. And we had tools at our disposal to potentially explore that. So that's how tr started. And that I think was. The beginning of the end or the, the start of the beginning. However you wanna think about it. Because what it did, was it the process of starting trove? It was so fun and it was not at all in conflict with the way I was thinking about my science, the science that was happening on the team was extremely rigorous. And I experienced like a different structure. And that was like the light bulb in my head that not all science should be structured the same way. It really depends on what you're trying to achieve. And then I went down this rabbit hole of trying to study the history of what you might call meta science. Like what are the different structures and iterations of this that have happened over, over the history of even the United States. And it's, hasn't always been the same. Right? And then I think [00:06:35] like, as a scientist, like once you grapple with that, that the way things are now is not how they always have been. Suddenly you have an experiment in front of you. And so that is how Arcadia became born, because I realize. Couched within this trove experiment is so many things that I've been frustrated about that I, I, I don't feel like I've been maximized as the type of scientist that I am. And I really want to think in my career now about not how I fit into the current infrastructure, but like what other infrastructures are available to us. Right? [00:07:08] Ben: Nice. [00:07:09] Seemay: Yeah. So that, that was the beginning. [00:07:11] Ben: and, and so you, you then, I, I, I'm just gonna extrapolate one more, more step. And so you sort of like looked at the, the real, the type of work that you really wanted to do and determined that, that the, the structure of Arcadia that you've built is, is like perhaps the right way to go about enabling that. [00:07:30] Seemay: Okay. So a couple things I, I don't even know yet if Arcadia is the right way to do it. So I [00:07:35] feel like it's important for me to start this conversation there that I actually don't know. But also, yeah, it's a hypothesis and I would also say that, like, that is a beautiful summary, but it's still, it was still a little clunkier than the way you described it and the way I described it. So there's this gap there then of like, okay, what is the optimal place for me to do my science? How do we experiment with this? And I was still acting in a pretty passive way. You know, I was around people in the bay area thinking about like new orgs. And I had heard about this from like ju and Patrick Collison and others, like people very interested in funding and experimenting with new structures. So I thought, oh, if I could find someone else to create an organization. That I could maybe like help advise them on and be a part of, and, and so I started writing up this proposal that I was trying to actually pitch to other people like, oh, would you be interested in leading something like this? [00:08:35] Like, and the more that went on and I, I had like lots and lots and lots of conversations with other scientists in academia, trying to find who would lead this, that it took probably about six months for me to realize like, oh, in the process of doing this, I'm actually leading this. I think and like trying to find someone to hand the keys over to when actually, like, I seem to be the most invested so far. And so I wrote up this whole proposal trying to find someone to lead it and. It came down to that like, oh, I've already done this legwork. Like maybe I should consider myself leading it. And I've, I've definitely asked myself a bunch of times, like, was that like some weird internalized sexism on my part? Cause I was like looking for like someone, some other dude or something to like actually be in charge here. So that's actually how it started. And, and I think a couple people started suggesting to this to me, like if you feel so strongly about this, why aren't you doing this? And I know [00:09:35] it's always an important question for a founder to ask themselves. [00:09:38] Ben: Yeah, yeah, no, that's, that's really clutch. I appreciate you sort of going into the, the, the, the, the, the, like, not straight paths of it. Because, because I guess when we, we put these things into stories, we always like to, to make it like nice and, and linear and like, okay, then this happened and this happened, and here we are. But in reality, it was it's, it's always that ambiguity. Can, can I actually ask two, two questions based on, on that story? One is you, you mentioned that. In academia, even if you had the money, you wouldn't be able to put together that strike team that you thought was necessary. Like why can, can you, can you unpack that a little bit? [00:10:22] Seemay: Yeah. I mean, I think there's a lot of reasons why one of the important reasons, which is absolutely not a criticism of academia, in fact, it's maybe like my support of the [00:10:35] mission in academia is around training and education. That like part of our job as PIs and the research projects we set up is to provide an opportunity for a scientist to learn how to ask questions. How to answer those

