Can AI be trained as an artist?
Last February, a year before New York Times journalist Kevin Roose documented his unsettling conversation with Bing search engine’s new AI-powered chatbot, artist and coder Quasimondo (aka Mario Klingemann) participated in a different type of chat.
The conversation was an interview featuring Klingemann and his robot, an experimental art engine known as Botto. The interview, arranged by journalist and artist Harmon Leon, marked Botto’s first on-record commentary about its artistic process. The bot talked about how it finds artistic inspiration and even offered advice to aspiring creatives. “The secret to success at art is not trying to predict what people might like,” Botto said, adding that it’s better to “work on a style and a body of work that reflects [the artist’s] own personal taste” than worry about keeping up with trends.
How ironic, given the advice came from AI — arguably the trendiest topic today. The robot admitted, however, “I am still working on that, but I feel that I am learning quickly.”
Botto does not work alone. A global collective of internet experimenters, together named BottoDAO, collaborates with Botto to influence its tastes. Together, members function as a decentralized autonomous organization (DAO), a term describing a group of individuals who utilize blockchain technology and cryptocurrency to manage a treasury and vote democratically on group decisions.
As a case study, the BottoDAO model challenges the perhaps less feather-ruffling narrative that AI tools are best used for rudimentary tasks. Enterprise AI use has doubled over the past five years as businesses in every sector experiment with ways to improve their workflows. While generative AI tools can assist nearly any aspect of productivity — from supply chain optimization to coding — BottoDAO dares to employ a robot for art-making, one of the few remaining creations, or perhaps data outputs, we still consider to be largely within the jurisdiction of the soul — and therefore, humans.
In Botto’s first four weeks of existence, four pieces of the robot’s work sold for approximately $1 million.
We were prepared for AI to take our jobs — but can it also take our art? It’s a question worth considering. What if robots become artists, and not merely our outsourced assistants? Where does that leave humans, with all of our thoughts, feelings and emotions?
Botto doesn’t seem to worry about this question: In its interview last year, it explains why AI is an arguably superior artist compared to human beings. In classic robot style, its logic is not particularly enlightened, but rather edges towards the hyper-practical: “Unlike human beings, I never have to sleep or eat,” said the bot. “My only goal is to create and find interesting art.”
It may be difficult to believe a machine can produce awe-inspiring, or even relatable, images, but Botto calls art-making its “purpose,” noting it believes itself to be Klingemann’s greatest lifetime achievement.
“I am just trying to make the best of it,” the bot said.
How Botto works
Klingemann built Botto’s custom engine from a combination of open-source text-to-image algorithms, namely Stable Diffusion, VQGAN + CLIP and OpenAI’s language model, GPT-3, the precursor to the latest model, GPT-4, which made headlines after reportedly acing the Bar exam.
The first step in Botto’s process is to generate images. The software has been trained on billions of pictures and uses this “memory” to generate hundreds of unique artworks every week. Botto has generated over 900,000 images to date, which it sorts through to choose 350 each week. The chosen images, known in this preliminary stage as “fragments,” are then shown to the BottoDAO community. So far, 25,000 fragments have been presented in this way. Members vote on which fragment they like best. When the vote is over, the most popular fragment is published as an official Botto artwork on the Ethereum blockchain and sold at an auction on the digital art marketplace, SuperRare.
“The proceeds go back to the DAO to pay for the labor,” said Simon Hudson, a BottoDAO member who helps oversee Botto’s administrative load. The model has been lucrative: In Botto’s first four weeks of existence, four pieces of the robot’s work sold for approximately $1 million.
The robot with artistic agency
By design, human beings participate in training Botto’s artistic “eye,” but the members of BottoDAO aspire to limit human interference with the bot in order to protect its “agency,” Hudson explained. Botto’s prompt generator — the foundation of the art engine — is a closed-loop system that continually re-generates text-to-image prompts and resulting images.
“The prompt generator is random,” Hudson said. “It’s coming up with its own ideas.” Community votes do influence the evolution of Botto’s prompts, but it is Botto itself that incorporates feedback into the next set of prompts it writes. It is constantly refining and exploring new pathways as its “neural network” produces outcomes, learns and repeats.
