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Samuel Arbesman, "The Magic of Code: How Digital Language Created and Connects Our World—and Shapes Our Future" (PublicAffairs, 2025)
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Samuel Arbusman
To the New Books Network.
- [01:33]
Gregory McNiff
Welcome to the New Books Network. I'm your host Gregory McNiff, and I'm excited to be joined by Professor Samuel Arbusman, the author of the Magic of How Digital Language Created and Connects Our World and Shapes Our Future. The book was published by Public affairs in the US in June of 2025. Samuel is a scientist in residence at Lux Capital. In addition, he is an ex lab Senior Fellow at Case Western Reserve University's Weatherhead School of Management and a research Fellow at the Long Now Foundation. His writing has appeared in the New York Times, the Wall Street Journal, and the Atlantic, and he was previously a contributing writer for Wired. His previous books are Over Complicated Technology at the Limits of Comprehension and the Half Life of Facts why Everything We Know has an Expiration Date. He holds a PhD in Computational Biology from Cornell University and lives in Cleveland with his family. The first computer he used was a Commodore VIC 20. I selected the Magic of Code because it pulls the curtains behind what many might seem or view as a very scientific or mundane process, namely writing code and instructed computers. Sam shows that it is anything but, and he does a wonderful job of candidly bringing code to life and showing why all of us should consider coding beyond just the efficiency or the practical use of an exercise. Moreover, it's a fantastic book with wonderful literary references and deep insights into what makes us human and where the technology is going and how we should think about it. Hello, Sam, thank you for joining me today to discuss your book.
- [03:05]
Samuel Arbusman
Thank you so much. Wonderful to be chatting with you.
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Gregory McNiff
Great. Sam, why did you write the Magic of Code and who is the target reader?
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Samuel Arbusman
That's a great first question. And the way I would think about this is. I mean, certainly code and computation are interesting to me, but really. But when I think about the current conversation around computation technology more broadly, it feels somewhat broken. I feel like when we talk about these things, there's often kind of this. This adversarial perspective. We're adversarial towards it, we're worried about it. Sometimes we just kind of have a certain amount of ignorance around these kinds of things. And certainly an adversarial or concerned attitude is valid. But when I remember my own experience with computers growing up, it wasn't just full of worry, it was also full of wonder and delight. And it also didn't feel like computation was just this branch of engineering. It actually felt much more like this humanistic liberal art that in thinking about code and computation, you also connect it to language and philosophy and biology and art and, and the nature of how we think and even aspects of like, the fundamental nature of reality. And so for me, when I think about this much more broad approach to computing, as well as even just kind of the wonder and delight of the touchstones of my youth, of I. You mentioned the Commodore VIC 20, but also, like, early aspects of, like, experiences with, with programming or Fractals or SimCity, there was this, this sense of wonder and delight. And for me, that was really the point of the book. The point of the book is kind of, is to actually rekindle that sense of wonder and understanding and, and by doing so, hopefully provide a healthier way of, of how we actually approach computing, kind of connecting the human with the machine and then hopefully, in at least some small way, kind of repair society's broken relationship with technology. And so for me, like, that is a very tall task, very large task in terms of, like, what is the reader? And I would say, I mean, on one level there was actually a very, very simple target for the reader, which was when I was young and first getting interested in computing. There wasn't anything quite like this. And so I had to cobble together from lots of different sources, various aspects about how computing connected all these different areas. And so for me, in some ways this is almost a way of paying it forward to like future young Sams who are first getting interested in computing. But I would say more broadly, it's really anyone who is computing curious. It can be anyone who kind of realizes that, that there should be something more here for when we think about computing and code. And it can even be going all the way up to the experts, because I probably talk about so many different things that there's probably something for everyone. But I would say it's really for people who think that there's something there and what they really want to try to understand the nature of code and computation more broadly and as well as kind of situate it not just as this computer science thing, but more so as this, this broader field that really can connect to everything. And so if, if you have a loved one who is a software engineer kind of excited by these things and you really want to understand what's going on there, then this book could also be for you as well.
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Gregory McNiff
Yeah, that, not surprisingly, is a great introduction explanation of the book and one I think you accomplished very well. I mean, particular wonder of, I'll say coding, but really computation. And you touch on so many themes. Like you said, software to artificial life to evolution. And I should say, I think at some point in the book you disclose you were trained as a. I believe your first training was in biology. So it's f. How you tie that in. And oh, I wanted to mention, as you just said, you actually say, I wish I had this book when I was starting out, maybe as an undergrad or a high school student. So it is really a wonderful book. And I, as a classics major, study Greek and Latin. I really enjoyed it. I do have a nephew who is doing both computer science and classical languages, so this definitely helps me connect with him. And he actually named his dog Pearl, which until I ran, I assumed was just some reference to a. A piece of jewelry. But I now need to connect with him and figure out how far this went back. This, like you said, this wonder with computers. Jump a little forward. I do want to talk about the initial themes, but I just want to hit you with this quote from your book. About a third of the way through you write, computing is still the premier realm where the league of precise and powerful magic happens. Can you maybe expand on that and provide some context?
