A Portrait of the Artist as a Young Algorithm

AI-generated art from emerging products like Dall-e and Midjourney begs the question of what is creativity, what is art, who makes it, and who owns it.

At this year’s Colorado State Fair, it wasn’t Grandma Nancy or Aunt Bea that walked away with the most talked about prize. It was a video game artist named Jason Allen.

The category Allen entered was “digitally manipulated photography,” but Allen didn’t exactly labor pixel-by-pixel, or just do a little post-production on a photo.  Actually, he didn’t do much art at all. The main thing he did was give some text descriptors to midjourney, an AI-generating art program.  He described the scene in some detail. The AI turned his prompts into images for the final version. (He did do a bit of retouching using other digital tools.)

Théâtre D’opéra Spatial by Jason Allen. Jason Allen via Discord

Allen reportedly made it clear when he entered the contest that his submission was AI-generated. But apparently the judges didn’t pay much heed. What has ensued since his win is a heated debate about what constitutes art. And by the way—what does it mean to be an artist?

 Two of the most popular AI-generated art creation tools are Dall-e 2, from San Francisco nonprofit OpenAI, and Midjourney, created by a small private research lab, are. They both are moving out of stealth and beta modes and becoming used more widely.  In the process, they are sending into a tizzy the worlds of fine art, graphic illustration, advertising, and more or less anything that ever required a human artist’s touch.

While they each use variations on related technology, the basic idea is to train computers by showing them art and images — millions of millions of them — everything from classical art to Instagram shots. And the images are described using words.  Finally, the AI tools are trained to understand the relationship between objects and ideas. That’s why you can ask Dall-e to create things like “two hamsters exploring a calculator”. The software is even trained on artistic styles — pointillism, cubism, realism, or the style of particular artists, like Claude Monet.

For readers who want to get deep down in the weeds here’s a good look at how Dall-e 2 works. The animation is, in its own right, a work of art.

Living Artists Take Umbrage

Enter Greg Rutowski, a video game artist. His style is called out by Dall-e and Midjourney prompts using his name. He is not a happy camper.  His art is being copied and imitated over and over. According to MIT Technology Review, Rutowski’s name has been used as a prompt more than Michaelangelo’s. Rutowski wants programs like Dall-e and Midjourney to remove “live artists” like himself from their toolkits. So does artist Karla Ortiz, who caught the art world’s attention after she tweeted about her art being used as a prompt.

And it’s not just artists that might face the question of commercial extinction, if tools like this become more widely used. Meta has released background on Make-A-Video and Evoke Music uses text prompts to create and suggest musical compositions. One can imagine a scenario when “human-created” art, video, and music could become relegated to Etsy-like outposts on the web for connoisseurs, while most commercial art becomes machine-generated.

The Humanity Tax

“A Robot holding a paintbrush and painting a picture” is the prompt for this piece of “art” created by Louis Rosenberg using MidJourney. He argues that neither the program nor he should be thought of as being truly creative.

AI-generated art from emerging products like these begs the question of what is creativity, what is art, who makes it, and who owns it. Says Louis Rosenberg, a pioneer in AI and Chief Scientist at Unamimous.AI, a group-think/swarm technology powered by AI:  “These generative AI systems will be able to produce in minutes what would take human writers and artists, and composers hours or days or weeks to produce.” The technology itself is remarkable, he says, but it is not creative.

 

In an interview Rosenberg tells of asking the generative AI program midjourney to create an image of a “robot holding a paintbrush painting a picture.”. (Seen at top.)

The image that’s ultimately created by the computer does convey emotion, is artistic, and certainly has a style, but not only is AI not creative it’s not even intelligent. “The AI has no idea what a robot is or a paintbrush. It has no artistic sensibilities. It doesn’t even know what these words mean.” says Rosenberg.

“And I’m not an artist,” he continues. “I just fed some text prompts into the computer.” The artwork, he contends, is created using a statistical process that correlates imagery with the words and phrases in the prompt.

This piece of art was created by instructing Dall-e to create a painting of “a fox in a field at sunrise in the style of Claude Monet”.

So where did this “creativity” come from? It comes from the fact that these machines have been trained on millions and millions of images and millions and millions of text prompts.  “What it’s really doing,” argues Rosenberg “is capturing collective humanity’s artistic sensibilities and regurgitating them.”

Rosenberg goes so far as to suggest that we might want to consider a “humanity tax” doled out to the people whose collective sensibilities contributed to the algorithmic output. Think of it as sort of a Universal Basic Income to compensate us all, as machines whittle away at even more of our jobs. (Maybe Andrew Yang was on to something after all.)

My own very first attempt at Midjourney (below) produced astounding results that have only gotten better as I’ve become more precise with my prompts (though I’m thinking Midjourney has not had many calls for cornucopias.)

Once again, technology finds itself ahead of the system of rules and regulations (IP, trademarks, copyrights)  meant to protect creators. Both Dall-e and Midjourney will charge a monthly fee for using its services after an initial feemium trial. Both plan to allow you to commercialize your results.

This week, not coincidentally, Disney’s latest job posting hinted at the future.  It wasn’t a job offer for animators or illustrators. It was for tech-savvy lawyers who are about to deal with some thorny issues.

My first attempt at creating art through text prompts using Midjourney. My prompt was “fall root vegetables in a cornucopia spilling out on to a table”. To try it yourself head Discord’s Midjourney area and put yourself in a newbie room.

