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Peter Bauman: Hello everyone, and welcome to this Le Random discussion about Chapter 9 of our generative art Timeline, which covers the decade of the 2010s. I'm your host, Peter Bauman, or Monk Antony, the Editor-in-Chief at Le Random, and joining us today are an incredible list of artists who created this art history that we're going to be discussing today. They are Tyler Hobbs, Helena Sarin, Rhea Myers and Gene Kogan. Also joining us from the Le Random team are our co-founder, thefunnyguys, and our collection lead, Conrad House. So today we're looking at digital expression in the 2010s.
We're taking a detailed look, and this is all part of the project I've been working on at Le Random, creating this generative art Timeline for the last about year and a half. So it's already at about 100,000 words and 1,000 moments, and we are just releasing Chapter 9 of the Timeline, which covers the 2010s.
We're going to start with a little bit of a sneak peek into 70,000 years ago in Chapter 1, the pre-modern era. We've had a talk for every chapter except for Chapter 6. We had two. So this is our 10th talk. On Chapter 9, it covers, broadly speaking, two enormous shifts in technology and art, and they are the rise of AI and its capabilities in the decade and also the environmental impact on the invention of NFTs. Blockchains started the decade before, and they really took off the decade after, but we are going to look at how this decade was instrumental in their development and, of course, the development of NFTs and the proto-NFTs that led to the tokens that we know of today. And on top of these huge AI developments like convolutional neural networks and GANs, we're going to talk about the…
And the first one is Tyler Hobbs. Tyler is a visual artist who works primarily with algorithms, plotters and paint. He began his generative practice in 2014, so 10 years ago, and has been of course active in the space since then. His artwork focuses on computational aesthetics, and we did a whole interview on algorithmic aesthetics that we put out last year. How they are shaped by the biases of modern computer hardware and software, and how they relate to and interact with the natural world around us. His practice involves programming custom algorithms that are used to generate serial visual imagery. And like I said, Tyler got his practice started more than 10 years ago. So really interesting to chat with him about that first part of his career that covered a lot, and especially the theoretical foundation of his practice.
And our next guest is Helena Sarin. She is a visual artist and software engineer who has worked with cutting edge technologies at places like Bell Labs and as an independent consultant developing computer vision software using deep learning. While primarily in tech, she has also explored applied arts like fashion, food styling and photography. Her artwork remained mostly analog until she discovered GANs in the second part of the decade that we're discussing today. And these afforded her a new model of creation, of employing her data sets and receiving inspired and surprising, unpredictable results. These days she calls herself an engineering artist, and her main interest is generative pottery.
Our next guest is Rhea Myers, who is an artist, hacker and writer whose work places technology and culture in mutual interrogation, to produce new ways of seeing the world as it unfolds around us. She began investigating blockchain as early as 2011 with a blog post striving to be the first Bitcoin artist. And in 2014 she started making conceptual blockchain art in the vein of Marcel Duchamp, and crafting theory as well as narrative.
And our next guest is the incredible Gene Kogan. Gene is an artist and programmer and educator exploring autonomous systems, emergence, collective intelligence, generative art and computer science. He is interested in advancing scientific literacy through open source toolkits for creatives that stress creativity and play. This includes building educational spaces which are as open and inclusive as possible. Gene was one of the most influential figures in the decade in terms of reimagining machine learning for creative expression. Beyond demonstrating the power of the new tools himself through his work, he was instrumental in teaching the world about these tools.
So what an incredible lineup of people and artists and thinkers to talk to today. Our first set of questions are more for the group. So let's get started.
What do you think the name for this decade should be? Just to start out on a very basic level. The '80s, I named that chapter the PC Era. The '90s I called the Net Era. The 2000s I called the Tooling Era, for Processing and openFrameworks and a lot of those instrumental tools. But what would best describe this era? Do you think it should be the AI Era? Do you think it should be the On-Chain Era? Is there something else?
Rhea Myers: I'd call it the GPU Era, because the things that people noticed as the decade went on were the things that were using GPUs, whether it's blockchain for hashing or AI for neural net simulation. So if we view it in terms of technology, and that obviously absolutely was the PC Era, it's tempting to call the 2010s the Cloud Era, because that's what scaled all of the apps that created the society we now have. But in terms of the focus of the art, it's very much on what you can misuse GPUs for.