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Samuel Arbusman
Yeah, I mean, so when people talk about magic, whether it's kind of like the way people like in the ancient or medieval world, Think about magic, thought about magic or even just the way in which we have stories of fantasy around sorcery and, and, and, and spells. These often involved ideas around using text or words to kind of coerce and control the world around you. And in many ways this has been a desire that we have yearned for millennia to kind of have these powers. And the truth is probably, and these have been desires for a long time, but only in the past, I don't know, 75 odd years have that has that actually been a reality. Since the advent of the modern digital computer. Which means that in some ways this ability to use precision, precision language, precision words, precision binary code and then have it actually affect the world around us, that is what computers can do. This is, this is the nature of code. And I, and, and of course I am not the only one to have realized this. I, many people throughout the history of computing have realized this kind of equation between sorcery and magic and computation. But there's actually something to be said for, for taking it really seriously because I, I think oftentimes when we think about magic in the realm of technology, we're like, oh, it's like, it's magic, it just works. And there's something to be said there for like, oh yeah, like, like, like the iPhone is really powerful and has this kind of magical experience. And I think there's something powerful in discussing that. But for me, if you actually look at, rather than just saying magic as something that just works, but magic as this thing that we've wanted or these stories that we've told ourselves around with very particular features and then taking those, those features seriously and seeing the ways in which they map onto the realm of code. It turns out by take, by using this kind of metaphor and this analogy, you actually learn a whole bunch about how code itself operates. Now of course we don't necessarily want to take that analogy so far. That may bend it so far that it, that it breaks. But there is something to be said for taking serious. So for example, just on the, on the very simple level around education and craft, I mean magic is not like magic when we, in our stories, it's not necessarily just something that you kind of just pick up very easily. It actually requires a great deal of effort. So you have to spend seven years at Hogwarts or apprentice with, with some expert wizard or whatever it is. And the same kind of thing is true with, with code and learning how to program. Like programming takes work and like trying to understand all the details is, is a very specific type of, of. Of training. And then of course, I discuss other things as well. So the. Around the. The power of names. And I kind of equate it to how we think about variables. I also have certain things around, like symbolic manipulation and discuss it around sort of like kabbalistic manipulations and as well as in the coding world, things around regular expressions. But then even, of course, with AI and AI right now, it is built upon the consumption of huge amounts of creative works of human beings, of text and books and art. And in many ways, this parallels some of the things, some of the themes that we've seen in the world of magic, which is this idea of sacrifice, that something of value must be given up in order for the magic to work. And I feel like we have allowed all these artificially intelligent systems to operate only by virtue of sacrificing huge amounts of creative work. And this has actually led to a great deal of conversation and discussion and debate around whether or not this is worth it. I mean, do you. If we're just sacrificing these things to allow these AI systems to work, but we're not compensating the creatives, the people who actually built these kinds of things, the creators, then are we losing something? Is it not fair? And there's a whole discussion around this. And so for me, kind of taking this equation seriously, this idea that the realm of precise language is both the realm of code and the realm of. Of magic actually can lead you to a greater understanding of computing and technology. And so for me, I think that's actually a really powerful thing to think about.
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Gregory McNiff
Yeah, no, that's a really fascinating analogy, and I do want to drill down on some of that or most of that. One other just takeaway I had from your book is the references. There are a fair amount of pop culture references, very standard today. Just connect with your audience. Or, for example, Madonna, Return of the Superman and Super Mario. You definitely connected with me on those references, but candidly, there were a number of, I would call highbrow references as well. Charles Teller, the philosopher Aristophanes even had a comment about Electric Birds. I think if anyone knows, he wrote a play called the Birds. Leon Cass, Wendell Berry, a poet from Kentucky who tends to favor land and the human and the earthy, someone you might actually counterpose or set it on the opposite side of the table, or the notion of technology and advancement. Just wanted to ask you about these references and your background because it was unique to see so many. I think we talked before this interview about. You referenced a book by Turner on theology. Is this just from your normal course of reading or, I don't know, did you spend a year just sitting in some Renaissance library or something? How did you think about these references and why, why are there so many?
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Samuel Arbusman
Yeah, well, it's a great question and for me, I mean, and going back to like talking about like code as this kind of like humanistic liberal art, for me, like if you're kind of taking that kind of thing seriously, then it should connect to lots of different areas of our knowledge. And so I really kind of, I really wanted to try to, to convey that by connecting to lots of different things. That being said, and for me I, I like reading and consuming lots and lots of different things. And so for me it was, I would say, a combination like in terms of like the, the work for the book. It was a combination of directed effort and so actually trying to do lots of different research alongside just in my own experience, having come across things or coming across things in ways that I hadn't expected and tying them in, I would say, I mean for me, I, I, so one, and you mentioned earlier that my, that my background is originally in biology. I would say another area that I'm also steeped in is kind of this world of complexity science, kind of this idea that there are huge complex systems that we, that we are surrounded by, whether it could be sociological, biological, technological systems with huge numbers of interacting parts. But one area, one aspect of that is this idea of network science looking at kind of abstracting away the details and realizing that we are all embedded in networks. And for me, when I think about knowledge, knowledge is another one of these types of networks. And so I, I, I've always thought highly interdisciplinarily and I've never really liked kind of the artificial boundaries between different domains. And so for me, yeah, if I can, if I can connect various, yeah, like, like essayists and poets and ancient Greek philosophers and, and certain Talmudic texts or biblical references as well as certain things from modern music and, and video games. Then, then for me like, and all, all is the better. I kind of view this as right, like the true richness of, of human civilization. And so for me, I, yeah, I think there is a great power to be gained from connecting all the different things together. And you also mentioned, you mentioned the book by, by Turner, Philology. And he actually, he even discusses some of this kind of thing in terms of the nature of philology itself. And so the nature of philology, it was kind of this like proto field within the humanities where prior To, I don't know, archeology and history and linguistics and, and, and certain aspects of anthropology. There was this field of, of philology which was to kind of. To understand the history of words and their relationships, but in the process touched upon all those different fields. And of course, in many universities we don't really have philology anymore, at least within the United States. It's kind of fractured and has created all these different little specialties. And so for me, in the same way that maybe back in the day there were natural philosophers rather than people just studying physics or chemistry or biology or what have you, in the same way we have philologists who used to study all these different fields. I feel like computing as this liberal art has the potential to connect to lots of different things and by trying to, yeah, make these references from all over different things that hopefully would give people, give the reader kind of a flavor of really all the different ways in which it can be connected to, to the, to various aspects of the human experience.
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Gregory McNiff
Yeah, no, that is fascinating because, you know, as you mentioned and you, you have academic appointments as well and you realize probably more than anyone, it does seem that modern university is focused more on specialization than this idea of an integrated whole. And maybe it's a science versus humanities and maybe it's a stereotype. But yeah, it's interesting throughout the book you connect coding with the different aspects, I guess, of humanities, the arts, the sciences, cultures. So I didn't pick that reference up to now, but yeah, that's a very important theme. I do want to hit on this notion of theology and specifically language. What is the relationship between coding and our language? Is code another language? How do you think about that?
