The Knowledge Project with Shane Parrish - #15 Samuel Arbesman: Future-Proof Your Knowledge

Episode Date: November 28, 2016

Samuel Arbesman is a complexity scientist focusing on the changing nature of science and technology. We discuss learning, reading, and how to optimize both to get the best outcome.   Go Premium: Members get early access, ad-free episodes, hand-edited transcripts, searchable transcripts, member-only episodes, and more. Sign up at: https://fs.blog/membership/   Every Sunday our newsletter shares timeless insights and ideas that you can use at work and home. Add it to your inbox: https://fs.blog/newsletter/   Follow Shane on Twitter at: https://twitter.com/ShaneAParrish Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to the Knowledge Project. I'm your host, Shane Parrish, curator behind Farnham Street, an online intellectual hub of interestingness covering topics like human misjudgment, decision-making, strategy, and philosophy. Today we're going to be talking about technology. The Knowledge Project allows me to interview amazing people from around the world to deconstruct why they're good of what they do, more conversation than prescription. On this episode, I have Sam Abrasman. Sam is a complexity scientist whose work focuses on the nature of scientific and technological change.

He's currently the scientist in residence at Lux Capital, which is a venture capital firm focused on big daring ideas in science and technology. Sam's also written two books that I love, The Half-Life of Facts and Overcomplicated. On this episode, we're going to get to know Sam better and explore overcomplicated in our relationship with technology. in the process we're going to learn a bit the difference between physics thinking and biological thinking which I think you'll particularly enjoy let's dig in we're going to talk about your new book but it's been a long time coming I loved your old book the half-life of facts

overcomplicated's coming out soon thanks for being here thank you it's great to be able to talk to so I want to I've taken a bit of a different format with the podcast recently somebody gave me these table topic cards and I've been going through all these questions and I've just been randomly asking people but I think we'll start with what was your first job my first job and this is including like even like little part-time jobs and stuff like that yeah the first job that I can really remember I spent and I'm not even totally sure I was paid for this but it felt job like I spent a summer being essentially like a lab tech in a like electrochemistry company that was owned by family friends of ours.

And I spent essentially kind of like a bottle washer slash data collector, but it gave me my first exposure to the world of science and research. Cool. That sounds like a, I mean, it's had a big impact on you, right? Oh, yeah. So I did that. And then, of course, I think like the next summer, several summers after that, I was a camp counselor at a day camp.

So it really wasn't like, oh, from there, I was like doing nothing but science. But yeah, I guess it had an impact. How did you end up writing your first book, The Half Life of Facts? That was your first book, right? Yes, that was my first book. So you mean like the process or how, like what made you want to write that book? I've always been interested in writing and kind of writing for popular audiences. And I had been playing in the space of thinking about the science of science and kind of the nature of how what we know changes and kind of like looking actually at the regularities of this and trying to understand how to quantify.

this kind of area. And I had even begun, and kind of in the early stages of doing some research related to that, but then actually wrote this little piece about what I called meso facts. So meso facts were sort of facts that bits of information that change neither too slowly nor too rapidly. They're sort of at the middle or meso scale. The idea is that there's bits of knowledge that change very, very rarely like how many continents there are on the planet. You learn those once you're good. Then there's bits of information that change fairly quickly, like what the stock market closed at yesterday or what the weather's

going to be like tomorrow. We're pretty good at updating those kinds of information. But in between, there's a whole slew of things that we often learn the same way we learn the unchanging facts, like things like how many billions of people there are on the planet, or how many elements there are on the periodic table. These things change, but they might change over the course of decades or over the course of a human lifetime.

And these kind of meso facts are, and they're sort of in this weird category where we learn them once, but we really should be updating them mentally. So I wrote this little essay about it. And it got a lot of attention, a number of agents and publishers contacted me and said, do you think there's a book in there? And since I've been thinking about this, I said, yes.

And I'm excited to kind of tell the story of, like, the larger picture of how knowledge grows and changes and kind of the science of science and science of information, growth and change. Has that, oh, I have so many questions right now. Has that changed how you learn things? Like, do you prioritize learning differently now because of that? I think ideally, I think practically maybe I don't always as much as I, I should. But I know when people talk about, maybe it's more like how I kind of think about information. So for example, when people talk about how the internet and Google is ruining

our brains or our memories, I am actually much more positively inclined towards those technologies simply because if we don't necessarily have as good memories anymore, it means that we now actually have to look things up more often and make sure we actually have the information correct, which means we are more likely to have the most up-to-date bit of knowledge. So I actually think those kinds of things are really good. And I think for me, after working on that book, I think it's also, I just, I've gotten better at delighting in being wrong and having the facts that I thought were true being overturned. Because I know this is the way of the world. And I think this is kind of, and for me, it's more just having a scientific mindset towards everything around.