Tyler Hobbs: Yeah, for me, a couple of things that really stood out as big changes in that time period are social media, in particular for visual artwork, generative artwork. I'd say Instagram and Reddit were the two biggest changes for me. They were very influential for me in terms of what art I was exposed to, in terms of how I was sharing my art and eventually how I started to make my living from artwork. And I think digital artists very natively adopted these formats and locations. And so I think those had a massive, massive effect.
Peter Bauman: So potentially there's an argument that you could call it a social network era.
Gene Kogan: I'd maybe concur on social media. It feels like it really just impacted, also for me, just how I got exposed to things. So for machine learning and AI before that, it was really you had to be kind of part of an institution. Maybe the de-institutionalization is also something I would kind of associate with it. So suddenly there was this AI Twitter that sprang up in the 2010s, and it was possible to be not affiliated with any specific research institution or grad school and keep up with what was going on. And I would also maybe say open source code is a big part of it too. It's been around for longer than that, but it really grew a lot in the 2010s. And so those are the things I think that defined it for me.
Helena Sarin: For me, things around MOOCs and online massive learning were really instrumental on how I progressed on my own, essentially. And Gene is a perfect example, because his NYU class on machine learning for artists was fundamental for my development. So I think it's really kind of a game-changer.
Peter Bauman: Does anybody else have any or want to share? Have any of you met in person before, or are you friends? Does anybody know each other in this chat?
Helena Sarin: So I will continue, because Gene actually, besides his more… He was instrumental in many cases. I mean, I gave my public talk at NYU on his invitation, and also then it was like 2018 and I tested the waters with my approach to working with the GANs. And this actually became a foundation for the article that Jason Bailey and I wrote at the end of that year. So yeah, I mean, I consider Gene as a friend, like mentor, and really kind of influencer.
Rhea Myers: I know everyone via social media. I know parasocial relationships get a bad rap, but it's an epistolary era. It's a network of, or kingdom of, letters that we live in, just electronically. And I have met groups of people that I know online. For me, notably the Gray Area blockchain show. Was that 2018, 2019? I met lots of people I had previously only known online, to the point where, in a confused way, we were sort of hugging each other like we haven't seen each other for ages. And I was like, oh, we haven't actually met physically before. Okay, yeah, that makes sense.
Peter Bauman: Yeah, maybe more evidence for this decade being the social media era. And yeah, I think Gene has had, I'm sure, similar effects on countless coders today and over the last 15 or 20 years. We might have touched on many of those already, but does anybody want to include or mention any lesser known themes from the decade?
Tyler Hobbs: I mean, I don't know that it's non-obvious. It's pretty obvious, but the rise of mobile devices, phones as well obviously. I don't think that it's completely… This pretty quickly became the most common viewing format for a lot of what we do. And so, yeah, that has a big impact on what kind of work is successful. And especially if you're doing something live, live generated, for example, you have now particular hardware requirements that you have to be able to work with. So, yeah, there's a big, big impact from the rise of mobile phones.
Peter Bauman: And yeah, I mean, from creation of the work to how people procure it and consume it. Yeah, everything has been affected by mobile. And also this decade saw mobile phones really go completely mainstream and global. And then also things like the iPad too, and its effects.
The third question is about everybody on this call's, I think, fascination with randomness and chance. So Rhea, your early work explored cybernetic principles, and in some of your early blockchain work like Facecoin, you were literally searching in randomness. You were searching for human elements like faces. Helena, you've said GANs possess, and I quote, a certain unpredictability that inspires, unblocks and creates something special. Tyler, randomness was the topic of the very first essay that you wrote in 2014. And Gene, you've long studied and taught and devoted your career essentially to processes like randomness and emergence. So how do you think the continued secularization of society has impacted your views on randomness? Is there a connection between your interest in randomness and secularization in general?
Rhea Myers: In generative art, randomness is obviously one of the touchstones. The other is rules. And you can either create an entirely rule-based or largely random with a few rules to interpret them, or interleave them however you want. And it looks like a crutch or like a failure to really get to the bottom of it. […] Does seem to have more of an impact post-financial crisis.