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Samuel Arbusman
Yeah, I mean, I certainly, I mean, code is a type of language and we have programming languages. There's a huge number of different programming languages, but at the same time, and we can discuss like the nature of these programming languages. But at the same time, though, programming languages are different than human natural languages. And so natural language is often messier and has a certain kind of expressiveness and of course, and code itself needs to do things and it needs to kind of be compiled or be run by a computer in, in, in a very specific way in which human natural language doesn't necessarily need to be, it needs to be maybe understood by another human being, but you're not necessarily running it in quite the same way. It doesn't necessarily have to do things. So, so for me. But, but that being said, there are. And even though these things are two, like they're two qualitatively. Different kinds of things. Going back to what I was saying, I the earlier analogy of language to code or magic to code and magic. There is actually a great deal of power in thinking about the analogy between natural language and programming languages because it turns out in the same way that natural languages have evolved over time and developed, you have the same kind of thing with programming languages. While programming languages are clearly designed, there is this clear evolutionary history between how different languages grew up over time and changed. And you also have sort of certain kinds of natural language that actually also have features of programming languages. So for example, if you look at the way in which biblical texts were written, so like, so ancient biblical Hebrew, it was probably quite different from like, in terms like the biblical Hebrew versus kind of the, the way in which other aspects of ancient Hebrew were being used. That there's kind of a certain spareness and repetitive of language or repetition of language maybe using far fewer wor. And, and if you look at programming languages there's a similar kind of thing where, and you have a relatively small number of keywords that are, that have to be used. There's repetition. So maybe when you're writing an if statement or whatever it is. And, and of course I don't want to say these things are identical, but you gain a certain amount of insight into looking at the language like the kind of, the linguistic stuff of programming itself. And so, and so for me, I mean, I also think about how we think about just the history of communicating with, with, with machines because that really is, is what, what code is and code has been. And to kind of give you a better sense of how we think about code and language and code itself is really this moving target. So back in the day you didn't even program using text. Like very, very early on. It was a very physical thing. It was like programming wires or cables into a machine and moving things around or flipping switches. And then at a certain point it was giving strings of binary of zeros and ones to kind of this, this, this very rudimentary machine language. And then people created these higher level languages that were then converted into binary. And so you had things around assembly which is maybe sort of a more human readable version of machine language. And now you have the languages like Fortran and C& Python and they're all different in certain ways. They all have certain similarities as well. Actually going back to kind of how we think about natural language, natural languages, while they are all fundamentally different in many interesting ways, they are all devoted to conveying certain ideas in your head to the reader or to the speaker, and they can all be translated from one to the other. You might lose certain nuances. And the same kind of thing is true with programming languages, which is above a certain level of complexity. Even though these. Some languages might be easier to understand or easier. Easier to express certain things or to write algorithms in, some might be much more difficult. They are all fundamentally interchangeable. And I think that fundamental feature of both natural language and programming languages is something that maybe a lot of people don't necessarily realize. And so, I mean, we can discuss more, but I would say that's kind of the way in which I think about how these things are similar and different. Of course, I mean, but ultimately the way I would kind of think about programming languages is they, they have the expressiveness and the potential for poetry and beauty of natural language, but also the mathematical precision and boringness of like a shopping list. And so it's a very weird kind of thing and I'm happy to kind of delve into that as more as well. But. But yeah, I, for me, there's a great deal of power in seeing the ways in which it is similar and different to natural language.
- [21:49]
TJ Watt / Scott Hanson / Liberty Mutual Spokesperson
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- [21:52]
Samuel Arbusman
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- [21:55]
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- [21:58]
Samuel Arbusman
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Gregory McNiff
That is fascinating and forgive me, I cannot remember if it's in your book, but I was reading something recently about Chomsky and his contributions to languages and how he was looking for the underlying structure for what I believe are 7,000 or over 7,000 different languages. And you almost hit on that. At least at the higher level. There is sort of this unifying structure between the languages. And I should say you have a nice discussion in the book. You said so many things. My head. I think I want to go back to the initial code because I do think you quote von Neumann, maybe it's Wheeler on how all you really need is a programming language of ones and zeros or binary. But you also do touch on language, which is fascinating because you have a discussion in the book about does language influence our thoughts or, you know, the language we're using does actually shape our thoughts. Could you love your thoughts on that? I mean, I know it's not the overriding theme of the book, but.
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Samuel Arbusman
Yeah. I mean. Yeah. Well, I need to be fair. The overarch, the overarching theme of the book is just these things are interesting and worth exploring. So I'm happy to explore anything you want. And so. Yeah. Yeah. And the way I would kind of. And so, so there's this hypothesis within linguistics known as the, the Sapir Whorf hypothesis. And so I, I think it's kind of made its way into popular culture probably through the the movie Arrival, where which is based on a Ted Chang short story, I believe, called the story of your life or it's kind of like a novella. And the, the idea behind this hypothesis is the idea that the language you use shapes the thoughts that you have and so there's kind of like. There's the very strong version, which is that certain language, certain kinds of thoughts are only thinkable in certain. In certain languages. And, like, if you so. So, like, certain kinds of language that you use, or maybe a certain language you use fundamentally has different sorts of ideas and thoughts within them than other kinds of languages. And it turns out this is. That very strong version is not really. Is not true that, I mean, you can translate between different languages and. And you might lose a certain amount of expressiveness or kind of some of the connotations. But, like, everything is fundamentally understand. So. So, like, the really, like, complex version of, like, oh, like, because we use language in a certain way, we think about the world in a certain way, and then if you had a different language, it would be fundamentally different, and almost like you couldn't really go between them, that kind of thing is not really true. That being said, there are, like, much softer and weaker versions of the Sapper Whorf hypothesis, which is the idea that. That at. At the edges, the language you use kind of shapes certain things. And so people have done research around whether or not the ways in which a certain language describes colors, does that affect the way in which you can process the colors that you see. So, for example, and actually going back to the popular culture reference. So I was. Back when I was in graduate school, I think I mentioned this story in the book. Back when I was in grad school, I went to see the movie Superman Returns with a friend of mine who grew up in Vietnam. And so after the movie, I was asking him, I said, what did you think of the movie? Because, I mean, he didn't really grow up with kind of like the Superman mythos in quite the same way. And he's like, oh, this is really interesting, but I was confused. What was the deal with the blue rock? I'm like, blue rock? Like, what are you talking about? Then I eventually realized he meant the green rock, kryptonite. And it turns out. And then after we were chatting about this, it turns out in Vietnamese, there's essentially one word for blue and green. I think, like, there's, like, a more formal way. You could say, I don't. Forgive me for not remembering the details, or I. I might butcher this. But I think, like, there's a more formal way where you would say, like, like the blue of the sky versus the blue of the trees or whatever to kind of, like, distinguish those different kinds of things. But when talking to my friend, what he was saying was essentially, I mean, he he obviously can see two different colors. Like, he, he know it's not like, oh, his brain can't process these colors, but when there are certain colors that are kind of at that boundary between blue and green, it will take him a little bit more time to figure out which word he needs to say in English or he might forget and, or confuse them. And so that was just kind of like a really interesting, weird little example. But of course we also have other kinds of things, which is just the idea that certain kinds of words create certain connotations. And so I gave the example where I think it was a, a company realized that they could sell more prunes if they simply marketed them as dried plums, even though they are obviously the exact same thing. Dried plums maybe don't sound as maybe like, maybe plums are kind of, or prunes are coated for like, just like, oh, senior citizens eat those or whatever. And. But dried plums, that sounds, that sounds very different. And so, and so there are at the very edges certain differences in how we process things. And, and one of the arguments I was making with, in terms of thinking about like natural language and how it's related to computational language is that there are that even though