alignDRAW brings together five essays that revisit Elman Mansimov’s 2015 experiment in generating images from text using neural networks. The book presents this early stage of text-to-image AI as both a technical and cultural event, where computation began to participate in image-making. The essays by Lev Manovich, Shana Lopes, Eva Jäger, Mat Dryhurst, and Darius Himes situate Mansimov’s work within longer histories of visual representation, from photography to conceptual art, and examine how alignDRAW blurs the boundaries between research and artistic practice.
Code experiments like Mansimov’s are consistent with the logic and aims of contemporary art: process over product, methodology made visible, participation invited, and new aesthetic territories explored. If contemporary art institutions claim to care about where genuinely new forms of seeing and making emerge, this project argues, they must broaden their attention to include these forms of computational creation.
In January 2024 the prestigious Worcester Art Museum announced their acquisiton of 3 paper prompt print editions:
The artworks will be included in an upcoming show entitled New Terrain: 21st-Century Landscape Photography. This Spring Exhibition will showcase meditations on place and identity created in the past 20 years.
The museum's vast collection spans 38,000 objects from around the world and Mansimov's alignDRAW will share a place alongside some of the some of the most renowned artists in history incluiding Picasso, Rembrant, Monet, Van Gogh, Matisse, Pollock and more.
In June 2024, alignDRAW was exhibited at Basel alongside Art Basel, the world's most prestigious art fair.
alignDRAW, the first text-to-image tool, was positioned between Harold Cohen's AARON, the first AI art-making tool, and Holly Herndon + Mat Dryhurst's Infinite ∞ Images, the only artwork created with DALL·E 1, the first mainstream AI tool.
"The widespread popularity of DALL·E and Stable Diffusion shed the limelight onto the first text-to-image model, alignDRAW, developed in 2015 by the AI researcher Elman Mansimov, its smaller lower-resolution images portraying the infancy of a technology that would then rapidly mature and come to define the artistic practice of the early 2020s. The small squares with barely recognisable shapes are significant as proof of concept of a new form of communication between machines and humans, one that involves our own language as opposed to code: finally the day has come when we can exchange ideas with machines as equals."
"There was significant advancement in image captioning around that time [2015]. Given my familiarity with generative models and newly acquired research skills, I pondered over inverting the captioning process. Instead of transitioning from image to text, I envisioned going from text to image. My core hypothesis before embarking on alignDRAW was that image generation should be a recurrent, iterative process, rather than an instantaneous creation.
I was 19 when I began this project. At the time, I wasn't dwelling on my age. I felt I was doing something significant and valuable. My primary goal was to hone my skills and enhance my academic prospects in deep learning. While I anticipated progress in generative models and image generation, I hadn't foreseen this technology becoming as mainstream and impactful as it has."
Read the full interview here: https://daily.xyz/insights/3
Mansimov et al's official published academic paper from 2015.
Mansimov et al's official published academic paper from 2015.
Vox video showing Elman Mansimov as the inventor of text-to-image models.
Vox video showing Elman Mansimov as the inventor of text-to-image models.
Wikipedia article crediting alignDRAW as the first modern text-to-image model.
Wikipedia article crediting alignDRAW as the first modern text-to-image model.
Wired magazine article from 2015 praising 19 year old "artificial intelligence wunderkind" Mansimov.
Wired magazine article from 2015 praising 19 year old "artificial intelligence wunderkind" Mansimov.
alignDRAW brings together five essays that revisit Elman Mansimov’s 2015 experiment in generating images from text using neural networks. The book presents this early stage of text-to-image AI as both a technical and cultural event, where computation began to participate in image-making. The essays by Lev Manovich, Shana Lopes, Eva Jäger, Mat Dryhurst, and Darius Himes situate Mansimov’s work within longer histories of visual representation, from photography to conceptual art, and examine how alignDRAW blurs the boundaries between research and artistic practice.
Code experiments like Mansimov’s are consistent with the logic and aims of contemporary art: process over product, methodology made visible, participation invited, and new aesthetic territories explored. If contemporary art institutions claim to care about where genuinely new forms of seeing and making emerge, this project argues, they must broaden their attention to include these forms of computational creation.