I mean, for me personally, I always found the world very random and confusing. So using randomness in a way that I controlled was kind of therapeutic, or at least helped me theorize my experience of the world. And people get very upset when we talk about randomness and computers, because computers cannot generate actually random numbers. They can just fool us with patterns we can't guess, unless you plug in a hardware key to get actual quantum noise or something.
But I don't think the absence of broader religious or economic narratives requires a concept of randomness, because randomness still brings in the concept of variety, which we also see inside, which is the idea of cybernetics. And we're still in a very singular political era where there's less and less variety in the options that we're faced with, and randomness, where it's profoundly embraced, can be an escape rather than a distraction from that.
Peter Bauman: Yeah, thank you, Rhea. I think you very elegantly rephrased the question as, why randomness at this time? And much better than my wordy one. But yeah, I think that's an excellent rephrasing.
Helena Sarin: Yeah, from the personal experience, I'll start with a quote by Richter. "Chance does it better than I can, but I have to prepare the conditions to allow randomness to do its work." And in my case, to delineate this randomness, I used my own datasets. So kind of divide and conquer in a sense. So in tandem with randomness that GANs brought, I used the limited datasets to work with this randomness, to like these conditions.
Tyler Hobbs: For me, randomness is really just a necessary ingredient in exploring things systematically. So for me, the things that I found most interesting in nature were systematic, so biological systems or geological systems. And of course, those involve randomness, because if there was no randomness involved, then it would always do exactly the same thing. And so there wouldn't be anything very interesting to it. So really to have something systematic that also has variety and depth to it, you have to have that random ingredient. So for me, in my artwork, randomness is the fuel that powers the exploration of the systems that I develop.
So yeah, it's a really curious relationship with randomness. I've never really thought about it in spiritual terms. I don't have that kind of connection to it. For me, it's just really a necessary part of doing anything systematically.
“"Chance does it better than I can, but I have to prepare the conditions to allow randomness to do its work."” — Helena Sarin 16:52
Gene Kogan: I would maybe emphasize emergence. That was always kind of the thing that drove me. So a lot of small things, which in the sense random just means kind of uncoordinated, doing their own thing. But then something emerges from all of those small parts, like something bigger that's not explicitly there but really seems to be there. That is always, I think, at the heart of generative systems, more so than randomness itself.
Peter Bauman: Part of asking that question is, I think globally in the US and a lot of developed economies globally, you saw secularization really increase over the last 10 or 15 years. I'm wondering if that's coincided with this growth in technology, how our relationship with tech is changing. But Conrad, I think, did you want to?
Conrad House: Yeah, I think since we kind of hit the halfway point, we can maybe start jumping into some direct artist questions, and can hand it over to Jan.
thefunnyguys: Helena, your practice was entirely analog until you discovered GANs at some point between 2014 and 2018, we believe, after which generative models became your primary medium. And when, how did you first discover these GANs? And what about them prompted such a transformative artistic shift?
Helena Sarin: So, randomness… For instance, because I was doing some training for object recognition. And at the time the papers, specifically the CycleGAN paper, appeared about synthetic data, and actually this is what I did for my consulting gig. And I mean, the results were outstanding. I was so surprised how well, back in 2016, neural networks could generate pretty much plausible data, which was good for augmenting the data sets and also even surprised the customers.
And then, because I had so much digitized work of my own, I decided I should give it a try, like CycleGAN, to do this domain to domain translation. So basically a CycleGAN is style transfer on steroids, if you're familiar with style transfer. And I never did it. I mean, I was oblivious to the whole machine learning art community. And that was actually a blessing, because I achieved something being like an idiot. But I didn't have any imposter syndrome or anything, because I didn't know people even do such stuff.
And then one of my watercolor mentors, she told me she doesn't know anything about computers or anything, but she felt there is something in it. And then I decided, okay, to help with it, I'll post on Twitter. And this is how the story began.
Peter Bauman: Were your first posts those ones that I think are from February or March 2018? Were those your first works?