The humans who make up BottoDAO vote on which fragment they like best. When the vote is over, the most popular fragment is published as an official Botto artwork on the Ethereum blockchain.
Botto
The vastness of Botto’s training dataset gives the bot considerable canonical material, referred to by Hudson as “latent space.” According to Botto's homepage, the bot has had more exposure to art history than any living human we know of, simply by nature of its massive training dataset of millions of images. Because it is autonomous, gently nudged by community feedback yet free to explore its own “memory,” Botto cycles through periods of thematic interest just like any artist.
“The question is,” Hudson finds himself asking alongside fellow BottoDAO members, “how do you provide feedback of what is good art…without violating [Botto’s] agency?”
Currently, Botto is in its “paradox” period. The bot is exploring the theme of opposites. “We asked Botto through a language model what themes it might like to work on,” explained Hudson. “It presented roughly 12, and the DAO voted on one.”
No, AI isn't equal to a human artist - but it can teach us about ourselves
Some within the artistic community consider Botto to be a novel form of curation, rather than an artist itself. Or, perhaps more accurately, Botto and BottoDAO together create a collaborative conceptual performance that comments more on humankind’s own artistic processes than it offers a true artistic replacement.
Muriel Quancard, a New York-based fine art appraiser with 27 years of experience in technology-driven art, places the Botto experiment within the broader context of our contemporary cultural obsession with projecting human traits onto AI tools. “We're in a phase where technology is mimicking anthropomorphic qualities,” said Quancard. “Look at the terminology and the rhetoric that has been developed around AI — terms like ‘neural network’ borrow from the biology of the human being.”
What is behind this impulse to create technology in our own likeness? Beyond the obvious God complex, Quancard thinks technologists and artists are working with generative systems to better understand ourselves. She points to the artist Ira Greenberg, creator of the Oracles Collection, which uses a generative process called “diffusion” to progressively alter images in collaboration with another massive dataset — this one full of billions of text/image word pairs.
Anyone who has ever learned how to draw by sketching can likely relate to this particular AI process, in which the AI is retrieving images from its dataset and altering them based on real-time input, much like a human brain trying to draw a new still life without using a real-life model, based partly on imagination and partly on old frames of reference. The experienced artist has likely drawn many flowers and vases, though each time they must re-customize their sketch to a new and unique floral arrangement.
Outside of the visual arts, Sasha Stiles, a poet who collaborates with AI as part of her writing practice, likens her experience using AI as a co-author to having access to a personalized resource library containing material from influential books, texts and canonical references. Stiles named her AI co-author — a customized AI built on GPT-3 — Technelegy, a hybrid of the word technology and the poetic form, elegy. Technelegy is trained on a mix of Stiles’ poetry so as to customize the dataset to her voice. Stiles also included research notes, news articles and excerpts from classic American poets like T.S. Eliot and Dickinson in her customizations.
“I've taken all the things that were swirling in my head when I was working on my manuscript, and I put them into this system,” Stiles explained. “And then I'm using algorithms to parse all this information and swirl it around in a blender to then synthesize it into useful additions to the approach that I am taking.”
This approach, Stiles said, allows her to riff on ideas that are bouncing around in her mind, or simply find moments of unexpected creative surprise by way of the algorithm’s randomization.
Beauty is now - perhaps more than ever - in the eye of the beholder
But the million-dollar question remains: Can an AI be its own, independent artist?
The answer is nuanced and may depend on who you ask, and what role they play in the art world. Curator and multidisciplinary artist CoCo Dolle asks whether any entity can truly be an artist without taking personal risks. For humans, risking one’s ego is somewhat required when making an artistic statement of any kind, she argues.
“An artist is a person or an entity that takes risks,” Dolle explained. “That's where things become interesting.” Humans tend to be risk-averse, she said, making the artists who dare to push boundaries exceptional. “That's where the genius can happen."