In January 2024 the prestigious Worcester Art Museum announced their acquisiton of 3 paper prompt print editions:
The artworks will be included in an upcoming show entitled New Terrain: 21st-Century Landscape Photography. This Spring Exhibition will showcase meditations on place and identity created in the past 20 years.
The museum's vast collection spans 38,000 objects from around the world and Mansimov's alignDRAW will share a place alongside some of the some of the most renowned artists in history incluiding Picasso, Rembrant, Monet, Van Gogh, Matisse, Pollock and more.
In June 2024, alignDRAW was exhibited at Basel alongside Art Basel, the world's most prestigious art fair.
alignDRAW, the first text-to-image tool, was positioned between Harold Cohen's AARON, the first AI art-making tool, and Holly Herndon + Mat Dryhurst's Infinite ∞ Images, the only artwork created with DALL·E 1, the first mainstream AI tool.
"The widespread popularity of DALL·E and Stable Diffusion shed the limelight onto the first text-to-image model, alignDRAW, developed in 2015 by the AI researcher Elman Mansimov, its smaller lower-resolution images portraying the infancy of a technology that would then rapidly mature and come to define the artistic practice of the early 2020s. The small squares with barely recognisable shapes are significant as proof of concept of a new form of communication between machines and humans, one that involves our own language as opposed to code: finally the day has come when we can exchange ideas with machines as equals."
"There was significant advancement in image captioning around that time [2015]. Given my familiarity with generative models and newly acquired research skills, I pondered over inverting the captioning process. Instead of transitioning from image to text, I envisioned going from text to image. My core hypothesis before embarking on alignDRAW was that image generation should be a recurrent, iterative process, rather than an instantaneous creation.
I was 19 when I began this project. At the time, I wasn't dwelling on my age. I felt I was doing something significant and valuable. My primary goal was to hone my skills and enhance my academic prospects in deep learning. While I anticipated progress in generative models and image generation, I hadn't foreseen this technology becoming as mainstream and impactful as it has."
Read the full interview here: https://daily.xyz/insights/3
Mansimov et al's official published academic paper from 2015.
Mansimov et al's official published academic paper from 2015.
Vox video showing Elman Mansimov as the inventor of text-to-image models.
Vox video showing Elman Mansimov as the inventor of text-to-image models.
Wikipedia article crediting alignDRAW as the first modern text-to-image model.
Wikipedia article crediting alignDRAW as the first modern text-to-image model.
Wired magazine article from 2015 praising 19 year old "artificial intelligence wunderkind" Mansimov.
Wired magazine article from 2015 praising 19 year old "artificial intelligence wunderkind" Mansimov.
alignDRAW brings together five essays that revisit Elman Mansimov’s 2015 experiment in generating images from text using neural networks. The book presents this early stage of text-to-image AI as both a technical and cultural event, where computation began to participate in image-making. The essays by Lev Manovich, Shana Lopes, Eva Jäger, Mat Dryhurst, and Darius Himes situate Mansimov’s work within longer histories of visual representation, from photography to conceptual art, and examine how alignDRAW blurs the boundaries between research and artistic practice.
Code experiments like Mansimov’s are consistent with the logic and aims of contemporary art: process over product, methodology made visible, participation invited, and new aesthetic territories explored. If contemporary art institutions claim to care about where genuinely new forms of seeing and making emerge, this project argues, they must broaden their attention to include these forms of computational creation.
In January 2024 the prestigious Worcester Art Museum announced their acquisiton of 3 paper prompt print editions:
The artworks will be included in an upcoming show entitled New Terrain: 21st-Century Landscape Photography. This Spring Exhibition will showcase meditations on place and identity created in the past 20 years.
The museum's vast collection spans 38,000 objects from around the world and Mansimov's alignDRAW will share a place alongside some of the some of the most renowned artists in history incluiding Picasso, Rembrant, Monet, Van Gogh, Matisse, Pollock and more.
In June 2024, alignDRAW was exhibited at Basel alongside Art Basel, the world's most prestigious art fair.
alignDRAW, the first text-to-image tool, was positioned between Harold Cohen's AARON, the first AI art-making tool, and Holly Herndon + Mat Dryhurst's Infinite ∞ Images, the only artwork created with DALL·E 1, the first mainstream AI tool.