Helena Sarin: No, I mean, originally all my art was posted on Flickr, and this is like end of 2017 when I dared to post stuff. And then when I started searching for if people are doing these things, I discovered this small at the time community, Gene including. And Gene actually retweeted one of my stuff at the end of the year 2018. At the time I did these kind of food related things, because most of my photography was around stills and food, like a lot of Instagram today.
Peter Bauman: Maybe we can move to Rhea and Tyler about your beginnings in this decade as well. So Rhea, I know you began investigating blockchain. I've read in a post where you said you wanted to be the first Bitcoin artist, but it wasn't until you moved to Vancouver in 2013, and then working with, I think, blockchain more closely in 2014, that your interest in the work really took off. So I'm wondering, what was it about Vancouver that made blockchain and Bitcoin and that scene so appealing? And how was it different then from what I would imagine is quite a different community today, with the laser eyes and some different leaders of the community more recently as well? So yeah, I'm curious, what was it like 10 years ago?
Rhea Myers: Vancouver had and has a curiously large blockchain community. It's a Cascadian city. It has connections to the Canadian healthcare but West Coast libertarian leanings to some of its inhabitants' politics, and that makes for an interesting mix. And it means that the tech scene and the culture scene here were both very interested in cryptocurrency from very early on.
And it was the combination of those two, of companies like Dapper, who I later worked for, who created the ERC-721 standard and CryptoKitties for NFTs, and cultural spaces like DCTRL, which were much more community and hacker oriented, mixing without differentiation between their objectives or their social scenes. It meant that you had a very fertile ground both for the ideas being pursued in the technological infrastructure and then being pursued almost, not almost, but actually utopianly within society. And that completely fascinated me, because I hadn't seen anything like that since the early web in the mid-1990s. And so that was irresistible as someone who really hadn't got the early web or early net art, to be able to do that kind of thing. […] My primary focus from bots and 3D printing and other early 2010s tech to the blockchain as something that became, as it does for many people, a rabbit hole that I disappeared down and haven't really emerged from again yet.
Conrad House: Yeah, I like how you kind of mentioned that. It kind of gave you a second chance to experience a new form of media art with having this new whole emergence that you can kind of forefront.
And maybe we can move on to Tyler now. Maybe we can kind of have a question for you on your introduction into the 2010s. You really started to become a generative artist around 2014, and you had a lot of your very early essays around this time talking about how randomness is a fundamental part of your practice. You mentioned a lot of things by John Cage and Sol LeWitt, and a lot of these early essays, like what programming brings to art and how that kind of highlights the new artistic possibilities within generative art. So maybe can you touch on how these early essays and early concepts became so foundational to your practice, and then how your practice evolved within the 2010s going into the 2020s?
Tyler Hobbs: Absolutely. So just for context, prior to making generative art, I was a traditional artist doing drawing and painting, and I had been doing that since I was young. And 2014 is when, just the first time, I started trying to create artwork through programming. And I benefited a little bit, like Helena mentioned, of not being really aware of the tooling at that time or the style or the existing body of work. And so I was able to kind of come into it in a fresh way with my own ideas. But what was really fascinating and what kind of prompted me to write those early essays is how big of a shift it is. […] People fully tap what it was capable of.
And at least to me, I wasn't super up to date on what, for example, Casey Reas had been doing up to then. But to me, it felt like there was so much potential for moving up the level of abstraction and starting to not just make a specific work, but to think about what makes work good in general. How can you make a body of work in kind of one go, or a series? Obviously, there's some historical precedent for some of those things. Like John Cage obviously worked with randomness. Sol LeWitt obviously worked with rules. But neither of them, I could be wrong, but I don't think either of them really zoomed out quite so much to the systems level about how far it could potentially go. And I think computers are part of what makes that possible. You can create so much more complex systems, so much more specific systems. And of course, they take care of all the labor for you.
And so I was at the same time both really fascinated in what does it mean to work generatively? How does that change the artistic practice and the potential goals? And also with the computer as a medium, and specifically programming as a medium. I had seen a lot of digital painting. That's super common. I wasn't particularly interested in digital painting, but I'd still seen lots of it. But I really hadn't seen people working through programming. It had never made its way to me. And there's so many unique and particular and powerful capabilities that come from programming. And I also worked as a programmer, so I was very aware of just how much you can accomplish through programming. And so the essays were a bit of a thought experiment for me of really what is programming unlocking that somebody who's working with a pen or a paintbrush doesn't have access to. And that's what really compelled me to create a lot of my early work and to write about those ideas.
thefunnyguys: I believe, Tyler, in the 2000s, that you have been focused all along the century on procedural generative art, while Gene and Helena, they were exploring AI art. Rhea was exploring blockchain technology. Were these technological developments on your radar at all? And did you consider exploring them?