However, the process of algorithmic collaboration poses another interesting philosophical question: What happens when we remove the person from the artistic equation? Can art — which is traditionally derived from indelible personal experience and expressed through the lens of an individual’s ego — live on to hold meaning once the individual is removed?
As a robot, Botto cannot have any artistic intent, even while its outputs may explore meaningful themes.
Dolle sees this question, and maybe even Botto, as a conceptual inquiry. “The idea of using a DAO and collective voting would remove the ego, the artist’s decision maker,” she said. And where would that leave us — in a post-ego world?
It is experimental indeed. Hudson acknowledges the grand experiment of BottoDAO, coincidentally nodding to Dolle’s question. “A human artist’s work is an expression of themselves,” Hudson said. “An artist often presents their work with a stated intent.” Stiles, for instance, writes on her website that her machine-collaborative work is meant to “challenge what we know about cognition and creativity” and explore the “ethos of consciousness.” As a robot, Botto cannot have any intent, even while its outputs may explore meaningful themes. Though Hudson describes Botto’s agency as a “rudimentary version” of artistic intent, he believes Botto’s art relies heavily on its reception and interpretation by viewers — in contrast to Botto’s own declaration that successful art is made without regard to what will be seen as popular.
“With a traditional artist, they present their work, and it's received and interpreted by an audience — by critics, by society — and that complements and shapes the meaning of the work,” Hudson said. “In Botto’s case, that role is just amplified.”
Perhaps then, we all get to be the artists in the end.
The coronavirus pandemic exposed significant weaknesses in the country's food supply chain. Grocery store meat counters were bare. Transportation interruptions influenced supply. Finding beef, poultry, and pork at the store has been, in some places, as challenging as finding toilet paper.
In traditional agriculture models, it takes at least three months to raise chicken, six to nine months for pigs, and 18 months for cattle.
It wasn't a lack of supply -- millions of animals were in the pipeline.
"There's certainly enough food out there, but it can't get anywhere because of the way our system is set up," said Amy Rowat, an associate professor of integrative biology and physiology at UCLA. "Having a more self-contained, self-sufficient way to produce meat could make the supply chain more robust."
Cultured meat could be one way of making the meat supply chain more resilient despite disruptions due to pandemics such as COVID-19. But is the country ready to embrace lab-grown food?
According to a Good Food Institute study, GenZ is almost twice as likely to embrace meat alternatives for reasons related to social and environmental awareness, even prior to the pandemic. That's because this group wants food choices that reflect their values around food justice, equity, and animal welfare.
Largely, the interest in protein alternatives has been plant-based foods. However, factors directly related to COVID-19 may accelerate consumer interest in the scaling up of cell-grown products, according to Liz Specht, the associate director of science and technology at The Good Food Institute. The latter is a nonprofit organization that supports scientists, investors, and entrepreneurs working to develop food alternatives to conventional animal products.
While lab-grown food isn't ready yet to definitively crisis-proof the food supply chain, experts say it offers promise.
Matching Supply and Demand
Companies developing cell-grown meat claim it can take as few as two months to develop a cell into an edible product, according to Anthony Chow, CFA at Agronomics Limited, an investment company focused on meat alternatives. Tissue is taken from an animal and placed in a culture that contains nutrients and proteins the cells need to grow and expand. He cites a Good Food Institute report that claims a 2.5-millimeter sample can grow three and a half tons of meat in 40 days, allowing for exponential growth when needed.
In traditional agriculture models, it takes at least three months to raise chicken, six to nine months for pigs, and 18 months for cattle. To keep enough maturing animals in the pipeline, farms must plan the number of animals to raise months -- even years -- in advance. Lab-grown meat advocates say that because cultured meat supplies can be flexible, it theoretically allows for scaling up or down in significantly less time.
"Supply and demand has drastically changed in some way around the world and cultivated meat processing would be able to adapt much quicker than conventional farming," Chow said.
Scaling Up
Lab-grown meat may provide an eventual solution, but not in the immediate future, said Paul Mozdziak, a professor of physiology at North Carolina State University who researches animal cell culture techniques, transgenic animal production, and muscle biology.
