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.
“Virtual Biopsies” May Soon Make Some Invasive Tests Unnecessary
At his son's college graduation in 2017, Dan Chessin felt "terribly uncomfortable" sitting in the stadium. The bouts of pain persisted, and after months of monitoring, a urologist took biopsies of suspicious areas in his prostate.
This innovation may enhance diagnostic precision and promptness, but it also brings ethical concerns to the forefront.
"In my case, the biopsies came out cancerous," says Chessin, 60, who underwent robotic surgery for intermediate-grade prostate cancer at University Hospitals Cleveland Medical Center.
Although he needed a biopsy, as most patients today do, advances in radiologic technology may make such invasive measures unnecessary in the future. Researchers are developing better imaging techniques and algorithms—a form of computer science called artificial intelligence, in which machines learn and execute tasks that typically require human brain power.
This innovation may enhance diagnostic precision and promptness. But it also brings ethical concerns to the forefront of the conversation, highlighting the potential for invasion of privacy, unequal patient access, and less physician involvement in patient care.
A National Academy of Medicine Special Publication, released in December, emphasizes that setting industry-wide standards for use in patient care is essential to AI's responsible and transparent implementation as the industry grapples with voluminous quantities of data. The technology should be viewed as a tool to supplement decision-making by highly trained professionals, not to replace it.
MRI--a test that uses powerful magnets, radio waves, and a computer to take detailed images inside the body--has become highly accurate in detecting aggressive prostate cancer, but its reliability is more limited in identifying low and intermediate grades of malignancy. That's why Chessin opted to have his prostate removed rather than take the chance of missing anything more suspicious that could develop.
His urologist, Lee Ponsky, says AI's most significant impact is yet to come. He hopes University Hospitals Cleveland Medical Center's collaboration with research scientists at its academic affiliate, Case Western Reserve University, will lead to the invention of a virtual biopsy.
A National Cancer Institute five-year grant is funding the project, launched in 2017, to develop a combined MRI and computerized tool to support more accurate detection and grading of prostate cancer. Such a tool would be "the closest to a crystal ball that we can get," says Ponsky, professor and chairman of the Urology Institute.
In situations where AI has guided diagnostics, radiologists' interpretations of breast, lung, and prostate lesions have improved as much as 25 percent, says Anant Madabhushi, a biomedical engineer and director of the Center for Computational Imaging and Personalized Diagnostics at Case Western Reserve, who is collaborating with Ponsky. "AI is very nascent," Madabhushi says, estimating that fewer than 10 percent of niche academic medical centers have used it. "We are still optimizing and validating the AI and virtual biopsy technology."
In October, several North American and European professional organizations of radiologists, imaging informaticists, and medical physicists released a joint statement on the ethics of AI. "Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future," reads the statement, published in the Journal of the American College of Radiology. "The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes."
Overreliance on new technology also poses concern when humans "outsource the process to a machine."
The statement's leader author, radiologist J. Raymond Geis, says "there's no question" that machines equipped with artificial intelligence "can extract more information than two human eyes" by spotting very subtle patterns in pixels. Yet, such nuances are "only part of the bigger picture of taking care of a patient," says Geis, a senior scientist with the American College of Radiology's Data Science Institute. "We have to be able to combine that with knowledge of what those pixels mean."
Setting ethical standards is high on all physicians' radar because the intricacies of each patient's medical record are factored into the computer's algorithm, which, in turn, may be used to help interpret other patients' scans, says radiologist Frank Rybicki, vice chair of operations and quality at the University of Cincinnati's department of radiology. Although obtaining patients' informed consent in writing is currently necessary, ethical dilemmas arise if and when patients have a change of heart about the use of their private health information. It is likely that removing individual data may be possible for some algorithms but not others, Rybicki says.
The information is de-identified to protect patient privacy. Using it to advance research is akin to analyzing human tissue removed in surgical procedures with the goal of discovering new medicines to fight disease, says Maryellen Giger, a University of Chicago medical physicist who studies computer-aided diagnosis in cancers of the breast, lung, and prostate, as well as bone diseases. Physicians who become adept at using AI to augment their interpretation of imaging will be ahead of the curve, she says.
As with other new discoveries, patient access and equality come into play. While AI appears to "have potential to improve over human performance in certain contexts," an algorithm's design may result in greater accuracy for certain groups of patients, says Lucia M. Rafanelli, a political theorist at The George Washington University. This "could have a disproportionately bad impact on one segment of the population."