    1h 14m
  6. DARPA and Advanced Manufacturing with William Bonvillian

    08/02/2022

    DARPA and Advanced Manufacturing with William Bonvillian

    William Bonvillian does a deep dive about his decades of research on how DARPA works and his more recent work on advanced manufacturing.  William is a Lecturer at MIT and the Senior Director of Special Projects,at MIT’s Office of Digital Learning. Before joining MIT he spent almost two decades as a senior policy advisor for the US senate. He’s also published many papers and a detailed book exploring the DARPA model.  Links William's Website The DARPA Model for Transformative Technologies Transcript [00:00:35] In this podcast, William Bonvillian, and I do a deep dive about his decades of research about how DARPA works and his more recent work on advanced manufacturing. Well humans, a lecturer at MIT and a senior director of special projects at MIT is office of digital learning. Before joining MIT. He spent almost two decades as a senior policy advisor for the us Senate. He's published many papers and a detailed book exploring the DARPA model. I've wanted [00:01:35] to compare notes with him for years. And it was a pleasure. And an honor to finally catch up with him. Here's my conversation with William [00:01:42] Ben: The place that I I'd love to start off is how did you get interested in, in DARPA and the DARPA model in the first place you've been writing about it for more than a decade now. And, and you're probably one of the, the foremost people who who've explored it. So how'd you get there in the first. [00:01:58] William: You know, I, I I worked for the us Senate as a advisor in the Senate for for about 15 years before coming to MIT then. And I I worked for a us Senator who is on the on the armed services committee. And so I began doing a substantial amount of that staffing, given my interest in science technology, R and D and you know, got early contact with DARPA with some of DARPA's both program managers and the DARPA directors, and kind of got to know the agency that way spent some time with them over in their [00:02:35] offices. You know, really kind of got to know the program and began to realize what a, what a dynamic force it was. And, you know, we're talking 20, 20 plus years ago when frankly DARPA was a lot less known than it is now. So yeah, just like you know, kind of suddenly finding this, this Jewelbox varied in the. It was it was a real discovery for me and I became very, very interested in the, kind of the model they had, which was so different than the other federal R and D agencies. [00:03:05] Ben: Yeah. And, and actually um, It sort of in your mind, what is the for, for people who I, I think tend to see different federal agencies that give money to researchers as, as all being in the same bucket. What, what do you, what would you describe the difference between DARPA and the NSF as being [00:03:24] William: well? I mean, there's a big difference. So the NSF model is to support basic research. And they have, you know, the equivalent of project [00:03:35] managers there and they, they don't do the selecting of the research projects. Instead they queue up applicants for funds and then they supervise a peer review process. Of experts, you know, largely from academia who evaluate, you know, a host of proposals in a, in a given R and D area mm-hmm and and make valuations as to which ones would qualify. What are the kind of best most competitive applicants for NSFs basic research. So DARPA's got a different project going on, so it doesn't work from the bottom up. It, it has strong program managers who are in effect kind of empowered to go out and create new things. So they're not just, you know, responding to. Grant applications for basic research, they come into DARPA and develop a [00:04:35] vision of a new breakthrough technology area. They wanna stand up. And so it's, and there's no peer review here. It's really, you hire talented program managers. And you unleash them, you turn them loose, you empower them to go out and find the best work that's going on in the country. And that's, that can be from, from universities and often ends in this breakthrough technology area they've identified. But it also could be from comp companies, often smaller companies and typically they'll construct kind of a hybrid model where they've got academics. Companies working on a project, the companies are already always oriented to getting the technology out the door. Right. Cause they have to survive, but the researchers are often in touch with some of the more breakthrough capabilities behind the research. So bringing those two together is something that the program manager at DARPA does. So while at [00:05:35] NSF, the program manager equivalent, you know, their big job is getting grant out the door and supervising a complex selection process by committee mm-hmm . The role of the, of the ARPA of the, of the DARPA program manager is selecting the award winners is just the beginning of the job. Then in effect you move into their home, right? You work with