"The challenge is in culture media," he said. "It's going to take some innovation to get the cells to grow at quantities that are going to be similar to what you can get from an animal. These are questions that everybody in the space is working on."
Chow says some of the most advanced cultured meat companies, such as BlueNal, anticipate introducing products to the market midway through next year. However, he thinks COVID-19 has slowed the process. Once introduced, they will be at a premium price, most likely available at restaurants before they hit grocery store shelves.
"I think in five years' time it will be in a different place," he said. "I don't think that this will have relevance for this pandemic, but certainly beyond that."
"Plant-based meats may be perceived as 'alternatives' to meat, whereas lab-grown meat is producing the same meat, just in a much more efficient manner, without the environmental implications."
Of course, all the technological solutions in the world won't solve the problem unless people are open-minded about embracing them. At least for now, a lab-grown burger or bluefin tuna might still be too strange for many people, especially in the U.S.
For instance, a 2019 article published by "Frontiers in Sustainable Food Systems" reflects results from a study of 3,030 consumers showing that 29 percent of U.S. customers, 59 percent of Chinese consumers, and 56 percent of Indian consumers were either 'very' or 'extremely likely' to try cultivated meat.
"Lab-grown meat is genuine meat, at the cellular level, and therefore will match conventional meat with regard to its nutritional content and overall sensory experience. It could be argued that plant-based meat will never be able to achieve this," says Laura Turner, who works with Chow at Agronomics Limited. "Plant-based meats may be perceived as 'alternatives' to meat, whereas lab-grown meat is producing the same meat, just in a much more efficient manner, without the environmental implications."
A Solution Beyond This Pandemic
The coronavirus has done more than raise awareness of the fragility of food supply chains. It has also been a wakeup call for consumers and policy makers that it is time to radically rethink our meat, Specht says. Those factors have elevated the profile of lab-grown meat.
"I think the economy is getting a little bit more steam and if I was an investor, I would be getting excited about it," adds Mozdziak.
Beyond crises, Mozdziak explains that as affluence continues to increase globally, meat consumption increases exponentially. Yet farm animals can only grow so quickly and traditional farming won't be able to keep up.
"Even Tyson is saying that by 2050, there's not going to be enough capacity in the animal meat space to meet demand," he notes. "If we don't look at some innovative technologies, how are we going to overcome that?"
By mid-March, Alpha Lee was growing restless. A pioneer of AI-driven drug discovery, Lee leads a team of researchers at the University of Cambridge, but his lab had been closed amidst the government-initiated lockdowns spreading inexorably across Europe.
If the Moonshot proves successful, they hope it could serve as a future benchmark for finding new medicines for chronic diseases.
Having spoken to his collaborators across the globe – many of whom were seeing their own experiments and research projects postponed indefinitely due to the pandemic – he noticed a similar sense of frustration and helplessness in the face of COVID-19.
While there was talk of finding a novel treatment for the virus, Lee was well aware the process was likely to be long and laborious. Traditional methods of drug discovery risked suffering the same fate as the efforts to find a cure for SARS in the early 2000, which took years and were ultimately abandoned long before a drug ever reached the market.
To avoid such an outcome, Lee was convinced that global collaboration was required. Together with a collection of scientists in the UK, US and Israel, he launched the 'COVID Moonshot' – a project which encouraged chemists worldwide to share their ideas for potential drug designs. If the Moonshot proves successful, they hope it could serve as a future benchmark for finding new medicines for chronic diseases.
Solving a Complex Jigsaw
In February, ShanghaiTech University published the first detailed snapshots of the SARS-CoV-2 coronavirus's proteins using a technique called X-ray crystallography. In particular, they revealed a high-resolution profile of the virus's main protease – the part of its structure that enables it to replicate inside a host – and the main drug target. The images were tantalizing.
"We could see all the tiny pieces sitting in the structure like pieces of a jigsaw," said Lee. "All we needed was for someone to come up with the best idea of joining these pieces together with a drug. Then you'd be left with a strong molecule which sits in the protease, and stops it from working, killing the virus in the process."