And science, science is, it's a body of knowledge, but even more important than that, science is really a means of querying the world. And I think recognizing that everything we know is constantly in draft form is really, like, that's the way we should be thinking about the world. And so whether or not you're doing science or simply just living your life and reading magazines and having bits of knowledge and information about nutrition or how to take care of babies being overturned, I think these things are all really, really good and they're really exciting. And I think we just need to kind of make that more explicit in our everyday lives. What was your process for writing? Like, what did you do every day? How did you go about that? So for this most recent book, which I have maybe a better memory of, the way I did it was I set myself a goal of kind of just a certain amount of text to create per day, at least as the initial step, I said something like a thousand words a day.

And so I just write a thousand words. And it didn't have to be on a single topic. It didn't have to be good. It could be kind of on various things that I wanted to write about. It could be multiple things that I wanted to write about related to the book over the course of the thousand words. And then when I got something that was book length, then I kind of took it all, printed it out, kind of began rearranging it, realized that there were parts in it that I wanted to remove. There were parts that need to be fleshed out significantly. There were things that needed to be connected.

And then I kind of went through this kind of iterative smoothing process. And eventually I felt like I had something. Then I took the first chapter, kind of the introductory chapter, showed it to my wife. She read it and said, this is garbage. And I said, what do you mean by that? And so we actually had this long discussion about kind of the disruptions. between what I thought I was writing about and what I actually had written. And then I explained to her what my goals were.

And she's like, oh, write that explicitly. So I went back, worked on that chapter, clarified it. She said, okay, this actually makes sense now. And then went and kind of retooled the rest of the book. Then I went through it and then showed it to my editor and kind of went from there, kind of this constant iterative, repetitive process of kind of going through and making sure that there was little, or at least smaller and smaller differences between what I thought I wanted to write and what I actually was.

writing. At the same time, though, there were also like whole sections and whole topics that I eventually realized were just beyond the mandate and the theme of the book. While they were kind of interesting, they really ended up not adding much and probably confusing things. And so I slowly but surely kind of tightened and tightened the actual theme and narrative of the actual story I wanted to tell. And so I think initially for this most recent book, I wanted to include huge amounts about philosophy of science and the nature of like, the complexity of our scientific models. And I think there might still be some of that in there, but the vast majority of that

has been found on the cutting room floor. I want to talk about your new book. We'll get into that in a little bit. Do you write more in the morning or at night? I write in the morning. So my goal is to always get the majority of the writing done as soon as possible so that it no longer hangs over my head the rest of the day. And so I can feel like whatever I do for the rest of my work, I have accomplished.

I have accomplished the goal that I needed to do writing-wise, which is. is nice. To what extent would you say science or art is more essential to humanity? Ooh. As somebody, you're a practitioner of both, right? Oh, that is an interesting question. I think, I mean, so here's the thing. So first of all, if you ask a lot of scientists, I think they would discuss how there's a lot of art within the science and how science is conducted and the way in which you ask questions. And of course, you still have to answer those questions in a very rigorous and kind of scientific and perhaps non-artistic way, but the way you can ask questions, and sometimes even the way you answer them, if you can think of a very clever

experiment, there's often a certain amount of art to that as well. So I actually think a number of scientists would kind of push back at that question. At the same time, though, I think they're both very necessary. I think they're both kind of different ways. I mean, I'm not even sure how different ways they are about thinking about the world. They're kind of both approaches to querying the world, but they're also ways of producing these beautiful things. And so, and science is almost like beautiful output constrained by reality. And art is also beautiful output constrained by reality of the media that you're using in some very different sort of ways. I'm not entirely sure if that was a non-answer. But I think they're both very good. And at the

same time, though, there are certainly certain types of artistic forms that speak to me more than others, like certain types of contemporary art I have more trouble with than others. Other ones I like a lot. And I also think there's a lot of very interesting points of interaction. So there's like one whole, like the whole realm of like computational and generative art. What's that? It's where you almost create algorithms that are responsible for generating the, the images that you're looking at or some sort of artistic output. So like, for example, reading, playing,

learning. Stellist lenses do more than just correct your child's vision. They slow down the progression of myopia. So your child can continue to discover all the world has to offer through their own eyes. Light the path to a brighter future with stellist lenses for myopia control. Learn more at SLOR.com and ask your family eye care professional for SLOR Stellist lenses at your child's next visit. You could create a small computer program that generates a a tree or an entire forest of trees. And these are all computationally generated. So no one really was the person who kind of painted the tree or kind of drew the tree.