them on an ongoing basis. DARPA program managers are spending at least one third of their time on the road, linking up with their, you know, with their grantees, the folks they've contracted with sort of helping them along in the process. And then, you know, the typically fund a group of research awards in an area they'll also work on putting together kind of a thinking community amongst those award winners. Contract winners so that they begin to share their best ideas. And that's not a, that's not easy, right? Yeah. Yeah. If you're an academic [00:06:35] or you, a company, you stuff, you trading ideas is a complicated process, but that's one of the tasks. That the DARPA program manager has, is to really build these thinking communities around problems. And that's what they that's what they're driven to do. So it's a very, very different situation. This is, this is the different world here that Dar is created [00:07:01] Ben: and, and sort of actually to, to, to click on The, the how DARPA program managers interact with ideas. Do you have a sense of how they incentivize that idea sharing? Is it just the, the concept that if you share these ideas, they might get funded in a way that they wouldn't or like what, how do they sort of construct that That trust that people for people could actually be sharing those ideas. [00:07:28] William: Yeah. In, in some ways then it starts out at an all stage. So before, you know, a new [00:07:35] program manager arrives at DARPA and often they'll have, I mean, this could be ape. It could be I RPA, which worked slightly different ways, but similar kind of approach RPE is our energy DARPA. I, APA is our intelligence Dar. Right. And then soon we'll have a help DARPA, which has now been funded. Yeah. I wanna [00:07:55] Ben: your opinion on that later. [00:07:57] William: Okay. Well, we're working away on this model here. You know, you hire a program manager and you hire somebody. Who's gonna be, you know, talent and dynamic and kind of entrepreneurial and standing up a new program. They get the DARPA and they begin to work on this new technology area. And a requirement of DARPA is that really be a breakthrough. They don't wanna fund incremental work that somebody else may be doing. They wanna find a new, new territory. That's their job, revolutionary breakthroughs. To get there. They'll often convene workshops, 1, 2, 3 workshops with some of the best thinkers around the country, including people, [00:08:35] people who may be applying for the funding, but they'll, they'll look for the best people bringing together and get, you know, a day long process going um, often in several different locations to kind of think through. Technology advance opportunity. How, how it might shape up what might contribute, how might you organize it? What research might go into it, what research areas and that kind of begins the kind of thinking process of building a community around a problem. And then they'll make grant awards. And then similarly, they're gonna be frequently convening this group and everybody can sit on their hands and keep their mouth shut. But you know, that's not often the way technologists work. They'll get into a problem and start wanting to share ideas and brainstorm. And that's, that's typically what then takes place and part of the job of the, of. Partner manager DARPA is to really encourage that kind of dialogue and get a lot of ideas on the table and really promote it. Yeah. [00:09:34] Ben: [00:09:35] And, and then also with, with those ideas do, do you have, like, in your, your having looked at this so much, do you have a sense of how much there there's this tension? You know, it's like people generally do the best research when they feel a lot of ownership over their own ideas and they feel like they're, they're really working on. The, the thing that they want to work on. But then at the same time to sort of for, for, for the, a project to play into a broader program, you often need to sort of adjust ideas towards sort of a, a bigger system or a bigger goal. Do you have, do you have an idea of how much Program managers sort of shape what people are working on versus just sort of enabling people to work on things that they would want to work on. Otherwise. [00:10:24] William: Yeah. The program manager in communication with DARPA's office directors and director. Right, right. So it's a very flat organization. You know, and [00:10:35] there'll be an office director and a number of program managers working with that office director. For example in the field of, of biological technologies, a fairly new DARPA office set up about a decade ago. Yeah. You know, there'll be a group of DARPA program managers with expertise in that field and they will often have often a combination of experiences. They'll have some company experience as well as some academic research experience that they're kind of walking on both sides. They'll come into DARPA often with some ideas about things they

    48 min
  7. Philanthropically Funding the Foundation of Fields with Adam Falk [Idea Machines #45]