going back to what I was saying before, that like, fundamentally at like most programming languages, you can convert from one to the other. This is kind of, this idea of like what is known as a sort of like Turing completeness, that like, once you kind of achieve turn completeness, then every language language is interchangeable, but fundamentally different, different languages have different levels of ease of communicating those kinds of things. So yes, everything can technically be written in binary. Is it easy to do? Is it fun to do? No, it's, it could be terrible, at least for me, who did not grow up doing that kind of thing. And there are certain kinds of programming languages that are specifically designed often for writing different kinds of things, so allowing you to express certain types of algorithms more easily or achieving a certain amount of elegance. And so there are also, there are some languages that are very elegant and simple, but actually might be relatively hard to do complicated things in and vice versa. And so this is kind of one aspect in a way, the way in which, in programming languages, in the same way that language more broadly is kind of a tool. It's a tool for allowing us to think. And I imagine we're going to be talking about more about tools for thought later. But languages really do, at least programming languages do affect the ways in which we communicate our needs and our desires to machines and kind of instantiate those things in code. And so even though, and I mentioned before, like, that like, code is kind of. This moving target has always changed. Programmers will often try to choose a programming language that is kind of ideal for their, for their needs. That being said, I mean, there are some languages that are designed a little bit more to be agnostic. And so you mentioned before Perl. So Perl is one of these weird languages which on the surface it's actually, I haven't used it a lot very recently and even back in the day I didn't use it that much because it felt very messy. But in terms of the philosophy behind it, it actually, so the, the creator, he actually had a, he, he has a background in linguistics. And so he actually tried to take some of these ideas of languages and expressiveness and some of the kind of like, agnosticism around. Like. Oh, like, like just as English can be used to, to write a list, write a haiku, it can, it can do all the, you can, you can write a novel, you can, you can, you can write a very beautiful literary novel, you can write a really trashy novel. It can do all these different kinds of things. And his argument was programming languages should be the same thing. They shouldn't necessarily be good for just one thing. They should allow you to have a certain amount of choice and, and flexibility in what you use. And so, and he really tried to use that philosophy in constructing the language of Perl. And so for me, when I think about, yeah, like language and the naturalists and the expressiveness, um, yeah, certain ones are easier than others, but they all can kind of fundamentally do the same sorts of things, but at the edges they do affect what you, what you can do. And so, yeah, you often try to try to choose a language that fits the kinds of needs, needs and expressiveness or even the aesthetics that you want.
- [31:48]
Gregory McNiff
On that point, do you think coding language evolves?
- [31:52]
Samuel Arbusman
Uh, yes and no. I mean, so I would say it evolves in the sense that language, I mean, the programming languages we use have certainly changed over time. And even like within a certain language there are different versions. And so there would be. And so going back to Perl, like, they might have like, added keywords and things over time or with Python or other languages, but these are not very natural and organic and they sometimes are in the sense that people start, people realize a need and they add things, but it's, there's still an element of design in the way that within biology there's not. Or even within linguistic evolution there's not always quite the same way where it's not. And sometimes we, we have like conscious neologisms and new words and, and new, new things. But, but in terms of maybe how we like what, what is linguistically acceptable and what is not, that probably happens a little bit more informally. So, for example, I remember talking to my grandfather years ago and I, I must have used the phrase, oh, this, this thing is very fun. And he looked at me weird. He's like, you can't say that. You can't say very fun. And it turns out that over the course of several decades, whether or not you used fun and very kind of in this, in this combination, it changed. And he grew up and learned language in one, in one milieu and I learned it in a different one. And so, and so things change and, but that one, though, there was not any sort of conscious change as far as I'm aware. While with programming languages oftentimes there needs to be a certain amount of deliberateness in how things are evolved. That being said, the way in which we think about evolutionary change is kind of these, like evolutionary trees and the phylogenetics and like that. There is a similar kind of thing that can be thought of within programming languages where every programming language, by and large, I mean, I guess Fortran was sort of the first higher level one, but, but most languages are built upon ones that come before them or certain ideas that come before them. But unlike in biology where you might, where things only kind of have like one or two parents, you like whether or not you're a cell or kind of higher life form, in computer language evolution you can have many, many parents. You can have a language that, that is inspired by many different ideas. And going back to Perl, and Perl has in some ways many, many direct or indirect parents where they're. It kind of drew almost like, like magpie, like from lots of different languages of like, oh, here's all these different ideas. And so, so I love that, that messiness of language, but there is still a certain amount of deliberateness as well.
- [34:25]
Gregory McNiff
I should mention the book is divided into three sections, Code, Thought and Reality. And in the first section you do spend some time on tradition. And I think we exchanged emails. I was actually reminded of the movie the Paper Chase. I'm going back, I think 50 years. The young law student manages to read the books of his professor who has a quote saying, it's hard being the living extension of tradition. And this is one thing that shocked me in your book because I think we all think of technology as a Destructive innovation, moving forward and breaking the mold for something better. But you suggest, and you've just referenced this in your talk about evolution, is there a tradition to coding? I mean, do coders look back and are they conscious of what came before?
- [35:09]
Samuel Arbusman
So I think, I mean, sometimes they're conscious, sometimes they're conscious, sometimes they're not. But I would say, I mean that history and that tradition within computing and even technology more broadly, it is very important and well worth understanding. And so. Right. Like when I, when I look at like the world of Silicon Valley and kind of the, the current tech world, it feels like very disruptive and very ignorant or unbothered by the history of what has come before it. And sometimes proudly so of like, oh, like we don't have to care what, like what, what came before us. Only the new is the thing that really matters. And for me, I feel that when we, when we ignore what has come before us, kind of that tradition and that history, we lose a great deal. And not only do we lose a great deal, it often can hamstring our ability to actually build new things going forward. And so for me, I, um, when I think about technology, like trying to understand, like the path dependence of what has come before us, realizing that maybe no new idea is actually truly new, these kinds of things are very valuable. And when it comes to computing itself and like computing and computation and code, I think recognizing this kind of thing is actually very, very important. So for me, so, so one of the, one of the fundamental ideas within computer science and computation is this idea of abstraction, this idea that you can build sophisticated, like computational systems or pieces of, like modules of software that then can then be incorporated by you or by other people into successive bits of software. And the idea behind abstraction is you can kind of, you have the, the interface for this piece of code, but you don't have to necessarily worry about what is inside it and how it is doing the thing it's doing. You can kind of abstract away those details. And this truly is, is the history of how software and computer programming is done, is that there is a great deal of power on building upon what has come before you. So if I have to write a computer program, I don't necessarily have to reinvent the way in which numbers are stored within my computer program or within the machine. I don't necessarily have to worry about the operations of addition and subtraction and multiplication. All these kinds of things have by and large been been done before me. And so I don't necessarily have to worry about, I Don't know how to draw text or images onto a screen because I can take those libraries off the shelf. And so for me, when I think about the process of code and comp and programming, it really is this process of accretion and building things upon each other. And not even just building things for yourself, especially once we have kind of the realm of open source where people build things that can then be incorporated by other people or you can add and change those things yourself, even if you're not necessarily one of the creators. Means that we have this huge like repository like library of wisdom and technologies and applications and abilities that, that we don't have to start from scratch. And this is, and, and in many ways this parallels the way we think about human knowledge and, and, and, and science and things like that and all these ideas is that we are not starting from scratch. Whenever we try to make a new scientific discovery or think about the world, we, we are standing upon the shoulders of giants and we are, we are using this repository that has come before us. And the same kind of thing is true in technology specifically. I mean in this. And we think about creativity. Creativity is come is this constant process of recombination in novel ways. And that is how technological innovation occurs. That is how we write software. So really trying to understand the tradition that's come before us I think is, is really valuable in terms of allowing us to actually let flow the true full force of creative power.