Overreliance on new technology also poses concern when humans "outsource the process to a machine." Over time, they may cease developing and refining the skills they used before the invention became available, said Chloe Bakalar, a visiting research collaborator at Princeton University's Center for Information Technology Policy.
"AI is a paradigm shift with magic power and great potential."
Striking the right balance in the rollout of the technology is key. Rushing to integrate AI in clinical practice may cause harm, whereas holding back too long could undermine its ability to be helpful. Proper governance becomes paramount. "AI is a paradigm shift with magic power and great potential," says Ge Wang, a biomedical imaging professor at Rensselaer Polytechnic Institute in Troy, New York. "It is only ethical to develop it proactively, validate it rigorously, regulate it systematically, and optimize it as time goes by in a healthy ecosystem."
How Emerging Technologies Can Help Us Fight the New Coronavirus
In nature, few species remain dominant for long. Any sizable population of similar individuals offers immense resources to whichever parasite can evade its defenses, spreading rapidly from one member to the next.
Which will prove greater: our defenses or our vulnerabilities?
Humans are one such dominant species. That wasn't always the case: our hunter-gatherer ancestors lived in groups too small and poorly connected to spread pathogens like wildfire. Our collective vulnerability to pandemics began with the dawn of cities and trade networks thousands of years ago. Roman cities were always demographic sinks, but never more so than when a pandemic agent swept through. The plague of Cyprian, the Antonine plague, the plague of Justinian – each is thought to have killed over ten million people, an appallingly high fraction of the total population of the empire.
With the advent of sanitation, hygiene, and quarantines, we developed our first non-immunological defenses to curtail the spread of plagues. With antibiotics, we began to turn the weapons of microbes against our microbial foes. Most potent of all, we use vaccines to train our immune systems to fight pathogens before we are even exposed. Edward Jenner's original vaccine alone is estimated to have saved half a billion lives.
It's been over a century since we suffered from a swift and deadly pandemic. Even the last deadly influenza of 1918 killed only a few percent of humanity – nothing so bad as any of the Roman plagues, let alone the Black Death of medieval times.
How much of our recent winning streak has been due to luck?
Much rides on that question, because the same factors that first made our ancestors vulnerable are now ubiquitous. Our cities are far larger than those of ancient times. They're inhabited by an ever-growing fraction of humanity, and are increasingly closely connected: we now routinely travel around the world in the course of a day. Despite urbanization, global population growth has increased contact with wild animals, creating more opportunities for zoonotic pathogens to jump species. Which will prove greater: our defenses or our vulnerabilities?
The tragic emergence of coronavirus 2019-nCoV in Wuhan may provide a test case. How devastating this virus will become is highly uncertain at the time of writing, but its rapid spread to many countries is deeply worrisome. That it seems to kill only the already infirm and spare the healthy is small comfort, and may counterintuitively assist its spread: it's easy to implement a quarantine when everyone infected becomes extremely ill, but if carriers may not exhibit symptoms as has been reported, it becomes exceedingly difficult to limit transmission. The virus, a distant relative of the more lethal SARS virus that killed 800 people in 2002 to 2003, has evolved to be transmitted between humans and spread to 18 countries in just six weeks.
Humanity's response has been faster than ever, if not fast enough. To its immense credit, China swiftly shared information, organized and built new treatment centers, closed schools, and established quarantines. The Coalition for Epidemic Preparedness Innovations, which was founded in 2017, quickly funded three different companies to develop three different varieties of vaccine: a standard protein vaccine, a DNA vaccine, and an RNA vaccine, with more planned. One of the agreements was signed after just four days of discussion, far faster than has ever been done before.
The new vaccine candidates will likely be ready for clinical trials by early summer, but even if successful, it will be additional months before the vaccine will be widely available. The delay may well be shorter than ever before thanks to advances in manufacturing and logistics, but a delay it will be.
The 1918 influenza virus killed more than half of its victims in the United Kingdom over just three months.
If we faced a truly nasty virus, something that spreads like pandemic influenza – let alone measles – yet with the higher fatality rate of, say, H7N9 avian influenza, the situation would be grim. We are profoundly unprepared, on many different levels.
So what would it take to provide us with a robust defense against pandemics?