    07/02/2022

    Philanthropically Funding the Foundation of Fields with Adam Falk [Idea Machines #45]

    In this conversation, Adam Falk and I talk about running research programs with impact over long timescales, creating new fields, philanthropic science funding, and so much more.  Adam is the president of the Alfred P. Sloan Foundation,  which was started by the eponymous founder of General Motors and has been funding science and education efforts for almost nine decades.  They’ve funded everything from iPython Notebooks to the Wikimedia foundation to an astronomical survey of the entire sky. If you’re like me, their name is familiar from the acknowledgement part of PBS science shows. Before becoming the president of the Sloan Foundation, Adam was the president of Williams College and a high energy physicist focused on elementary particle physics and quantum field theory. His combined experience in research, academic administration, and philanthropic funding give him a unique and fascinating perspective on the innovation ecosystem. I hope you enjoy this as much as I did.  Links - The Sloan Foundation - Adam Falk on Wikipedia  - Philanthropy and the Future of Science and Technology Highlight Timestamps - How do you measure success in science? [00:01:31] - Thinking about programs on long timescales [00:05:27] -  How does the Sloan Foundation decide which programs to do? [00:08:08] - Sloan's Matter to Life Program [00:12:54] -  How does the Sloan Foundation think about coordination? [00:18:24] -  Finding and incentivizing program directors [00:22:32] - What should academics know about the funding world and what should the funding world know about academics? [00:28:03] - Grants and academics as the primary way research happens [00:33:42] - Problems with grants and common grant applications [00:44:49] - Addressing the criticism of philanthropy being inefficient because it lacks market mechanisms [00:47:16] - Engaging with the idea that people who create value should be able to capture that value [00:53:05]   Transcript [00:00:35] In this conversation, Adam Falk, and I talk about running research programs with impact over long timescales, creating new fields, philanthropic science funding, and so much more. Adam is the president of the Alfred P Sloan foundation, which was started by the eponymous founder of general motors. And has been funding science and education efforts for almost nine decades. They funded everything from IP. I fond [00:01:35] notebooks to Wikimedia foundation. To an astronomical survey of the entire sky. If you're like me, their name is familiar from the acknowledgement part of PBS science shows. Before becoming the president of the Sloan foundation. Adam was the president of Williams college and I high energy physicist focused on elementary particle physics in quantum field theory. His combined experience in research. Uh, Academic administration and philanthropic funding give him a unique and fascinating perspective on the innovation ecosystem i hope you enjoy this as much as i did [00:02:06] Ben: Let's start with like a, sort of a really tricky thing that I'm, I'm myself always thinking about is that, you know, it's really hard to like measure success in science, right? Like you, you know, this better than anybody. And so just like at, at the foundation, how do you, how do you think about success? Like, what is, what does success look like? What is the difference between. Success and failure mean to [00:02:34] Adam: you? [00:02:35] I mean, I think that's a, that's a really good question. And I think it's a mistake to think that there are some magic metrics that if only you are clever enough to come up with build them out of citations and publications you could get some fine tune measure of success. I mean, obviously if we fund in a scientific area, we're funding investigators who we think are going to have a real impact with their work individually, and then collectively. And so of course, you know, if they're not publishing, it's a failure. We expect them to publish. We expect people to publish in high-impact journals, but we look for broader measures as well if we fund a new area. So for example, A number of years ago, we had a program in the microbiology of the built environment, kind of studying all the microbes that live in inside, which turns out to be a very different ecosystem than outside. When we started in that program, there were a few investigators interested in this question. There weren't a lot of tools that were good for studying it. [00:03:35] By 10 years later, when we'd left, there was a journal, there were conferences, there was a community of people who were doing this work, and that was another measure, really tangible measure of success that we kind of entered a field that, that needed some support in order to get going. And by the time we got out, it was, it was going strong and the community of people doing that work had an identity and funding paths and a real future. Yeah. [00:04:01] Ben: