AI Art: How artists are using and confronting machine learning | HOW TO SEE LIKE A MACHINE

Summary

The Museum of Modern Art's video explores how artists are engaging with AI technologies, not just as tools but as mediums to challenge and explore perceptions of reality. Artists use AI to ask existential questions about free will and machine behavior, venturing into the speculative and imaginative realms through unsupervised learning. The exhibition highlights the profound biases inherent in AI systems due to human-created data sets, raising questions about cultural and political implications. This dialogue reflects a long-standing investigation by artists into the relationship between humans and machines, suggesting both a consolidation of power and a creative redefinition of art's role in the modern world.

Highlights

• Artists using AI as an innovative artistic medium, not just a tool 🎨
• Artists challenging conventions by making AI do unexpected tasks 🤔
• The speculative nature of unsupervised learning in AI art 🧠
• Cultural and political biases embedded in AI training data 📊
• Revisiting the historical relationship between art and machines 🤝

Key Takeaways

• Artists are using AI to question and redefine tool usage and control 🛠️
• Unsupervised learning allows machines to dream and explore the unknown 🌌
• AI artworks speculate on limitless possibilities and break perceived boundaries 🙌
• The biases in AI systems are rooted in cultural and historical contexts 🌍
• Artists have always been voices in technological changes, challenging the status quo 🎨

Overview

The intersection of art and AI is a fascinating playground for the contemporary artist, with machine learning providing new ways to explore creativity and perception. This video from The Museum of Modern Art highlights how artists are leading conversations about AI and its implications, questioning the very nature of free will and manipulation by diving into unsupervised learning models.

Artists are not passively observing AI's growth; they are actively engaging with it, reshaping its constraints and exploring its potential. Instead of traditional labels and supervised learning, they're pushing AI to 'dream' by generating new realities and aesthetics that challenge our traditional boundaries of thought and creativity.

Moreover, the video dives into the biases ingrained in AI due to human subjectivity in data tagging, raising concerns about technology's impact on culture and politics. Artists seem to be revisiting and reimagining the age-old discourse on machines, suggesting an imminent shift in how art is perceived and created, questioning the future and significance of creativity itself.

Chapters

• 00:00 - 01:30: Introduction to AI in Art This chapter sheds light on the intersection of artificial intelligence (AI) and art. It highlights the prevalence of AI-driven tools in the art world, despite a general lack of understanding about these technologies. The chapter discusses how some artists embrace AI as a creative tool, while others aim to increase public awareness and understanding of AI. It also touches on the desire among certain artists to actively engage with and influence AI technologies, moving beyond passive consumption to active intervention and high-level integration.
• 01:30 - 03:00: AI and Machine Learning Concepts This chapter explores profound themes related to AI and machine learning, such as the concepts of free will, both human and machine, and challenges of perception. It highlights the human capacity to see beyond the ordinary, particularly through artistic expression. Artists are depicted as innovators, who ingeniously repurpose existing tools and technologies to see and create beyond their original intent, pushing the boundaries of what machines are perceived to understand and achieve. The chapter emphasizes the role of experimentation and innovation in art and technology.
• 03:00 - 05:00: Rafiq's Exhibition at MoMA The chapter discusses an art exhibition by Rafiq at the Museum of Modern Art (MoMA). The focus is on the artist's approach to using technology in art, not by outright rejecting it but by embracing it in innovative ways. The exhibition is currently garnering attention, with many people expressing amazement at the breakthroughs it showcases.
• 05:00 - 09:00: Trevor Paglen's Exploration of Training Data The chapter delves into Trevor Paglen's exploration of training data, particularly in the realm of AI research. It discusses various AI technologies such as OpenAI's DALL-E, GPT, Stable Diffusion, and Midjourney, highlighting their reliance on supervised learning and labeled training data. The chapter explores how these multimodal AI algorithms are designed to interact with humans through a supervised learning process, emphasizing the traditional approach where human input is crucial to their functioning and development.
• 09:00 - 12:00: Historical Context of Art and Machines This chapter explores the connection between art and technology, specifically focusing on the use of artificial intelligence in art. It discusses the difference between using AI for simple, illustrative tasks like generating images of specific objects, and more innovative uses where AI is employed in unconventional ways that deviate from standard expectations. The chapter highlights an exhibition at MoMA, which exemplifies this innovative approach by presenting art that doesn't strictly adhere to conventional technology usage.
• 12:00 - 15:00: Modern Implications of AI Systems This chapter explores the modern implications of AI systems, particularly focusing on unsupervised learning. Unsupervised learning allows AI models to independently create tags or labels based on its own observations, functioning somewhat like a black box whose internal workings are often unknown even to its creators. The discussion includes a case study where the entire metadata of Noma archives is subject to unsupervised learning techniques, highlighting the machine's imaginative capabilities beyond traditional data labeling or replication of reality.