But they're all beautiful. And they're all really, really wonderful to look at. And you can also, you can also do this where people are now using machine learning techniques to generate text in response to images that are being shown to the computer program. And that also generates some really interesting things. Now, of course, the question is, what is the actual artistic output? Is it the computer program that generated that? Is it the result?

I think in some ways, maybe sometimes those questions are kind of beside the point, because in the end, the output is very interesting, whatever level of output you're looking at. But yeah, there's some very interesting kind of points of connection with this kind of computational art and generative art.

And you also have generated design where people are using computer programs to design objects that almost have this biological appearance to them, but are still functional. And they also are really beautiful in different sorts of ways than we might think of kind of traditional designed objects. Do you think that we will have the same emotional connection as humans to art that is derived from an algorithm as we do, art that's derived from a person?

Maybe. I imagine if you don't tell someone where it comes from, you probably can get a similar kind of response, especially if it's almost indistinguishable. So there are actually way, there are computer techniques where you can give an image to a computer program. and it will actually generate it in the style of famous artists. In some cases, that actually looks pretty good, and I wonder if it would generate, kind of yield the same sort of emotional reaction. At the same time, though, I think maybe some emotional reactions

for computational general art would be maybe flattened. People would say, oh, it's not, doesn't feel as authentic, or maybe it would be frightening to some people when they realize that computers can kind of be creative in certain ways. For me, it's really exciting just to kind of see the extent to which computationally generate art can be as beautiful as it is. is. I think that's actually really interesting to look at. And so I'm, I'm maybe less bothered by some of the like epistemological issues and just kind of saying like, oh,

these outputs are really cool. And plus, and it's also, and if they're, they're beautiful and they're also, they're always different. And so you can see this even kind of going back like years ago when people first started doing like fractal art, either kind of just like zooming into like the Mandelbrot set or generating random mountainscapes that actually look like mountains but are entirely computer generated. These are really beautiful. They eventually kind of, and there's only so many of them you can look at before you're like, I've kind of seen everything that, everything kind of looks the same.

But at the same time, they're really, really interesting to look at and kind of see how, like, the variety, like the sheer variety of things that can be generated computationally. And there are a lot of people who are really at the cutting edge of this, both artists as well as computer scientists and practitioners. Yeah, and I'm just really impressed by the state of this space and what is really happening. I sort of at the frontier of computers and science blended with art. Yeah, I think, I mean, like the next decade or so is going to be fascinating to watch how that plays in. Yes. Who's the best teacher you've ever had and what made them the best

teacher? The best teacher I've ever had. Oh, probably my parents. I mean, they were very good in not only encouraging me to kind of always look things up and kind of showing me and guiding me on, like, how to learn new things, but also really, they were really instrumental in just making sure I, um, like, always kind of like thought about the world and playful ways. And actually, so the, um, the thing that I was always told by my father before I left to school, before I left for school when I was little was the phrase, uh, think, have fun and be a mench. Um, and, and be a mench is sort of the, and mench is like Yiddish for kind of like being a good person. And, and essentially he

wanted to kind of instill in me these like three ideas that you have to think about the world you can't you can't just kind of have this kind of unquestioning attitude towards it you have you have to have fun you have to have kind of this like playful attitude towards the world it's not just like I mean especially when you're learning new things it shouldn't be wrote it shouldn't be boring it should be playful and should be exciting you should have this kind of always the sense of curiosity and of course you should also be a good person while doing all these these three these these different things and I think that kind of attitude um and a perspective um I've really tried to live up to that. And it's really stuck with me. I think those kind of goals are, and those are the

kind of things that I'm going to be instilling into my own children. Those are things to get passed down family to family because it really resonated with you. Are you going to be telling your kids to look things up in physical books? Are you going to be telling them to look it up on the internet? Or wherever you can find things. I happen to have a lot of physical books, but the internet's also a great source of information. I mean, I would say physical. books are a great hedge against, like, technological change. And I know, like, my physical book, if I, like, 20 years from now, it will still be kind of backwards compatible with the human eye. But, like, an ebook, not entirely clear. But at the same time, if you want the most

up-to-date knowledge, in some ways, and the internet and, like, Wikipedia in particular, it's kind of the closest thing we have to complete world knowledge. I say that kind of flippantly, but I think there's some truth to that. And I think having access to those, to that kind of up-to-date information is really, really powerful and really important. And I think a balance between the old and the perennially wise encapsulated kind of maybe in like older books versus kind of the up-to-date and the recent encapsulated on the things you can look up online is a really good balance to kind of think about the world and make and understand it. While you were saying that, I had an interesting question pop in my mind, which is as an author, you deliver e-books,