Tyler Hobbs: Yeah, I did a little bit. I actually also did a MOOC where I wrote early neural net implementations, like a little convolutional network and things like that. So I was familiar with those technologies, but they didn't interest me quite so much as a potential artistic medium. And in fact, I had played with those well before I started making generative art, maybe by three or four years.
To me, I love programming, and working through GANs and neural nets and things like that removes you from the creation a certain distance, that programming to me at least feels like a much more direct relationship with what's being created. Obviously, I'm greatly generalizing here and there's a lot more nuance to it than that. But I really loved working directly with the code and trying not to just make fuzzy tools, but to make very specific rules that my artwork would follow. And so programming is what allowed me to do that. That's why I think it was more attractive to me.
Helena Sarin: It's actually interesting angle, because I am the other way, quite opposite to this. Maybe because I was programming for a living. And actually, I got it. I mean, for me, the whole decade was about generative art. And I started with Casey Reas's book, his first book published in 2010, exactly. And then I learned about generative art. I took a workshop with Casey in 2013, and I tried to convince myself that I can be good in that. And I didn't like it at all. And that's why GANs was a kind of savior, because I got against whatever, like this fuzziness that attracted me. So yeah, it's funny.
Peter Bauman: That's amazing. Like we had mentioned, and Tyler, you mentioned MOOCs, and we talked about one of the potential themes of this decade being open source code. And I wanted to ask you, Gene, about your book, Machine Learning for Artists, this educational resource for artists who want to explore with a lot of the tools that we've been talking about today. I just wonder what inspired you to first create those resources, and how do you see them impacting the future of AI art?
Gene Kogan: I mean, it's been really, really great workshop and teaching culture in generative art. And at NYU people, the creative technology world, there was a lot of workshopping. And so it's just kind of plugging into that.
At the time I was coming at it from this, I had a little bit of background. I studied machine learning, so I was kind of coming from this engineering world. And I found that there was more and more interest from people, creative technologists, generative artists, in AI, that there wasn't really a whole lot of materials out. There wasn't a lot of materials at all in general, but certainly not the kind that were focused on an application level, a creative explorer's level view of the technology.
And so it just kind of naturally felt like something I could do that people would respond to. It's also a way of kind of integrating myself into this scene. I could go and give classes and meet people. And it was really just like a big part of my way of actually making this my life. Because otherwise it was kind of just in front of the screen or reading papers or something, and it wasn't really that.
And I would also add that there was just a lot of invitations. I started getting invitations because there was just kind of this desire, and so I just kind of responded to that. And yeah, it was a big part of what I did for a long time. Still is. I'm not giving as many workshops as I used to, but still, it's been kind of a long time coming from making those.
Conrad House: And I think Helena has talked about previously in the chat some of these early workshops. Just wondering if Helena had any follow up for, I guess, Machine Learning for Artists or any of these other workshops, if there's any other kind of expansive inspirations that came from this at the time.
Helena Sarin: The community. I mean, that was, this is what I'm missing these days. Because I'm not against people coming, like this avalanche of AI. But at the time, there was really kind of, we knew each other. We met at the excellent festival, which is not any more, killed by COVID or whatever. So yeah, I mean, I definitely was, it was something transformative for me as well. Like I met so many people there, so like Refik and Mario Klingemann. So it's kind of like when you know people, you kind of like inspired by them. You make better in the sense. I mean, inspiration, not in terms of copying, because like you, at least myself, you basically try to do something different. And it's kind of like a bit of friendly competition. So yeah, I mean, it's something I'm really nostalgic for.
Conrad House: Yeah, that sense of community really kind of, I think, is a driving force for a lot of these movements, being so helpful for really, I guess, igniting a fire under everybody.