Minimize the attack surface: 2019-nCoV jumped from an animal, most probably a bat, to humans. China has now banned the wildlife trade in response to the epidemic. Keeping it banned would be prudent, but won't be possible in all nations. Still, there are other methods of protection. Influenza viruses commonly jump from birds to pigs to humans; the new coronavirus may have similarly passed through a livestock animal. Thanks to CRISPR, we can now edit the genomes of most livestock. If we made them immune to known viruses, and introduced those engineered traits to domesticated animals everywhere, we would create a firewall in those intermediate hosts. We might even consider heritably immunizing the wild organisms most likely to serve as reservoirs of disease.
None of these defenses will be cheap, but they'll be worth every penny.
Rapid diagnostics: We need a reliable method of detection costing just pennies to be available worldwide inside of a week of discovering a new virus. This may eventually be possible thanks to a technology called SHERLOCK, which is based on a CRISPR system more commonly used for precision genome editing. Instead of using CRISPR to find and edit a particular genome sequence in a cell, SHERLOCK programs it to search for a desired target and initiate an easily detected chain reaction upon discovery. The technology is capable of fantastic sensitivity: with an attomolar (10-18) detection limit, it senses single molecules of a unique DNA or RNA fingerprint, and the components can be freeze-dried onto paper strips.
Better preparations: China acted swiftly to curtail the spread of the Wuhan virus with traditional public health measures, but not everything went as smoothly as it might have. Most cities and nations have never conducted a pandemic preparedness drill. Best give people a chance to practice keeping the city barely functional while minimizing potential exposure events before facing the real thing.
Faster vaccines: Three months to clinical trials is too long. We need a robust vaccine discovery and production system that can generate six candidates within a week of the pathogen's identification, manufacture a million doses the week after, and scale up to a hundred million inside of a month. That may be possible for novel DNA and RNA-based vaccines, and indeed anything that can be delivered using a standardized gene therapy vector. For example, instead of teaching each person's immune system to evolve protective antibodies by showing it pieces of the virus, we can program cells to directly produce known antibodies via gene therapy. Those antibodies could be discovered by sifting existing diverse libraries of hundreds of millions of candidates, computationally designed from scratch, evolved using synthetic laboratory ecosystems, or even harvested from the first patients to report symptoms. Such a vaccine might be discovered and produced fast enough at scale to halt almost any natural pandemic.
Robust production and delivery: Our defenses must not be vulnerable to the social and economic disruptions caused by a pandemic. Unfortunately, our economy selects for speed and efficiency at the expense of robustness. Just-in-time supply chains that wing their way around the world require every node to be intact. If workers aren't on the job producing a critical component, the whole chain breaks until a substitute can be found. A truly nasty pandemic would disrupt economies all over the world, so we will need to pay extra to preserve the capacity for independent vertically integrated production chains in multiple nations. Similarly, vaccines are only useful if people receive them, so delivery systems should be as robustly automated as possible.
None of these defenses will be cheap, but they'll be worth every penny. Our nations collectively spend trillions on defense against one another, but only billions to protect humanity from pandemic viruses known to have killed more people than any human weapon. That's foolish – especially since natural animal diseases that jump the species barrier aren't the only pandemic threats.
We will eventually make our society immune to naturally occurring pandemics, but that day has not yet come, and future pandemic viruses may not be natural.
The complete genomes of all historical pandemic viruses ever to have been sequenced are freely available to anyone with an internet connection. True, these are all agents we've faced before, so we have a pre-existing armory of pharmaceuticals and vaccines and experience. There's no guarantee that they would become pandemics again; for example, a large fraction of humanity is almost certainly immune to the 1918 influenza virus due to exposure to the related 2009 pandemic, making it highly unlikely that the virus would take off if released.
Still, making the blueprints publicly available means that a large and growing number of people with the relevant technical skills can single-handedly make deadly biological agents that might be able to spread autonomously -- at least if they can get their hands on the relevant DNA. At present, such people most certainly can, so long as they bother to check the publicly available list of which gene synthesis companies do the right thing and screen orders -- and by implication, which ones don't.
One would hope that at least some of the companies that don't advertise that they screen are "honeypots" paid by intelligence agencies to catch would-be bioterrorists, but even if most of them are, it's still foolish to let individuals access that kind of destructive power. We will eventually make our society immune to naturally occurring pandemics, but that day has not yet come, and future pandemic viruses may not be natural. Hence, we should build a secure and adaptive system capable of screening all DNA synthesis for known and potential future pandemic agents... without disclosing what we think is a credible bioweapon.
Whether or not it becomes a global pandemic, the emergence of Wuhan coronavirus has underscored the need for coordinated action to prevent the spread of pandemic disease. Let's ensure that our reactive response minimally prepares us for future threats, for one day, reacting may not be enough.