Kindle books. As a book lover, you have a very physical connection with books that are unchanging. The connection is not unchanging, but the text is unchanging. How do you feel about the ability to update books, like update Kindle books, to reflect this latest information? Maybe you go in, you add an extra chapter, you change some things around, you fix some errors. That's not possible in physical books. It is possible in the electronic kind of medium. How do you feel about that as an author, a scientist, given your research. So I think the possibility is interesting. At the same time, though, maybe as an author, like at a certain point, I mean, you know

the book is never done. There's always new things to add or other bits of information to update as we kind of learn new things about the world. But at the same time, maybe as an author, you kind of want to just be done and say, okay, like, this is a finished product and, like, you have to be willing to kind of release it out into the world. And so I think there are different types of products that are maybe, like, outputs that are more well-suited to constantly being updated and other ones that are kind of just

like, this is a finished product and we can be done with it. And I've heard stories of like, even like with novels of like people editing them like right before they're going on stage to do a reading of their novels, even though of course the novel is done because they just realize, oh, they want to like say things a little bit differently. And of course, they can't do that. And a novel, and it's not even like, oh, the novel is being updated with new information. It's just being up like there's always tinkering to be done.

I think there's always tinkering to be done. But I think you also have to kind of recognize that you spend a lot of time with And it's like, now you have to be willing to kind of move on to the next thing. So I think there's, I think with with science, though, especially being able to not necessarily constantly update a single paper, but maybe link papers to newer papers that have information or kind of caveats or response to the original research. I think that kind of thing is very, very powerful. And I think in the scientific world, the ability to have all of our research open and

interconnected and constantly updated is really the key to actually making sure that as a scientific community, people can really query the most up-to-date state of the field and really test and see if it is the way things are, as opposed to right now in science, sometimes you have things that are out of date, but also because knowledge might be either locked behind journal paywalls or these articles are not. not necessarily as interconnected as they could be. People sometimes end up reinventing certain ideas. Sometimes that's just something due to jargon boundaries

where one field might reinvent a model that had been known for several decades in some other field because they just didn't even know that this thing was done using slightly different terms. This kind of thing, that will probably never stop happening. But I think by having open interconnected science, we will be able to kind of make sure that the resource, like that we don't waste resources

and we are kind of doing research. in the best possible way. I like that. Switching yours a little bit, would you choose to be the worst player on a winning team or the best player on a losing team? You said, so worst player on a winning team or best player on a losing team?

Yeah. Interesting. I think if you're, well, certainly being the worst player, kind of cynically the worst player on a winning team, is exciting because you still get to win. But I think the less cynical reason for that would be, If you're the worst player on a winning team, there's people around you who are all going to

kind of force you to get better. So that might be kind of a less cynical reason to be on it. The best player on a losing team might be a little bit discouraging. I'm not sure. I think it'd be more fun to be on a winning team, even if I'm not doing that great, because I'll at least have some good role models to look up to. And I'll be winning alongside. That dovetails into my next question, which is, how do you, how do you go about defining success for yourself?

So, that's a great question. So I think success, and certainly there are like external metrics. And I, there's a, there's a great book called The Guide to the Good Life by William Irvine, I believe. It's like, it's about essentially stoic wisdom. And I think you might have even mentioned it and discuss it on you on your blog. But it's the idea of like and one of the things he talks about is when you think about success there's there's only so much you can control especially externally in terms of one success and the best way to try to be successful and at the same time be happy with one's attempts is to internalize kind of the success metrics so like for example let's say you're trying to write a novel if you I mean there's the external metrics like best-selling novel

well regarded, critically acclaimed, all these different kinds of things. But then there's the internal metrics, which are did you make the best version of the book possible? And I think, and those are the kind of things you actually do have control over. And I have tried kind of, I'm more and more,

and not always successfully, this is kind of a continuing thing, but to try to kind of make metrics success much more internal, where you try to do, you try to be the best version of yourself possible and kind of be true to yourself and true to your abilities. And if there are external metrics that come along with it, that's wonderful. But there's only so much you control.

And so therefore, you kind of have to be, you have to recognize that. And I think if your only measures of success are truly external measures, you're really never going to be happy. So in that case, while being on a winning team is an external metric of success, being the worst player, well, I guess the question is, are you the worst player or are you a bad player? And if you're kind of the best you can be, but still the worst on a good team, then I think you've done your best.