I wanted to transition to Rhea. You mentioned it in your start of blockchain explorations that it was an easy aspect to jump into, because you were so fascinated with the ownership of art and the multiple aspects of ownership within art, whether that's provenance and other things. And then you've recently said that "all the early stuff I did was very intentionally made so you couldn't buy it. You couldn't sell it. You couldn't control it. That was the point of the work." How has your approach to creating art that resists commercialization evolved over time? I guess especially with the even more recent rise of NFTs.
Rhea Myers: From a career perspective, being so committed to a very European, hey, let's criticize capitalism flavor of art has not worked out very well for me with the early work, which people repeatedly ask me if they can buy. And I have to say, no, it's physically not possible.
But on a level with more integrity, I had to work through that to realize that no, the core proposition of the blockchain is the ownership and control of digital assets. And having realized that, and having been kind of blindsided by the emergence of digital assets, I had to really rethink, how do I maintain a critical position to making things to sell?
And the way that turned out to be possible was I'd already worked through that with the 3D printing work, where I was taking on the role of a post-conceptual artist, hiring some artisans to make objects, but actually revealing who these people were and creating them as artists in their own right, which they were, I must make very clear. And playing with the ownership mechanisms of Creative Commons licenses, to try and avoid making myself the villain whilst maintaining the role of the name artist in that system.
And so the things that really came through were with a Certificate of Inauthenticity, which takes that previously unownable work that I made, where I had already worked through being the bad guy, and ironized the anxieties of ownership and provenance that were emerging in NFT markets at the time into some art that I was perfectly happy firstly putting my name to, secondly taking money for, and then thirdly having people get something useful from as art.
But yeah, the dirty secret of the art world wherever you are is where the money comes from. And the thing that made a lot of people very uncomfortable about cryptocurrency and blockchain art was you could see where the money was coming from. You couldn't pretend that you lived in this carefree world of imagination and information and aesthetics and art, unsullied by the turn of capital. No, you could see where the money comes from and how much effort goes into securing the products of your imagination. And so getting my hands dirty with that was and continues to be a fantastically useful moral and intellectual exercise, definitely.
Conrad House: Yeah, that's very interesting, Rhea. I believe with your release just earlier this week, like a 10k drop, once again you're also playing into these questions.
Rhea Myers: Yes, yeah, I mean, 10k drop, obviously it's a bad joke, but it's also […] owning and promotion and emotional and social and economic investment in generative art, PFPs on the blockchain, and trying to give people a history painting in very strange form of that era and its ambitions.
“But yeah, the dirty secret of the art world wherever you are is where the money comes from.” — Rhea Myers 40:07
thefunnyguys: I think it's a fantastic collection. We're now approaching the end of the hour, and maybe one general question. I will point it first to you, Tyler, but please, all of you, jump in. We're curious to hear all of your thoughts.
A thread in generative art history is that a lot of criticism is focused on the use of computers and the coldness of computers, and that computer art is kind of like a contradiction in terms, because art is human. Computers are cold, they're not human. How can they even go together? A question that we have for you, Tyler. How does technology, in your opinion, reveal what makes us human?
Tyler Hobbs: That's a good question. It's definitely one that I think about a lot. I don't know that I have the answer to it per se, but obviously a lot of what the computer reveals about us is via contrast. It's all the ways that the computer is different from us. It's, in fact, quite alien. What motivates a lot of my work today is exploring the ways in which it is strange and the ways in which it is different from everything that we evolved to expect and to enjoy in the world.
It's quite obvious to me that computers aren't going anywhere. Our lives are getting more and more entangled with computers. I'm kind of interested in this question of, with that shift to a more digital life, what really is changing? What are we maybe losing or gaining? Strangely, I'm not pro or anti-computer. I just find it really fascinating and just really ripe for artistic exploration.
Computers do everything perfectly, exactly the way that you ask them to. They never deviate from that at all. You ask for a straight line and you get a perfectly straight line. In fact, you have to work to get the opposite of that. You want anything that has any complexity or richness to it, you have to really, really work for it. Whereas, you know, we're used to in the natural world, you try and draw a straight line by hand and it's never going to be straight. It's always going to be flawed. You have to work really, really hard to try to make it perfect. It's quite the opposite end of the spectrum, and that means there's a lot of really interesting phenomena to investigate, I think, there. That's a big focus of my artwork.