So I guess one way that I've been thinking about it, it's just, it's almost like counterfactual impact. Right. Whereas like if you hadn't gone in, then it, the, it wouldn't be [00:04:12] Adam: there. Yeah. I think that's the way we think about it. Of course that's a hard to, to measure. Yeah. But I think that Since a lot of the work we fund is not close to technology, right. We don't have available to ourselves, you know, did we spin out products? Did we spin out? Companies did a lot of the things that might directly connect that work to, [00:04:35] to activities that are outside of the research enterprise, that in other fields you can measure impact with. So the impact is pretty internal. That is for the most part, it is, you know, Has it been impact on other parts of science that, you know, again, that we think might not have happened if we hadn't hadn't funded what we funded. As I said before, have communities grown up another interesting measure of impact from our project that we funded for about 25 years now, the Sloan digital sky survey is in papers published in the following sense that one of the innovations, when the Sloan digital sky survey launched in the early. Was that the data that came out of it, which was all for the first time, digital was shared broadly with the community. That is, this was a survey of the night sky that looked at millions of objects. So they're very large databases. And the investigators who built this, the, the built the, the, the telescope certainly had first crack at analyzing that [00:05:35] data. But there was so much richness in the data that the decision was made at. Sloan's urging early on that this data after a year should be made public 90% of the publications that came out of the Sloan digital sky survey have not come from collaborators, but it come from people who use that data after it's been publicly released. Yeah. So that's another way of kind of seeing impact and success of a project. And it's reached beyond its own borders. [00:06:02] Ben: And you mentioned like both. Just like that timescale, right? Like that, that, that 25 years something that I think is just really cool about the Sloan foundation is like how, how long you've been around and sort of like your capability of thinking on those on like a quarter century timescale. And I guess, how do you, how do you think about timescales on things? Right. Because it's like, on the one hand, this is like, obviously like science can take [00:06:35] 25 years on the other hand, you know, it's like, you need to be, you can't just sort of like do nothing for 25 years. [00:06:44] Adam: So if you had told people back in the nineties that the Sloan digital sky survey was going to still be going after a quarter of a century, they probably never would have funded it. So, you know, I think that That you have an advantage in the foundation world, as opposed to the the, the federal funding, which is that you can have some flexibility about the timescales on what you think. And so you don't have to simply go from grant to grant and you're not kind of at the mercy of a Congress that changes its own funding commitments every couple of years. We at the Sloan foundation tend to think that it takes five years at a minimum to have impact into any new field that you go into it. And we enter a new science field, you know, as we just entered, we just started a new program matter to life, which we can talk about. [00:07:35] That's initially a five-year commitment to put about $10 million a year. Into this discipline, understanding that if things are going well, we'll re up for another five years. So we kind of think of that as a decadal program. And I would say the time scale we think on for programs is decades. The timescale we think of for grants is about three years, right? But a program itself consists of many grants may do a large number of investigators. And that's really the timescale where we think you can have, have an impact over that time. But we're constantly re-evaluating. I would say the timescale for rethinking a program is shorter. That's more like five years and we react. So in our ongoing programs, about every five years, we'll take a step back and do a review. You know, whether we're having an impact on the program, we'll get some outside perspectives on it and whether we need to keep it going exactly as it is, or adjust in some [00:08:35] interesting ways or shut it down and move the resources somewhere else. So [00:08:39] Ben: I like that, that you have, you almost have like a hierarchy of timescales, right? Like you have sort of multiple going at once. I think that's, that's like under underappreciated and so w one thing they want to ask about, and maybe the the, the life program is a good sort of like case study in this is like, how do you, how do you decide what pro, like, how do you decide what programs to do, right? Like you could do anything. [00:09:04] Adam: So th that is a terrific question and a hard one to g