AI Art: How artists are using and confronting machine learning | HOW TO SEE LIKE A MACHINE Transcription

• 00:00 - 00:30 foreign are powered by AI in some way but there's very little understanding about it there are artists that are using AI as a tool and there are artists that want the people to understand more about AI we sometimes feel captive to or passive to the technologies that are simply given to us and for a number of artists they want to intervene in those processes at a high level and think
• 00:30 - 01:00 about existential questions of Free Will of human will machine will but also questions of perception how can we see things that are actually not made for us to see one thing that artists have always been very good at is taking a tool that exists in the world and making it do something it's not supposed to do when artists are looking at technology as another kind of tool to experiment with
• 01:00 - 01:30 or maybe even subvert or divert they're saying I'm not just going to reject this technology wholesale at all in fact I might embrace it but I might try and make it do something else this is the show at Momo right now how it looks like how many people there like this like wow last year we saw amazing breakthroughs
• 01:30 - 02:00 in AI research like open ai's Dali dalitu chechi PT stable diffuse mid-journey like these are all actually following a very similar pattern which are extremely supervised extremely labels multimodal AI algorithms allowing us to interact with them in quote unquote supervised learning which is the more conventional mode of machine learning humans are the ones that are
• 02:00 - 02:30 essentially tagging these bits of information from the outset saying here's a picture of a pencil this is a pencil AI is quite good at saying if you tell me to create a bluebird I'll show you a picture of a bluebird that looks real but this was to me not very inspiring to be honest to me what was more inspiring is what happens if you don't use a technology as it's imposed to us but use it a different way and in this context unsuperwise the exhibition now at MoMA is actually doing something different it's not exactly following the
• 02:30 - 03:00 labeling data or try to mimic reality it is trying to dream and speculate an imagination of a machine so unsupervised learning means that actually you're letting the machine learning model do that tagging based on its own sort of who knows yeah it's actually it's a lot of it is a black box like we don't actually know what's going on in there for unsupervised we took the entire metadata of Noma archives which
• 03:00 - 03:30 is around 138 000 images and Rafiq used that data to create custom software artwork that would interpret and transform moma's collection data he has created a large-scale presentation of this real-time software artwork it's almost like a performance it's always changing the machine learning model has built an incredibly complex classification system
• 03:30 - 04:00 or map of moma's collection it has decided it's going to group a number of data points over here and a number of data points over here and you create a kind of Galaxy but in that Galaxy there's a lot of empty space so then the machine learning model in concert with rafiq's team is sort of navigating through that empty space and saying nothing exists here but what could exist and that is where the kind of
• 04:00 - 04:30 speculative and hypothetical and even what we might call a kind of dreaming starts to take place [Music] I should do the show of you this this is like the really next level so what you see on the left side is complete material these are all I made potential AI dreams so what we can do here I'm literally flying in the latest space of Moma archive and reconstructing those potential dreams
• 04:30 - 05:00 [Music] once AI starts to create this new reality we learned that there is no any borders between this biased categories that we need as humans to understand things that is a truly multi-dimensional imagination it is blending past now and feature it is blending multiple
• 05:00 - 05:30 materials it's just a convergence of things that we thought they are independent guy is not it's a reference it's just uh peering into another type of mind [Music] I think we are at a crucial inflection point right now
• 05:30 - 06:00 I've been calling it The generative turn it's a moment where what we previously understood as how everything from illustration to film directing to publishing works is all about to change very rapidly there is this assumption that AI systems are somehow highly scientifically objective that they are presenting a view on the world that is somehow unmediated but of course the
• 06:00 - 06:30 opposite is true you know these are systems that are profoundly skewed from the absolute beginning from the data that they're trained on this is something that I worked with Trevor paglanon where we studied the training data sets that are used to teach AI systems to see the world my interest in AI is not at all like oh what kind of whiz-bang kind of image can you make using it that's actually
• 06:30 - 07:00 totally uninteresting to me what's interesting to me is looking at AI as not only a set of Technical Systems but Technical Systems that have culture built into them that have politics implicitly built into them and trying to unpack that Trevor paglin's work behold these glorious times is a kind of hypnotic video that is basically flashing at you many different images that are used to train AI
• 07:00 - 07:30 the age of machine learning is kind of characterized by building AI systems and computer vision systems where the idea is that you have a lot of images of different things and then you use the neural network to find patterns across those images so behold these glorious times looks at those kinds of training sets these much larger databases that are used for things like object recognition forms of contemporary face recognition but other things as well for