Gene Kogan: I have kind of a lot of things I could say. There's something about what computers can do that fascinates me, but I'll just take one angle, which is that there's a lot of dialogue, debate about what AI has to say about this. A lot of it is, to me, if you view AI as a tool, something that can be used to just amplify an artist, an existing artist's work. I just saw a friend who's an illustrator, and he was able to turn his illustrations into basically a style of animation. You can turn every illustrator into an animator. And it's not something that just takes a few seconds. It's not something that you can work just as hard and just as long as you can work.
People will often say, oh, now it's easy to make pictures. And that's true. It's easy to make pictures, but you can work really long and hard to get better results. And you can just do more. It sort of adds this extra dimension or multiplication of your work. And so in that sense, maybe seeing computers as just kind of supporting and amplifying artists is one way of maybe resolving that tension. I would go further and talk about certain things that computers can do that are fascinating in and of themselves, but maybe that's just a bigger can of worms. So I'm going to leave it there.
“Computers do everything perfectly, exactly the way that you ask them to. They never deviate from that at all. You ask for a straight line and you get a perfectly straight line.” — Tyler Hobbs 43:52
Rhea Myers: The dirty secret of contemporary art is everyone uses computers. I've seen painters working out compositions in Photoshop. And even if you never type, if you don't touch a computer in the studio, you're on social media. And if you're not on social media, you're emailing your gallery. And if you're not emailing your gallery, then you've probably got a spreadsheet somewhere with all your work in it.
And I remember very clearly the moment that there's this phase transition from "you can't make art with computers, so there is nothing interesting about computer art" to "everyone makes art using computers, so there's nothing interesting about computer art," in the mid-2000s. And that was a really, really, I'm going to say interesting, but I mean in the bad sense, moment to live through.
But computers, for all their obsessive rule-following, and yeah, if you tell a computer to do something, it will happily boil the oceans or turn the world into paper clips in order to do what you think you've told it. […] They do capture and propagate the warmth and the noise of signs of human activity. I learned this again way back in the early '90s when I was first scanning photographs and getting all the photographic grain and scanner noise on the screen and thinking, I'm going to keep this. I'm not going to try and hide this. And morphing images and things like that. […] But brought something extra to it.
I think what we imagine to be the cold, hard, rule-based world of the computer is as much a product of the human imagination as anything else. And blockchain has been very useful for the vanguard and the absolute of that view of the opposition of clean, incorruptible, perfect rules versus messy human decision-making. Seeing that hit reality, and the rules haven't won. So yeah, that's been a very interesting outcome of the blockchain era in terms of how we see our relationship to computing machinery.
Peter Bauman: Interesting to think about how, yeah, a lot of the coldness of computers came from our human input. But Helena, was there anything that you wanted to?
Helena Sarin: No, for me, it's just, again, I never considered computers being cold or whatever. It was always a part of my job. And actually, my pet peeve right now that we have, nothing bad about a lot of AI art, but it kind of dulls the eye for me personally. So I basically pretty much stopped doing digital art and moved towards generative pottery. So this is my contribution to future artifacts, to kind of giving whatever produced essentially through designs with GANs back to humans.
Peter Bauman: Yeah, thank you. And I think even just that last comment about GANs back to humans, it highlights why we do these talks, and what Tyler was saying about the power of thinking about contrasts. I mean, I think it's helpful to look back, even if it's looking back just a few years. I think you can already see that this decade did have a personality in it, and it had an increasing number of developments that have affected and seemingly will continue to affect our lives. And you were all in the middle of it, and in the middle of it especially intervening creatively with these technologies. So thank you so much for not only joining us today, but also for being there and thinking ahead at that time. And it's been, I mean, what a pleasure for us to have you. Thank you so much for joining us. Really been a great talk, and I only wish genuinely that we could keep going. So thank you all so much.
Helena Sarin: Thank you, thank you. Bye.
Rhea Myers: Thank you, it's been wonderful.
Tyler Hobbs: Thank you everyone.
Gene Kogan: Thank you, thank you everybody.
Peter Bauman: Thank you everyone.