- [38:56]
Gregory McNiff
You sort of anticipated my next question, namely the role of randomness encoding. And I want to take two short quotes here. You talk about introducing simple actions leading to complexity and wonder. Quote a spectacular tapestry must be revealed. And then in your discussion of open source you say basic features are available for quote recombination. Unexpected uses will be gained. How important is randomness encoding and can it lead to these unusual or unexpected but surprising outcomes?
- [39:29]
Samuel Arbusman
I think so. Randomness is something interesting actually in terms of how it is encapsulated within computing, which is no process within kind of a traditional machine is actually ever truly random. And so, and maybe you can have like cosmic rays hitting, hitting your machine and flipping bits, you can't really rely on that kind of thing for generating randomness. And so that being said, randomness what while we might normally say oh, like the fact that a machine does not is not subject to randomness and doesn't have it, that's actually a really good thing. Randomness itself is actually a powerful feature of understanding our world. So if we're trying to understand whether or not some feature of the world is due to random chance or like statistically unlikely, or is actually something that maybe is something that we want to delve more deeply into. You need to be able to compare that to random chance. It turns out code making and code breaking actually has certain needs for randomness. They're like when you try to generate simulations of complex features of the world that also requires randomness. And so the question becomes, okay, how do we actually make randomness within a computer? And it turns out there's this process of pseudo randomness where you actually generate, you use very, very complex algorithms alongside kind of like seeding these with, I don't know, the current time in the machine or whatever, or maybe the, like the amount of time between keystrokes as you're typing something and using that seed and then doing kind of this complicated mathematical algorithm then will yield numbers which are effectively random or kind of have the features of randomness, and then you can use them to actually understand the complex features of the world around us. And so for me, I find that really, really interesting. I also think, and you also talked about this idea of using kind of like, like repeatable simple actions kind of leading to complexity and wonder. These simple actions combined with a certain amount of randomness, or even just pseudo randomness can lead to a huge amount of unfurling complexity within, within a machine. And so the, the idea behind this is I refer to these as emergent microcosms, where it's like, in the same way that like, like you think that a just doing actions over and over, maybe with a little bit of randomness, it should be relatively intuitive to understand what's going on there. It turns out that if you do simple actions and simple mathematics with fractals and things like that, often enough you actually get these kind of emergent, very complex and sophisticated behaviors that are often very difficult to anticipate based on the simple, the simple, I don't know, calculations that a machine does. And that's kind of the, the secret sauce of computing this. I think Richard Feynman has this quote of like, a computer is like, it's like, it's very simple, but it's fast as hell or something like that. And, and we forget that very simple operations, when done often enough, they add up to really complex outcomes. And of course, we see this kind of thing now with like artificially intelligent systems where you have a huge number of artificial neurons, these kind of these neural nets that when you pour lots and lots of data into them, and it just kind of goes through over and over and over, trying to recognize associations and, and train and train. You end up getting really, really sophisticated behaviors. But you can see the same kind of thing in everything as simple as computationally generated art. The way in which screensavers do their thing, the way in which fractals can be unfurled within computers. And some of these might use randomness, some, some do not. But there is a great deal of power in recognizing that that fundamental feature of the computer, which is it's, it's sheer relentlessness, which allows us to generate randomness within, within computers, to harness that randomness to actually simulate the world around us as well as to just generate these, these massive, these microcosms within the machine that are incredibly complex and rich.
- [43:26]
Gregory McNiff
In this discussion of recombination and maybe randomness, I think, think you say something along the lines of the Open source is very powerful. Could you, could you expand on why you think that?
- [43:37]
Samuel Arbusman
Yeah, I mean, so, I mean, I kind of hinted at it a little bit before, which is this idea of when you have a huge numbers, a huge number of raw material or kind of components that you can then recombine in novel ways, then, then like that, that's sort of the, the raw material for, for creativity and innovation. And, and Open Source is very powerful because it creates more and more of these raw materials. And so like, the idea behind like Open Source software is that you don't just necessarily have the, the piece of software itself kind of the end product when you compile it, but you actually have the raw material, you have the code itself that can then be taken and modified and incorporated to something larger and used for various other, various, various use cases. And for me, the, the power of recombination is often recombination in A4 uses that the creator can often not anticipate. And that's in a nutshell, one of the, one of the features of Open Source which is, and when you have something and the creators or the maintainers then just kind of give it away and allow people to incorporate in ways that they might not have been able to anticipate, then you can actually have this massive flywheel of innovative recombination. And so, so for me, Open Source is really kind of just taking these ideas of recombination and combining things together in novel ways and really just putting it into overdrive and kind of saying, okay, like these, like in the same way that nothing is built upon, like nothing is built from scratch, we are now going to open up everything and allow everything to be reused and modified. And changed in order to make that innovation just even more possible, whether in software or if we're talking at a very high level, that's kind of just the way innovation operates more broadly.
- [45:34]
Gregory McNiff
Fascinating. I want to move to the second part of the book thought. Can code have an esthetic appeal?