• 07:30 - 08:00 example gesture recognition or gait recognition so the video installation goes back and forth between just looking at the images by themselves at this very fast speed and then starting to get a glimpse of what the machine Learning System is doing internally to try to distinguish these images from one another Trevor is an artist who is is really laying bare the algorithmic and inherent biases in many AI systems but also the
• 08:00 - 08:30 ways in which these definitions have real world implications many of which are obviously terrifying there's moments towards the end of behold these glorious times where you're seeing training sets that are made out of things like family home videos or extremely personal moments in people's lives and they've been incorporated into training sets to understand oh this is a mother like putting down a baby so we want to understand what a mother with a
• 08:30 - 09:00 baby looks like so that we can try to sell them diapers or whatever we want right and so you're seeing the ways in which certain kinds of content are being ingested into machine Learning Systems in order to try to capitalize on learning what can be extracted from those moments of intimacy [Music] asks me as tourists that we're assuming that a single image can be given a label a single word when we know about the
• 09:00 - 09:30 multiplicity and complexity of a single image the idea that we can so benignly label something as a chair and then a person as say a debtor or a kleptomaniac these are things that are literally happening today in data sets and the risk there is that we're starting to see a very simplified and it's always just really bleached version of the world we have all kinds of
• 09:30 - 10:00 allegorical and kind of squishy meanings attached to all of the things that we look at in our everyday lives those are informed by our own histories our cultures our own memories and so that question of meaning making is all over the place artists what we bring to the party is thousands if not tens of thousands of years of thinking about what the hell an image is the kind of engineering computer science tradition does not have
• 10:00 - 10:30 that this is a place where artists are bringing voices to the conversation that I think are quite urgent we're talking about AI today but in truth the fascination and the fear that humans have with machines and with technology has been Amplified and examined and explored by artists and by designers since technology existed
• 10:30 - 11:00 early in the 20th century artists were fascinated by industrial production and what that meant for someone like an artist suddenly the most skilled human technician was actually outpaced by an apparatus like a camera or some kind of forming machine that used a conveyor belt and artists like Marcel Duchamp said well wait a second I'm actually going to radically redefine what an
• 11:00 - 11:30 artist does and what art even is and he took a ready-made industrial object like a bicycle wheel and stuck it on a stool and said this is Art because I say so in one Fell Swoop he realized this kind of crisis of the artist in the 20th century which is what is art if it's not technical facility or total realism these are things that suddenly felt scary but also exciting
• 11:30 - 12:00 I would like to just move to the beginning of the Museum of Modern Art and in 1934 there was a show called machine art in which pieces of Machinery were unveiled and placed on white pedestals against White Walls and the beauty of the machine revealed to the world and slowly but surely designers have been trying to really understand how to use machines so ocra from the mid-1960s was a font that was designed
• 12:00 - 12:30 to be understood by machines a few decades later it's instead the machines trying to make concept as readable as possible by humans it's an evolutionary process in which humans and machine kind of grow together there are definitely a lot of hard problems that AI can absolutely help solving having said that in going back to the idea that the context in which these
• 12:30 - 13:00 tools are always being deployed is by huge corporations they worry that there is a huge potential for a massive consolidation of wealth and political power and I'm concerned that that adds up to an increasingly inequitable Society even if the problems that we want to solve can be solved it's always about capitalism not technology right this system
• 13:00 - 13:30 drawing on storing on a labor on our voices on the earth on the mineralogical layer on energy on water so for anatomy of an AI system we really wanted to show the full life cycle of an AI system and in this case if we chose the Amazon Alexa so every time you turn to Alexa and say hey Alexa what's the weather tomorrow you're bringing into being this incredibly complex Network that starts all the way
• 13:30 - 14:00 back in the mines where the rare earth minerals are being extracted through to the end of life of these devices to really show that full planetary cost of an AI system I'm really interested in quite radical approaches of how people could use these tools in ways that they've never been designed to use how might we upend the expectations that these tools are for work or for efficiency and to try and
• 14:00 - 14:30 find ways to make them inefficient to find ways to actually make them work against themselves I also believe that AI algorithms may have a different purpose it's kind of this finding the language of humanity by using Collective memories to create Collective dreams and eventually Collective consciousness the near feature that is coming right now very much will be questioning creativity questioning who will Define
• 14:30 - 15:00 what is real or not and I think we have to be ready [Music]