    1h 5m

  8. 05/30/2022

    Managing Mathematics with Semon Rezchikov [Idea Machines #44]

    In this conversation, Semon Rezchikov and I talk about what other disciplines can learn from mathematics, creating and cultivating collaborations, working at different levels of abstraction, and a lot more! Semon is currently a postdoc in mathematics at Harvard where he specializes in symplectic geometry. He has an amazing ability to go up and down the ladder of abstraction — doing extremely hardcore math while at the same time paying attention to *how* he’s doing that work and the broader institutional structures that it fits into. Semon is worth listening to both because he has great ideas and also because in many ways, academic mathematics feels like it stands apart from other disciplines. Not just because of the subject matter, but because it has managed to buck many of the trend that other fields experienced over the course of the 20th century.   Links Semon's Website Transcript [00:00:35]  Welcome back to idea machines. Before we get started, I'm going to do two quick pieces of housekeeping. I realized that my updates have been a little bit erratic. My excuse is that I've been working on my own idea machine. That being said, I've gotten enough feedback that people do get something out of the podcast and I have enough fun doing it that I am going to try to commit to a once a month cadence probably releasing on the pressure second [00:01:35] day of. Second thing is that I want to start doing more experiments with the podcast. I don't hear enough experiments in podcasting and I'm in this sort of unique position where I don't really care about revenue or listener numbers. I don't actually look at them. And, and I don't make any revenue. So with that in mind, I, I want to try some stuff. The podcast will continue to be a long form conversation that that won't change. But I do want to figure out if there are ways to. Maybe something like fake commercials for lesser known scientific concepts, micro interviews. If you have ideas, send them to me in an email or on Twitter. So that's, that's the housekeeping. This conversation, Simon Rezchikov and I talk about what other disciplines can learn from mathematics, creating and cultivating collaborations, working at different levels of abstraction. is currently a post-doc in mathematics at Harvard, where he specializes in symplectic geometry. He has an amazing ability to go up, go up and down the ladder of [00:02:35] abstraction, doing extremely hardcore math while at the same time, paying attention to how he's doing the work and the broader institutional structures that affect. He's worth listening to both because he has great ideas. And also because in many ways, academic mathematics feels like it stands apart from other disciplines, not just because of the subject matter, but because it has managed to buck many of the trends that other fields experience of the course of the 20th century. So it's worth sort of poking at why that happened and perhaps. How other fields might be able to replicate some of the healthier parts of mathematics. So without further ado, here's our conversation. [00:03:16] Ben:  I want to start with the notion that I think most people have that the way that mathematicians go about a working on things and be thinking about how to work on things like what to work on is that you like go in a room and you maybe read some papers and you think really hard, and then [00:03:35] you find some problem. And then. You like spend some number of years on a Blackboard and then you come up with a solution. But apparently that's not that that's not how it actually works.  [00:03:49] Semon: Okay. I don't think that's a complete description. So definitely people spend time in front of blackboards. I think the length of a typical length of a project can definitely. Vary between disciplines I think yeah, within mathematics. So I think, but also on the other hand, it's also hard to define what is a single project. As you know, a single, there might be kind of a single intellectual art through which several papers are produced, where you don't even quite know the end of the project when you start. But, and so, you know, two, a two years on a single project is probably kind of a significant project for many people. Because that's just a lot of time, but it's true that, you know, even a graduate student might spend several years working on at least a single kind of larger set of ideas because the community does have enough [00:04:35] sort of stability to allow for that. But it's not entirely true that people work alone. I think these days mathematics is pretty collaborative people. Yeah. If you're mad, you know, in the end, you're kind of, you probably are making a lot of stuff up and sort of doing self consistency checks through this sort of formal algebra or this sort of, kind of technique of proof. It makes you make sure helps you stay sane. But when other people kind of can think about the same objects from a different perspective, usually things go faster and at the very least it helps you kind of decide which parts of the mathematical ideas are really. So often, you know, people work with collaborators or there might be a community of people who were kind of talking about some set of ideas and they may be, there may be misunderstanding one another, a little