So it sounds like you have inherently an inner scorecard primarily to, I mean, there's no, it's a false duality between the two. Everybody is somewhere, you know, in the middle. But it sounds like you lean towards more internal metrics, things you can control the process. I strive. I would say I strive. And some of times that's always aspirational because I'm like I'm a person. Like there's always things I wanted to do that are kind of like like that are kind of independent of me. I don't have control over and, um, and sometimes you can be disappointed if they don't necessarily work out. Um, but I think for long term happiness and feelings

of success, you have to be more kind of, yeah, those, you need to have those internal scorecards more so. Otherwise, there's always things you can, there's always people you can compare yourself to and come up short, and there's always things you could be doing or succeeding at externally, and you're not. And so therefore, if you only use those external scorecards, you'll never be happy. And I'd much rather be happy than simply just kind of check boxes. I agree with that. Are there any other books that kind of dramatically impacted your life? So I would say, so another book that I really, I would say it was probably one of the best books I've read in the past few years. years in like the nonfiction realm is um the book immortality um by the philosopher stephen cave

where he looks at um the different ways in which humanity has tried to live forever um both kind of since ancient times as well kind of the modern versions like things like um immortality of the soul or just simply immortality through not dying and he looks at kind of the ancient the ancient version of it as well as the modern version of it and and eventually he kind of he actually goes through all of these and and find them all wanting, which is kind of interesting. It's an unbelievable book, finds them all wanting. And then he says, okay, what is the, like, what, how should we respond? And he ends up falling back on wisdom literature, sort of like things like Ecclesiastes

or the kind of writings of the Stoics and says these are the kind of things like recognizing that like even in the transience of life, you can still make life meaningful. And so the book is unbelievable. It kind of deals with these very, very deep themes of humanity, like, mortality, immortality, meaning in life. And, uh, but it also weaves in, um, amazing stories like about Alexander the Great's life, um, or the first emperor of China and his goals, um, as well as a lot of philosophy and, and other ideas. It's, it's, it's fantastic. Um, I would say that was really, that,

that was, that was a really, uh, influential book. Um, and then another one, I would say in a very different way, um, is, uh, the foundation trilogy by Isaac Asimov. Um, so I don't know if you're familiar with it. It's a set of science fiction novels written, I think in the 50s, but a long time ago. And it takes place in the far future. And the idea behind it is that there's this scientist, Harry Selden, who realizes that the galactic empire is going to fall. There's going to be a period of the dark ages. And so he creates this foundation to kind of shorten the amount of time there's going to be a dark age. But the idea behind is that this man, Harry Seldon, his branch of science is called psychohistory, which is essentially this quantitative science of human societies. And the idea is that even though each individual human is not predictable, if you get a whole bunch of people together in large enough groups, then suddenly there are actually, there are regularities.

There are rules to understanding how human societies operate. And so for me, that was actually one of the things that got me interested in thinking about computational social science and kind of quantitative science of human organizations and societies and cities. And it turns out if you ask a lot of social scientists or scientists who are kind of involved in computational social science or quantitative or network science and things like that, a lot of them read foundation at an early age and we're actually very interested, very influenced by it.

I've never read that. I'm going to have to pick that up after this. They're fun. Anything else come to mind? I remember when I was younger liking the novel Childhood's End by Arthur C. Clark a lot, although I have to say I had actually forgotten most of it. So there was a mini-series version of it, I think, on the sci-fi channel recently, and I watch it. And while I was watching it, I was kind of like rediscovering a lot of the plot points because I had forgotten so much of it. So that was kind of an interesting thing.

So I'm trying to, I would say those are, those are some pretty influential books of mine. But yeah, like Foundation Trilogy, Guide to the Good Life, Immortality. I guess also, I would say, I guess in the fiction realm, another one that I really enjoyed, Neil Stevenson's Cryptonomicon, just this, the idea that there can be a book that weaves together an amazing plot as well as some really, really profound ideas on philosophy and computer science and technology together. I found, like, that was, I think, one of the first times I had seen a book that had really done this. where, like, there were these unbelievably informational pieces as well as unbelievably, like, fun read. Like, it was, it's also an unbelievably fun read. And I think, I'm a big fan of most of Stevens' work.