"The widespread popularity of DALL·E and Stable Diffusion shed the limelight onto the first text-to-image model, alignDRAW, developed in 2015 by the AI researcher Elman Mansimov, its smaller lower-resolution images portraying the infancy of a technology that would then rapidly mature and come to define the artistic practice of the early 2020s. The small squares with barely recognisable shapes are significant as proof of concept of a new form of communication between machines and humans, one that involves our own language as opposed to code: finally the day has come when we can exchange ideas with machines as equals."
"There was significant advancement in image captioning around that time [2015]. Given my familiarity with generative models and newly acquired research skills, I pondered over inverting the captioning process. Instead of transitioning from image to text, I envisioned going from text to image. My core hypothesis before embarking on alignDRAW was that image generation should be a recurrent, iterative process, rather than an instantaneous creation.
I was 19 when I began this project. At the time, I wasn't dwelling on my age. I felt I was doing something significant and valuable. My primary goal was to hone my skills and enhance my academic prospects in deep learning. While I anticipated progress in generative models and image generation, I hadn't foreseen this technology becoming as mainstream and impactful as it has."
Read the full interview here: https://daily.xyz/insights/3
Mansimov et al's official published academic paper from 2015.
Mansimov et al's official published academic paper from 2015.
Vox video showing Elman Mansimov as the inventor of text-to-image models.
Vox video showing Elman Mansimov as the inventor of text-to-image models.
Wikipedia article crediting alignDRAW as the first modern text-to-image model.
Wikipedia article crediting alignDRAW as the first modern text-to-image model.
Wired magazine article from 2015 praising 19 year old "artificial intelligence wunderkind" Mansimov.
Wired magazine article from 2015 praising 19 year old "artificial intelligence wunderkind" Mansimov.
alignDRAW brings together five essays that revisit Elman Mansimov’s 2015 experiment in generating images from text using neural networks. The book presents this early stage of text-to-image AI as both a technical and cultural event, where computation began to participate in image-making. The essays by Lev Manovich, Shana Lopes, Eva Jäger, Mat Dryhurst, and Darius Himes situate Mansimov’s work within longer histories of visual representation, from photography to conceptual art, and examine how alignDRAW blurs the boundaries between research and artistic practice.
Code experiments like Mansimov’s are consistent with the logic and aims of contemporary art: process over product, methodology made visible, participation invited, and new aesthetic territories explored. If contemporary art institutions claim to care about where genuinely new forms of seeing and making emerge, this project argues, they must broaden their attention to include these forms of computational creation.
alignDRAW brings together five essays that revisit Elman Mansimov’s 2015 experiment in generating images from text using neural networks. The book presents this early stage of text-to-image AI as both a technical and cultural event, where computation began to participate in image-making. The essays by Lev Manovich, Shana Lopes, Eva Jäger, Mat Dryhurst, and Darius Himes situate Mansimov’s work within longer histories of visual representation, from photography to conceptual art, and examine how alignDRAW blurs the boundaries between research and artistic practice.
Code experiments like Mansimov’s are consistent with the logic and aims of contemporary art: process over product, methodology made visible, participation invited, and new aesthetic territories explored. If contemporary art institutions claim to care about where genuinely new forms of seeing and making emerge, this project argues, they must broaden their attention to include these forms of computational creation.
alignDRAW brings together five essays that revisit Elman Mansimov’s 2015 experiment in generating images from text using neural networks. The book presents this early stage of text-to-image AI as both a technical and cultural event, where computation began to participate in image-making. The essays by Lev Manovich, Shana Lopes, Eva Jäger, Mat Dryhurst, and Darius Himes situate Mansimov’s work within longer histories of visual representation, from photography to conceptual art, and examine how alignDRAW blurs the boundaries between research and artistic practice.
Code experiments like Mansimov’s are consistent with the logic and aims of contemporary art: process over product, methodology made visible, participation invited, and new aesthetic territories explored. If contemporary art institutions claim to care about where genuinely new forms of seeing and making emerge, this project argues, they must broaden their attention to include these forms of computational creation.