- [45:40]
Samuel Arbusman
I think, yes. I mentioned before that there are similarities and differences between programming languages and natural languages. But one of the things that I think is similar, although maybe it might look a little bit different than certain kinds of poetry, is that when you talk to programmers, there are certain types of code that can have, that have an aesthetics. Like there could be spaghetti code where just things are all recombined, they're all connected in weird ways, and there's no real clear design, and that often feels very ugly. But then there's other types of algorithms, types of code or programs that people have written that are beautiful, they're very elegant, they're able to do a huge number of things in a very, very simple way. And some of this might be in the eye of the beholder, and I imagine some of this might also require a certain amount of training to be able to identify those kinds of aesthetics. But in the same way that literary critics might spend many, many years trying to hone their aesthetic sense and trying to understand why certain texts are more beautiful than others, there is a similar kind of thing with code where you. Yeah, you can very easily, not very easily, but like, there are differences and there are these aesthetics. Now, of course there are differences and code has to still kind of do something as opposed to just, maybe just convey meaning. It has to operate. And so it might not necessarily be able to be as elegant as you would like. It might have to still just contend with the messiness of the real world and edge cases and, and, and exceptions and things like that. But, but I, I do think that programming and code can have an aesthetics even within a single language, but it's certainly across different languages. Like, different languages often have different feels and, and they resonate with different programmers in different sorts of ways.
- [47:24]
Gregory McNiff
Well, why do you describe Lisp as God's own programming language?
- [47:29]
Samuel Arbusman
So, yeah, I was. Yeah, so to be clear. Yeah, that, that, that's not my, that's not my description. It's a quote from someone else. But Lisp is one of these very interesting languages that it, it's, it has a very small number of, like, primitives that. But going back to, like, I mentioned this idea of like, Turing completeness, that, that once you achieve a certain amount of features within a Primary language. They are all basically like, then every language is equivalent. They're kind of considered to be turn complete. And Lisp does this. So Lisp is a, I think it's a. It's an abbreviation for list processing. So it just, it, it basically every, every program, every function, every, every bit of information, they're all just a series of lists and it's just lists operating on lists and so on the one. So it's very, it feels like almost like mathematically obvious. And so, so in that sense, I would say that's probably the reason why it's been described as like God's own programming language. Like, it's just so very simple. The downside is sometimes though, to get it to do anything very powerful is sometimes difficult and requires huge amounts of like nested parenthetical statements. And I have it and well, I don't know if I've ever used Lisp proper. I've used kind of like variants of Lisp so known as. So one of them is Scheme, which is sort of like a simplified version that's often used for teaching certain types of programming. And it's very elegant, it's very powerful. It also. I remember having grown up learning different programming languages which are kind of like much more imperative or iterative. Lisp, which is called a functional language where it's like functions operating on functions. It has a very different feel. And like the moment my. And, and my. I initially struggled to understand it and when I finally did, it was like, it was like a light going off in my mind where I finally understood it and I realized its elegance and it was very powerful. Do I use Lisp or Lisp variants for programming in every day? No. Do I realize there is a power to some of these functional features? Yes. And so I almost feel like in some ways Lisp, at least for me, and I'm sure many people will disagree, like Lisp is almost like. It's almost like too elegant to kind of get anything done in the real messy world. But I find it's kind of spare beauty very impressive. If you thought goldenly breaded McDonald's chicken couldn't get more golden, think golden because new sweet and smoky special edition gold sauce is here made for your chicken favorites at participate in McDonald's for a limited time.
- [49:58]
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- [50:26]
Samuel Arbusman
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- [50:55]
Gregory McNiff
See mint mobile.com earlier in our conversation. In the book you talk about this idea of compressing richness and complexity into a simple code or a simple mathematical formula. Why is this sort of low energy, high expressiveness ratio or approach quote, the holy grail of languages?
- [51:15]
Samuel Arbusman
Yeah, and because I think this idea is that, I mean fundamentally programmers are a little bit lazy. They want, and one of the reasons you want to, you want to program is you want to be able to use the machine to do your work, for you to actually kind of solve problems and do interesting things, things that you would not otherwise be able to do. And the more you can be lazy, the more you can actually do a lot in a very small number of lines of code, the better. And so in that way, I think people have always tried to, yeah, aim towards this kind of holy grail of like, like things where I don't, I don't have to require a lot of work, but it can do so much. It can express exactly the kinds of things I want. And, and so, so there is, there's a fundamental nature around like the power and expressiveness of a programming language. But I also think there's kind of the practical aspect of like, yeah, programmers are, are kind of lazy and they want to just be able to do a lot with a little.
- [52:05]
Gregory McNiff
Yeah, should everyone learn to code?
- [52:09]
Samuel Arbusman
So I'm of multiple minds about this and I think, well mentioning before about like how coding is this moving target, I think coding is changing yet again now with AI generated code and people talking about vibe coding and the like where you can kind of ask a machine to generate code for you and you can actually just generate software very powerfully and very easily. And so in some ways Maybe we don't necessarily have to learn how to code the way I learned. And so going back to along this idea of like is encoding is a moving target. Like, do I want people to learn how to program in binary? Binary. I never learned that. I would never want people to do that kind of thing or in assembly language or whatever it is. That being said, I do think understanding how to code or understanding programming languages and programming is actually powerful for a number of reasons. One, is it actually this, like the comp. Like the way in which you think when you are thinking computationally is a very different sort of thing that you might not otherwise learn. And so just from a sort of like liberal art perspective, it actually is a really good thing to learn if you want to just expand the way in which your brain operates. On a practical level, I also think it is very useful because as we are more easily generating code through AI, what is even more powerful than generating code automatically and just taking it as a given is actually knowing how to work in concert or kind of in partnership with these machines. So like, they generate code, you dive into it, you modify it a little bit, you say, no, this is actually not what I'm thinking about. Also, knowing how to code actually gives you a much better sense of the kinds of questions and the kind of requests that you can make of these AI tools. So for me, when it comes to if you want to be able to generate software for yourself, I actually think it's a really powerful thing. That being said, you want to, you want to be able to make it, make it easy and powerful. And then there's also just kind of the idea of computational thinking, but I would say kind of related to kind of everyone learning how to code. There is this, this trend around democratization of code. And so I mentioned before that when we talked about magic and code that like a lot of magic and sorcery required great, like many years of effort to learn. But there were also traditions of magic that were for kind of the everyday user of like, here's a spell to help a farmer find some lost cattle or whatever it is. And the same kind of thing has actually also been a goal of programming, where programming should not just be the domain of the software developer, it should also be like the everyday user of like, if. And how much more powerful would a computer be? If you can kind of just look outside in the world and say, oh, like here are various use cases and I would love to write programs and, and if you know how to program, that's great, but we should also kind of lower that barrier. So I think AI is kind of doing that. But there have been actually a number of tools throughout computing history designed to kind of democratize that, that kind of code and sort of lower the barrier. And so I think for me, that kind of thing that's kind of like in between kind of thing, or maybe on ramp is actually really, really powerful, and we need more of that.