I love his stuff. But I would say, Cryptonomicon was one in particular that really kind of demonstrated that you could do this kind of thing together. I really enjoyed it. Were you always a big reader? Yes. I can, I actually can remember when I was younger, there was a period where I think I sent myself the goal of, like, reading, like, five, like, five Arthur C. Clarke novels over the course

of four days. I was trying to do, like, more than one book per day and seeing if that was possible. And I think I succeeded. I saw I was a big reader, but I also read, and I would say I read heavily science fiction when I was younger, as well as actually, I read a lot of, like, Mark, like, the collected columns of Martin Gardner, who wrote, like, recreational mathematics, the recreational mathematics column in Scientific American. So that was, like, one of the first places where people read about Conway's Game of Life, the sort of cellular automaton world, as well as a whole much of other things. I would kind of read recreational mathematics, science fiction, and pretty much anything that

was like super nerdy that combined philosophy and science and mathematics and technology. That was my catnip. Did you have a lot of physical books in your house? Did your parents keep a lot of books? Yes. Yes. Yeah, we actually did. Yeah, we had a lot of books.

And how did they encourage you to read? or was it just something you picked up or you saw them doing or? I imagine it was probably leading by example because I think I can remember I know stories of like even even at a young age I was like trying to model my parents and like kind of walking around

with like little books. So like even if it was like a little gray address book that really I wasn't reading because like obviously it was an dress book. I don't even know if I could read at that point. But like I wanted to kind of be a reader. And so I think it was a combination of modeling having the books around and also be encouraged.

Like if I didn't know something like going to look it up and I can remember at like at dinners with my family and like when my grandparents were over we'd have these debates and then we would like one of us would go rush over and like grab the like a volume from the encyclopedia and try to actually look things up and see that's awesome someone was correct and like and even I still do I still do those things now um at meals just because like I feel like it's it's great to have that uh yeah I mean I do that all the time on my phone. So overcomplicated, can you give me or give us, I guess, the audience, an overview. I've read the book, but most people haven't. Can you give us an overview of the book and how it started?

Sure. Yeah. So the idea behind the book is that we all know that technologies are becoming more and more complex and kind of more complicated over time. But increasingly, technologies are becoming so complicated that not only just does the everyday person not fully understand them, but But increasingly even the experts who work on them with a daily basis or even the people who built them don't necessarily fully understand them or their implications any longer. And so the book looks at kind of what are the forces that lead us towards this ever greater incomprehensibility? And what do we do about it?

Do we simply say like, we're in trouble? Like this is kind of the new state of being or are there ways of meeting these technologies halfway? And so I happen to be kind of fairly optimistic person by disposition. So I think there are ways of meeting these technologies halfway. And so I kind of lay out different perspectives and ways of approaching our technologies. And again, I'm using technology fairly broadly. I'm using it to mean everything from like the software on your computer to the entire

internet, to our urban infrastructure, to even our legal systems, our legal codes. What surprised you the most as you were developing the book and the idea and flushing out the ideas? You didn't have that preordained before you went in, I suspect. out. So I think with, and with this, it was more, initially, I knew there were, I knew there were many examples of this kind of thing. But as I was writing the book, and as I was talking to people, it just became abundantly clear that an area after area, this kind of things, and this incomprehensibility wasn't at the limits and like edges of our experience. Like, this incomprehensibility

is found in aspects of every part of our lives. And so it's like the software on our computers, on our desktops, it can be found in like medical devices, kitchen appliances, in our cars with like millions of lines of code. This like reduced understanding and vast complexity of technology is really everywhere we look. And I would say it's probably been accelerated significantly due to the fact that we now have computation embedded within everything else. There's only so many levels of kind of like levels of hierarchy of complexity you can have if you don't have the ability to have code within something.

And once you do, though, then suddenly it can baffle your mind. And I think that was really interesting to see. And every time I discuss it with someone, like, from no matter what the domain, people would just give me more and more examples. And it was fantastic and a little surprising to see that. What was your favorite chapter in the book? So I would say my favorite chapter might be the final chapter where I look at one of the perspectives of technology that I think, I would say people sometimes when they're confronted

with technology they don't understand, or maybe they can't even understand, is they often respond with one of two extremes of either fear in the face of the unknown or this reverential, almost religious sense of awe towards technology they don't understand. And I think both of these, while they're fairly common, they're not good. Mainly because they end up cutting off questioning. Because if you're really afraid of something, you're so afraid you can't even question it. And if you have this reverential awe toward something, you don't realize that maybe it's actually a lot messier than it really is. And I think somewhere in between, almost like a humble but constantly questioning approach towards technology is really the way we need to think

about it. And I use the analogy of I kind of look at the way certain philosophers in the middle ages approach the world around them. So I use the example of Moses Maimonides, a philosopher from the 12th century, I believe. And he recognized that, and there were things that we would never understand. There are natural limits to what the human mind can understand. I mean, I think people kind of recognize this. But at the same time, over the past maybe century or two, there's been this like somewhat like triumphless sense when it comes to science that like if there's a question no matter what if we put our minds to it we can understand everything and and I think and that's that's not always true there are limits and we're going to bump up against our limits