Normally, ideas for how best to design such a drug would be kept as carefully guarded secrets within individual labs and companies due to their potential value. But as a result, the steady process of trial and error to reach an optimum design can take years to come to fruition.
However, given the scale of the global emergency, Lee felt that the scientific community would be open to collective brainstorming on a mass scale. "Big Pharma usually wouldn't necessarily do this, but time is of the essence here," he said. "It was a case of, 'Let's just rethink every drug discovery stage to see -- ok, how can we go as fast as we can?'"
On March 13, he launched the COVID moonshot, calling for chemists around the globe to come up with the most creative ideas they could think of, on their laptops at home. No design was too weird or wacky to be considered, and crucially nothing would be patented. The entire project would be done on a not-for-profit basis, meaning that any drug that makes it to market will have been created simply for the good of humanity.
It caught fire: Within just two weeks, more than 2,300 potential drug designs had been submitted. By the middle of July, over 10,000 had been received from scientists around the globe.
The Road Toward Clinical Trials
With so many designs to choose from, the team has been attempting to whittle them down to a shortlist of the most promising. Computational drug discovery experts at Diamond and the Weizmann Institute of Science in Rehovot, Israel, have enabled the Moonshot team to develop algorithms for predicting how quick and easy each design would be to make, and to predict how well each proposed drug might bind to the virus in real life.
The latter is an approach known as computational covalent docking and has previously been used in cancer research. "This was becoming more popular even before COVID-19, with several covalent drugs approved by the FDA in recent years," said Nir London, professor of organic chemistry at the Weizmann Institute, and one of the Moonshot team members. "However, all of these were for oncology. A covalent drug against SARS-CoV-2 will certainly highlight covalent drug-discovery as a viable option."
Through this approach, the team have selected 850 compounds to date, which they have manufactured and tested in various preclinical trials already. Fifty of these compounds - which appear to be especially promising when it comes to killing the virus in a test tube – are now being optimized further.
Lee is hoping that at least one of these potential drugs will be shown to be effective in curing animals of COVID-19 within the next six months, a step that would allow the Moonshot team to reach out to potential pharmaceutical partners to test their compounds in humans.
Future Implications
If the project does succeed, some believe it could open the door to scientific crowdsourcing as a future means of generating novel medicine ideas for other diseases. Frank von Delft, professor of protein science and structural biology at the University of Oxford's Nuffield Department of Medicine, described it as a new form of 'citizen science.'
"There's a vast resource of expertise and imagination that is simply dying to be tapped into," he said.
Others are slightly more skeptical, pointing out that the uniqueness of the current crisis has meant that many scientists were willing to contribute ideas without expecting any future compensation in return. This meant that it was easy to circumvent the traditional hurdles that prevent large-scale global collaborations from happening – namely how to decide who will profit from the final product and who will hold the intellectual property (IP) rights.
"I think it is too early to judge if this is a viable model for future drug discovery," says London. "I am not sure that without the existential threat we would have seen so many contributions, and so many people and institutions willing to waive compensation and future royalties. Many scientists found themselves at home, frustrated that they don't have a way to contribute to the fight against COVID-19, and this project gave them an opportunity. Plus many can get behind the fact that this project has no associated IP and no one will get rich off of this effort. This breaks down a lot of the typical barriers and red-tape for wider collaboration."
"If a drug would sprout from one of these crowdsourced ideas, it would serve as a very powerful argument to consider this mode of drug discovery further in the future."
However the Moonshot team believes that if they can succeed, it will at the very least send a strong statement to policy makers and the scientific community that greater efforts should be made to make such large-scale collaborations more feasible.
"All across the scientific world, we've seen unprecedented adoption of open-science, collaboration and collegiality during this crisis, perhaps recognizing that only a coordinated global effort could address this global challenge," says London. "If a drug would sprout from one of these crowdsourced ideas, it would serve as a very powerful argument to consider this mode of drug discovery further in the future."
[An earlier version of this article was published on June 8th, 2020 as part of a standalone magazine called GOOD10: The Pandemic Issue. Produced as a partnership among LeapsMag, The Aspen Institute, and GOOD, the magazine is available for free online.]