- [55:23]
Gregory McNiff
You spent some time on HyperCard. It's what was so magical about it.
- [55:28]
Samuel Arbusman
Yeah, so, so HyperCard is exactly one of these examples of sort of like a, a tool for democratizing programming. So HyperCard was a piece of software. It was on the Macintosh, I think, beginning the late 80s through sometime in the 90s. And the, the way kind of the, the aesthetic of it or like the conceit was that you were generating stacks of cards, of virtual cards. And so kind of the way to think about it is maybe you're kind of generating a series of web pages for an entire website, but they're all kind of local and living on your machine. And so you could just very easily generate cards where you type in text or you, you draw on them, but you could also very easily drag and drop buttons onto them and then make the buttons do things like make a noise or connect or, or when you click on one button, it would take you to another card. And then in addition to that, though, under the hood, there was an entire language called HyperTalk. And HyperCard was this really interesting tool that it was what. So there's a phrase attributed to the computer. The computer scientist Seymour Papert, who was involved in the, the creation of the programming language logo of this idea of like low floors. And high, low floors would be like software that's very easy to start using. But high ceiling means it has a huge amount of expressiveness and power. And HyperCard was exactly that. It was very easy to kind of just get started and fool around, but then under the hood, you could start actually building real pieces of software. And it turns out a lot of really powerful software was built with HyperCard. And so people would make databases. But I think the, the first version of the incredibly popular computer game Myst was first developed in HyperCard. And, and so it was one of these things for me that was, that was my, that was my on ramp into code. It was what allowed me to actually first try to understand how to actually build computer programs. And so that was why. And certainly one of the aspects of, of its great deal of power and magic in ability to create that combine that low Floor and high ceiling kind of feel.
- [57:32]
Gregory McNiff
Before we move to part three, reality, could you briefly describe the Tonawanda Cardex I think all of us would benefit from?
- [57:41]
Samuel Arbusman
Sure, yeah. So, yeah, so the Tana Wanda Cardex was, as far as I'm aware, I think it's like it was the shortest lived NFL team. They played in the NFL, I think, for one game, and then, if I recall correctly, lost horribly. And so. So Tana Wanda is a town kind of in the Buffalo area. I actually grew up, I grew up in Buffalo and it's in the Buffalo metro. And it's named after Cardex, which was this weird. I think, I think it's still around in some sort of instantiation in some incarnation of its company. But they made a certain type of index card. And so for me, one of the interesting things there in terms of like at the time, well, one, it was just, it's. It's weird that a football team was basically named after a type of index card. But for me, one of the, one of the kind of interesting things I found about it is that index cards were in its time, kind of this very powerful technology. And we think of technology as like gadgets and phones and computers and things like that. But, but there are many different types of technologies. And like a pencil is a technology, the Alphabet could be considered a technology, and the index card is a technology. And it was a very powerful technology for aiding people in organizing information and compiling information, searching for things. It was kind of a tool for thought. And so I kind of used the Tanawanda card X and the, and the index card more broadly as a way of thinking about tools for thought, which of course now the computer itself is a very powerful tool for thought and aiding us in organizing information, searching across bits of information, combining different ideas together and really being what Steve Jobs refers to as a bicycle for the mind, kind of allowing us to get to the places we want to go, just much more faster and much more easily. But in this case, it refers to the way in which we think about concepts and ideas and, and, and the work of thought.
- [59:29]
Gregory McNiff
Okay, moving to this third part, reality. And you really, you really hit some major themes and connections. We talked about evolution, artificial life, biology, and I want to go deeper into those topics. But could you talk about simulation? And is this something we should encourage? Um, you reference Forbis, I'm sorry, a professor from Northwestern, I think Ken Forbis, his paper on computer modeling. Should it become a popular hobby?
- [59:58]
Samuel Arbusman
Yeah, and so, and so simulation and modeling, it is very, very powerful and I think it is really. I mean, it's this desire that we've had for many, many years throughout human history of whether it's making physical models, whether it's trying to encapsulate certain ideas we have about the world around us and like physics equations and mathematics and things like that. And really when we finally had the digital computer, it combined some of that. It was, it kind of made, it allowed us to have dynamic simulations and models of the world around us that could then act, but, but ones that could actually be described by language, by text, by computer programs. And this ability to simulate the world and model the world is something very, very powerful because it's often a precursor to understanding the world. But for me, one of the things we always often have to think about is what is, what is the goal in simulating and making a model? Is it prediction? Well, then if it's prediction, then maybe we need to have really, really complex models, like the, like weather models that are used for predicting the weather. But if it's for understanding a certain aspect of the world, then they often don't need to be as complex and often increased levels of complexity actually make things messier and lead us to reduced understanding. And so for me, trying to understand the reasons and purposes behind why we have a simulation is something very, very important. And so, and you mentioned this, the paper about why computer modeling should become a popular hobby. I actually, I love this idea. I love the idea of kind of like taking very complex models of the world or complex aspects of the world and trying to actually instantiate them in computer code. Oftentimes they are going to be necessarily simplified. So like for example, in the computer game SimCity, is it actually describing real cities? No, these are necessarily massive simplifications. Can they teach us a little bit about how cities work? Yeah. Can they teach us even more about the nonlinear nature of complex systems and the ways in which small changes might lead to unexpected consequences and the ways in which we need to like, think, think about, like, have better intuitions around how systems bite back. Yeah, SimCity is really good at that kind of thing. And so for me, the more and more we can create sometimes oversimplified models, but even just models that allow us to kind of simulate the world around us. I think it actually can give us better understanding and sometimes prediction of the massive complexity of the world.
- [62:27]
Gregory McNiff
You mentioned you were trained as a biologist, and I may have mentioned that for me, the most fascinating part of a very fascinating book is chapters 10 and 11 Bits in biology and Ghosts in The machine in which you write, quote, the weirdness of biology is causing us to rethink how weird computing can be, broadening our sense of what computation is. Could you talk about that?