understanding and that's true even for the technologies that we ourselves have made and we think oh that we're these rational logical creatures and therefore the constructions we make should also be logical and rational and that's not true these things evolve over time they're Kluji, they're messy. There's a lot in there that really no one fully understands anymore. And that's okay. As long as we still have a way of kind of like slowly, iteratively, and with a sense of humility, approaching our technology, I think then we'll never be overwhelmed by what we ourselves have built. I like that. I think for me, the chapter that resonated or actually the two ways of thinking that I took away from the book that I thought were incredibly insightful were

the biological versus physical thinking, so specifically chapter five, can you walk us through the differences between those two and how they manifest themselves when viewing technology? Sure. So I use this kind of dichotomy of two modes of thinking, which I call physics thinking or biological thinking. And it's an oversimplification. And of course, not all biologists use biological thinking, not all physicists use physics style thinking. So with that caveat in mind, The two modes are that the physics mode of thinking takes something and tries to kind of create a simple, a simple means of understanding the vast majority. So like, for example, like a single equation that explains the vast majority of the motion of objects and or like, or you can write down like a single formula and it would explain like 50% of what's going on in cities and how they work. So something like that.

On the other extreme, you have the biological approach, which says that you actually need to focus on the details and that the details and kind of cataloging the diverse instances of things that don't make sense. Not only do the details matter, but sometimes that's like those are wonderful and they're really, really exciting. And you can see kind of those trends in like certain biologists, like kind of the naturalist of old who would go around and collect a butterfly. as well as certain types of biologists who would just kind of, they focus on a certain molecular pathway or the relationship of the, or the interactions of two different species within a larger ecosystem, recognizing that you can still, even, you can even create models of understanding these things. They're not, they're still amenable kind of mathematical modeling, if that's what you want,

but focusing on kind of the details is very, very important for a larger understanding, while the physics approach might be, like, let's kind of sweep away the details and just focus on kind of the abstractions that we can learn from. And I think when we think about technologies, oftentimes we might feel that we need to have the physics style approach because we've built these systems. And so therefore, they should be amenable to kind of a simplified understanding. And oftentimes, that's not true. When we have a large technological system that interacts with the real world, the real world

is complicated. The technological system needs to be complicated. It needs to deal with like all the many edge cases and weird exceptions. Like when you build a self-driving car, for example, it can't just deal with the one case of driving on a highway in perfect weather. It might need to deal with rain or sleet or people jumping out into the middle of the road or animals doing their thing or glare. And when you build a technological system that mirrors the world in all of its complexity,

you have to be kind of aware of all the details. you end up making something that's more biological in structure and ends up therefore being more amenable to kind of this biological approach. And so oftentimes when you're confronted with a really complex technological system, you might have this desire to really think about it in a simple way. But in fact, it might be more appropriate because, and if a technology has a messy organic field to it has evolved over time and kind of accreted bits and pieces, if it looks biological, maybe we can actually learn from how biologists look at biological systems. And so we can look at the details and kind of catalog bugs in order to kind of gain further understanding, or in the case of like maybe a really complicated machine learning system, we might have a really powerful output, but it might be really difficult to kind of understand what's going on under the hood and how it arrived at that output. And so therefore we have to kind of use this iterative, slowly, like slow approach to understanding how the system did what it did, this almost like more biological approach where you kind of get bits and pieces. And based on that, slowly get a larger picture of what's going on. I think you need both.

You need kind of both the physics mode of thinking as well as the biological approach. But we certainly should not give short shrift to the biological mode of thinking when we're dealing with our own technologies. Often we get into almost like a false duality in these frames of thinking. It's like this is the one way to see the problem. Even if you have these multiple models like biological or physical thinking in your head, you end up kind of narrow and pigeon. But if you look at it through both lenses, you end up removing some of your blind spots.

Oh, yeah. No, I think, yeah, the more models you have, the better you're going to be able to approach these systems. And another kind of approach that I advocate with these technologies is like having simulations, like the same way that in playing with, like in playing SimCity, you might gain a better sense of the complexity of a city rather than not necessarily the detail of the city because SimCity is clearly an oversimplification of a real city. But do you still kind of understand the non-linearities in a city's performance or the bounds of how it operates? I think the same kind of thing if you have a simulation for a technology, you'll gain kind of a better appreciation for how it works and like maybe even how it bites back and does weird things you want to expect.