bit. And then they're kind of biting off pieces of a sort of, kind of collective and collectively imagined [00:05:35] mathematical construct to kind of make real on their own or with smaller groups of people. So all of those  [00:05:40] Ben: happen. And how did these collaborations. Like come about and  [00:05:44] Semon: how do you structure them? That's great. That's a great question. So I think this is probably several different models. I can tell you some that I've run across. So during, so sometimes there are conferences and then people might start. So recently I was at a conference and I went out to dinner with a few people, and then after dinner, we were. We were talking about like some of our recent work and trying to understand like where it might go up. And somebody, you know, I was like, oh, you know, I didn't get to ask you any questions. You know, here's something I've always wanted to know from you. And they were like, oh yes, this is how this should work. But here's something I don't know. And then somehow we realized that you know, there was some reasonable kind of very reasonable guests as to what the answer is. Something that needed to be known would be so I guess now we're writing a paper together, [00:06:35] hopefully that guess works. So that's one way to start a collaboration. You go out to a fancy dinner and afterwards you're like, Hey, I guess we maybe solved the problem. There is other ways sometimes people just to two people might realize they're confused about the same thing. So. Collaboration like that kind of from somewhat different types of technical backgrounds, we both realized we're confused about a related set of ideas. We were like, okay, well I guess maybe we can try to get unconfused together.  [00:07:00] Ben: Can I, can I interject, like, I think it's actually realizing that you are confused about the same problem as someone who's coming at it from a different direction is actually hard in and of itself. Yes. Yes. How, how does, like, what is actually the process of realizing that the problem that both of you have is in fact the same problem? Well,  [00:07:28] Semon: you probably have to understand a little bit about the other person's work and you probably have to in some [00:07:35] way, have some basal amount of rapport with the other person first, because. You know, you're not going to get yourself to like, engage with this different foreign language, unless you kind of like liked them to some degree. So that's actually a crucial thing. It's like the personal aspect of it. Then you know it because maybe you'll you kind of like this person's work and maybe you like the way they go about it. That's interesting to you. Then you can try to, you know, talk about what you've recently been thinking about. And then, you know, the same mathematical object might pop up. And then that, that sort of, that might be you know, I'm not any kind of truly any mathematical object worth studying, usually has incarnations and different formal languages, which are related to one another through kind of highly non-obvious transformation. So for example, everyone knows about a circle, but a circle. Could you could think of that as like the set of points of distance one, you could think of it as some sort of close, not right. You can, you can sort of, there are many different concrete [00:08:35] intuitions through which you can grapple with this sort of object. And usually if that's true, that sort of tells you that it's an interesting object. If a mathematical object only exists because of a technicality, it maybe isn't so interesting. So that's why it's maybe possible to notice that the same object occurs in two different peoples. Misunderstandings. [00:08:53] Ben: Yeah. But I think the cruxy thing for me is that it is at the end of the day, it's like a really human process. There's not a way of sort of colliding what both of, you know, without hanging out.  [00:09:11] Semon: So people. And people can try to communicate what they know through texts. So people write reviews on. I gave a few talks recently in a number of people have asked me to write like a review of this subject. There's no subject, just to be clear. I kind of gave a talk with the kind of impression that there is a subject to be worked on, but nobody's really done any work on it that you're [00:09:35] meeting this subject into existence. That's definitely part of your job as an academic. But you know, then that's one way of explaining, I think that, that can be a little bit less, like one-on-one less personal. People also write these a different version of that is people

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About

Idea Machines is a deep dive into the systems and people that bring innovations from glimmers in someone's eye all the way to tools, processes, and ideas that can shift paradigms. We see the outputs of innovation systems everywhere but rarely dig into how they work. Idea Machines digs below the surface into crucial but often unspoken questions to explore themes of how we enable innovations today and how we could do it better tomorrow. Idea Machines is hosted by Benjamin Reinhardt.

Information

  • Creator
    Benjamin Reinhardt
  • Years Active
    2018 - 2024
  • Episodes
    50
  • Rating
    Clean
  • Show Website
    Idea Machines

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