- [62:48]
Samuel Arbusman
Yeah. And so when, when people think about biology, there's a number, there's many, many different ways in which people think about the complexity of biology. But I would say kind of two modes of biology are focusing on kind of the mess and the information. And so like the informational view is this idea that like, oh, like biology fundamentally is very rule oriented and it's based on DNA that is kind of converted to rna, which in turn is created, creates proteins and the proteins can kind of do things, sometimes they're structures, but we have enzymes and they all operate in this kind of very intricate, almost informational mathematical dance. And that is a really powerful way of thinking about biology. And so you have like bioinformatics, like the information of biology and these kinds of things. But then on the other side you have the mess, the messiness of biology. I mean we are just kind of like, I don't squelching leaky creatures and, and trees live in dirt and like there's just kind of a great deal of mess. And then, then you zoom down even to the level of the cell. While I mentioned kind of the informational aspects of the cell and kind of like DNA and RNA and things like that, it's also incredibly messy going back to randomness. You know, it's not just like, oh, this molecule is doing this thing and we can, we can draw these nice little charts. It's just a whole bunch of stuff vibrating and they're all vibrating so quickly that things get done and things kind of become like statistical regularities, but it's really, really complex and messy. And so for me, while I think there is a great deal of power in noticing the way in which biology is computational, there is even more power in saying, wait a second, biology is processing information and so are, it's kind of traditional computing, but biology is doing it in a profoundly weird and non traditional way. And so biology should actually teach us more about the fact that information processing and computation as a thing is actually much, much bigger. So biology is kind of this like weird subset that is almost in some ways entirely different from the subset of computation that kind of traditional computing, like the laptop that I use is. And, and so for me, the more we delve into biology, the more we can actually learn about the true breadth and space and, and, and weirdness of what computing can actually incorporate. And, and then People, of course, as they learn more about biology, then begin to say, okay, can we actually harness it for computation? Or can we actually potentially even learn from it and build computers that might operate on entirely different frameworks that are maybe more biological and very, very different? And so, yeah, so ultimately, where I think taking the weirdness, the messiness, the messiness combined with the informational components of biology, taking these very, very seriously, allows us to broaden how we think about computation itself.
- [65:49]
Gregory McNiff
At one point, you refer to evolution as an algorithm. Would that make the universe one large code or calculation?
- [65:57]
Samuel Arbusman
So, I mean, there are people who have actually talked about this idea that really, that. I mean, the reality itself is really just this massive calculation. I mean, there's kind of. There's a, A simple version of this, which is oftentimes physics and reality itself are doing things that can be explained by calculations. And oftentimes they're doing things more efficiently than we might be able to calculate. And so we can kind of harness various aspects of. I like a river flowing to the sea to use that as some sort of optimization process or whatever it is. Whether or not, like reality itself is some massive calculation. I, for me, I think it's fun to think about. I'm not really sure how much we can gain from this idea as being just this massive calculation. Maybe it is. Maybe it's some, like, massive cellular automaton kind of just doing its thing and every little aspect, like, tiny bit of reality is kind of calculating based on its neighbors or whatever it is. I, I would view this as maybe a provocative and thoughtful analogy that maybe can allow us to think more interestingly about the world rather than viewing it as, yeah, it is just kind of this massive calculation, but it is. I, But I think it is very provocative and intriguing.
- [67:09]
Gregory McNiff
At the end of the book, you sort of, I guess you lay out what you believe is the correct relationship between coding and humans. And you cite this sort of quote, technically sweet example of Oppenheimer leading to the atomic bomb. And correct me if I have that wrong, because it's a little dramatic, but how do you think about the relationship between humans and coding? Are they growing together as one, a tool for the other? How do you think about that?
- [67:36]
Samuel Arbusman
I mean, certainly, I mean, technology and computation is affecting increasing aspects of our lives. I mean, like, to kind of say, like, I want to step back and not have computing be any aspect of my life is very, very difficult, and I imagine in many ways would be entirely futile. Just because it's touching, everything that being said, just because we can keep on making more and more powerful computational systems and technologies doesn't necessarily mean we should. This is going back to like that technically sweet thing, like just, just because we can do it doesn't necessarily mean we have to. And so for me, it always comes back to, comes back to this idea of recognizing that computers are tools. They are fundamentally tools made for humans in the same way that like we talk about, we talked about like computational tools for thought. And computers are ultimately, they are to allow us to be better versions of ourselves. So rather than saying, oh, this machine makes me do certain things, let's think about, okay, what is like, what would be the best version of myself and how do I allow computers to actually make me that best version of myself? And so, and really, ultimately, yeah, computing is meant for humans, not the other way around. And so we have to really focus on that very, very deliberately. So there's this fantastic computer show called Halt and Catch Fire. It was, came out maybe a decade or so ago and it's about kind of the early, like personal computing industry. And so it's very, it's a really good show. And very early on, possibly even in the first episode, someone's talking about a computer and says, like, the computer's not the thing, it's the thing that gets you to the thing. And I love that because for too too often technologists and people with computers, like, they're just, it's very easy to get wrapped up in the beauty and, and the delight of computers for their own sake. And I, and I do think there's a lot of wonder and delight in computing. But we also, but we have to recognize that these are meant to, to make us better versions of ourselves. They are meant for people to do like, they're the thing that gets us to the thing. They're the thing that we want to do. And as long as we keep that in mind, as opposed to kind of just taking these paths of last of sort of least resistance, hopefully we'll actually be, yeah, be building these things the way we want and the, the way we want society, like kind of in service of the kind of society we want as opposed to just sort of slouching towards these things that are not nearly as satisfying and make us fractions of ourselves.
- [70:05]
Gregory McNiff
I have to say, for a computer science professor, you make a great humanities or philosophy professor. Last question, just your favorite programming language and why.
- [70:15]
Samuel Arbusman
Oh, that's a fun one. I'm not. I mean, the programming language I use, I've historically used the most probably is Python, though. I have to say one of, one of the more fun languages or and maybe this is. It's more of like it's a cluster of languages. So there's a programming language called Processing which is designed for making it very easy to kind of do interesting visual experiments. And it's been adopted by a lot of artists, although I actually use it in my, my undergraduate thesis which for computer science. But and since then like the Processing was based on Java, but now that it's based on a number of different language and there's all these different variants, it's really, really fun and you can just make these unbelievably delightful like visualizations and almost like kind of screensaver kind of things are kind of. It's very good for what is known as creative coding where you can kind of just make like computer programs that themselves like the software is almost a work of art, like it generates a work of art. And so I would say yeah, Python is kind of my workhorse language. But the variants of Processing are also just so wonderful and lots of fun.
- [71:20]
Gregory McNiff
Fantastic. That concludes our interview. Again, the book is the Magic of How Digital Language Created and Connects Our World and Shapes Our Future by Samuel Arbusman. Sam, thank you so much for your time and writing such a thought provoking and enjoyable book.
- [71:35]
Samuel Arbusman
Thank you so much. This is a lot of fun to chat with you about all this.
- [71:39]
Gregory McNiff
Likewise.
- [71:45]
Samuel Arbusman
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Samuel Arbusman
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Samuel Arbusman
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