And I think that kind of thing is really important. So if I'm a manager in an organization, how should this change how I go about managing the complexity, not only in the software, but increasingly in the, you know, the vectors of the organizations. So I would say, I certainly approach it with a sense of humility. Don't think that a single mode of thinking can explain everything and have multiple different types of models, but also don't try to change too much too quickly. I think, especially like when some kind of comes into a new organization, there's a tendency to really make a mark and change a lot of different things.

And that desire should be tempered with this recognition that when a system might be highly non-linear, highly interconnected, and sensitive to change in ways you can't expect. Therefore, you have to respect that. You have to kind of respect the complexity of the system. And only in doing that can you kind of recognize what is in your capability and ability to actually change. Do you think that becomes increasingly hard as things like machine learning, maybe encroach more and more into the day-to-day of the workplace where even if we understood the technology

that created the algorithms, the algorithms, are not creating their own algorithms. So at some point, through so many iterations, we lose track of what's actually happening. We just know we get the output, and that creates a dependence. So I think, yeah, there can definitely be concerns, but I think as long as we recognize that these technologies and they're tools or instruments for doing things, and we've always used tools to kind of be better able to do our jobs and complete our tasks, these feel in some ways qualitatively different because of our reduced understanding. But I think as long as we

kind of recognize what the end goal is and still strive to always try to understand them as best we can, I think it can be okay. Is there any other advice you would have for people to integrate their thinking about technology and how to approach it? So I would say, I mean, I would say one way that a lot of people think about technology, like every day, like when You're looking at your iPhone and it does something weird. We often think with technology, like it's okay to kind of outsource our understanding because there's always some expert like an Apple genius who can fix it or can understand it. And I think we recognize that in many cases, that might not actually be true.

There might be no expert who can fully understand the system. Then it'll be more than we'll be a little bit more deliberate in actually trying to understand these technologies. And I think one thing that from an engineer, perspective, we need to build into these technologies, as well as something I think people just need to be mindful of looking for, is trying to find ways of kind of glimpsing what's happening underneath the hood

of a technology. Because I think for too long, we think a system is like perfect or really nice until something goes wrong and suddenly where the actual complexity and complication is revealed to us. And it's a lot better to have ways of kind of glimpsing the underlying

complexity underneath something that feels kind of very simplified. and very pristine. And so whether or not that's as simple as like playing with like the command line on your computer if you have access to it or or following more carefully what, what's happening with a progress bar as something's being installed, even if sometimes those progress bars are fairly divorced from the reality of what's happening underneath.

I think paying attention to those little details can actually provide you a little bit better sense of what's happening. Certainly not enough always. But it can at least give us a hope of getting a glimpse of what, what's going on kind of beneath the hood. I like that. I mean, it's moving us closer to technology because I don't think technology is going anywhere. So it's kind of integrating us a little bit closer into it, although the complexity of it will, you know, even the simplification of the complexity will be fascinating

to watch how that kind of comes out in the future. Oh, certainly. Yeah, I have no idea where that's going to go. But at some level, I know, like, the map is not the territory, right? when you simplify to such a degree of red versus, you know, green, for instance, on some corporate dashboards, you're missing the inherent complexity. And over time, you forget about what variables drive the equation that's showing you that. So you lose touch. Oh, yeah, certainly.

So I think just having ways of, yeah, try not to lose touch, even if just not losing touch as quickly, I think is really important. Awesome. Listen, I'm cognizant of the time. One more question before we go. What's on your nightstand right now? So two books, I'm kind of in the very early stages of reading, but I'm excited by, are Kevin Kelly's The Inappable about technology and kind of trends, as well as algorithms to live by by Brian Christian and Tom Griffiths. I'm really excited about those.

Nice. Somebody recommended that to me today, I think, actually. Oh, wow. That's fantastic. So I would say those are good. And also another one, which I have not yet started, is Robin Hanson's The Age of M. I'm really excited to kind of see and so the subtitle for that one is

work, love, and life when robots rule the earth. And I've heard unbelievable things about it. So I'm very excited and looking forward to that one as well. Awesome, Sam. Thank you so much. This has been a real pleasure. I really appreciate you taking the time. Thank you.

Hey, guys. This is Shane again. Just a few more things before we wrap up. You can find show notes at Farnham Street blog.com podcast. That's F-A-R-N-A-M-S-T-R-E-E-T-B-L-O-G.com slash podcast. You can also find information there on how to get a transcript. And if you'd like to receive a weekly email from me filled with all sorts of brain food, go to Farnham Street blog.com slash newsletter. This is all the good stuff I've found on the web that week that I've read and shared with close friends, books I'm reading, and so much more.

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