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New and Old Technologies and Future Warfare

Russia’s attack on Ukraine is seen as a test case for the wars of the future. Some critical insights are already emerging.

Some seven months into Russia’s current attack on Ukraine, the war is being characterized as “establishing principles for the twenty-first century.” In many ways, this conflict is being seen as a test case for the wars of the future. While it is too soon to draw specific lessons, some critical insights are already emerging.

One is that warfare is not experiencing the flashy technological transformations that some futurists expected. Instead, changes are incremental and focused. Technology is being tweaked to enable greater access to and sharing of information; sensor-based technologies are appearing in a variety of settings; and operational data is being collected and used to train artificial intelligence (AI) systems.

One often discussed aspect of the current fighting in Ukraine is the availability of open-source information. Watching Russian military manoeuvres just before the 2022 incursion, The Economist declared that open-source intelligence was leading to an “era of transparent warfare.”

Both sides in the Ukraine war can now more effectively monitor events on the ground. What is particularly interesting, however, is that the Ukrainian military is also profiting from technology that connects troops in the war zone to a wider group of technologically savvy individuals. Open-source experts and “online volunteers” are intercepting communications among Russian military units in Ukraine and providing that information to the Ukrainian military. Such practices will likely become common in future warfare, with skilled individuals from any location in the world virtually joining battlefields and supporting militaries on the ground.

Advances in monitoring and the constant sharing of data pose an interesting challenge for militaries. Gen. Mark Milley, the chairman of the United States Joint Chiefs of Staff, notes that “In a world of ubiquitous sensors, militaries must move away from large, conspicuous force deployments toward smaller units that change location rapidly and don’t attract notice.”

This is not a great shift for Western military forces, which have for some years been engaging in “light footprint” actions. These involve the use of remote technologies, such as drones; training local forces that are then deployed; and the deployment of small special-operations teams. Even with this shift, however, there is a growing recognition that new technologies are making it difficult to avoid detection and monitoring by a wide array of individuals, states, and groups.

The war in Ukraine has also been labelled the “smartphone war,” with many observers, including soldiers on the ground, contributing to the narrative of the fighting under way via social media. Other footage is captured by commercial satellites and drones. Providing up-to-the-minute footage is one way for militaries to counter disinformation. The combatants in Ukraine have also employed commercial drones in reconnaissance.

Military use of both smartphones and commercial drones illustrates the “dual use” nature of technological development; technology developed for civilian use is then used in warfare. In other words, advances in commercial technologies may well shape what shows up on the battlefield. While this point might seem obvious, it reinforces the idea above that weapons in the future might not be as “futuristic” as we have imagined.

It is interesting to note how Ukraine, the weaker military power, is adapting dual-use technologies to address asymmetry. For example, commercial drones become weapons when explosives are strapped on them. There is evidence that Ukraine has used these “homemade kamikaze drones” against Russian forces. Ukraine has also put out a general request for the donation of commercial drones (dubbed “dronations”) and crowdfunded for the purchase of military drones.

Military drones, particularly loitering munitions (or kamikaze drones), are taking on a new importance in current conflicts. What many militaries learned from the 2020 Nagorno-Karabakh conflict was that drones and loitering munitions are critical in modern military arsenals. The conflict in Ukraine seems to confirm this lesson. Electronic countermeasures against both commercial and military drones will likely keep pace with such developments.

We also see how military operations in Ukraine are providing data used to train AI systems; this is likely the case for both Russian and Western militaries. Indeed, Russia recently announced that it had formed a department specifically focused on developing weapons with AI. The United States, arguably the world leader in military AI, has already established the Chief Digital and Artificial Intelligence Office, which is intended to further the use of AI across the U.S. military. A challenge for incorporating AI into defence applications and weapon systems is the collecting of recent high-quality operational data; recent and current conflicts are providing such training data for more and more militaries.

We need to understand that all of these so-called technological advancements are making war zones more dangerous for civilians, but do nothing to aid civil society organizations (CSOs) in ensuring that civilians are protected. While CSOs might have a greater access to information and shared data that helps them to monitor military actions, remote access will not allow them to provide critical on-site humanitarian support.

Greater use of drones and loitering munitions will almost certainly result in more incidents of intentional and unintentional targeting of civilians and civilian infrastructure. How are we to protect civilians from such attacks or support them after they occur?

Ultimately there is no technological magic wand that will eliminate the hard work needed to rebuild societies after conflicts end. The ideal solution is to do the hard work in advance and prevent conflicts from escalating to armed violence. So far, nothing beats the oldest tool in the toolbox: diplomacy.

Branka Marijan is a Centre for International Governance Innovation contributor and a senior researcher at Project Ploughshares. Her work examines concerns regarding the development of autonomous weapons systems and the impact of artificial intelligence and robotics on security provision and trends in warfare.

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He Got A Rare Disease Diagnosis So He Rethought The Whole System

While working to understand not only his own rare disease but also how the broader healthcare industry develops treatments for rare diseases as a whole, Onno Faber uncovered the building blocks of his new company.

Onno Faber never expected to become a medical innovator. He was living the American dream. Born and raised in The Netherlands, Faber embraced entrepreneurship early, as the founder of a software engineering company during high school. He eventually moved to the U.S. to grow a technology company in the mobile communication space. But soon after arriving in Silicon Valley, he began to lose hearing in his left ear. “As it turned out, there was a brain tumor on my hearing nerve and further scans and research showed additional tumors in my brain and spinal cord”, Faber recalls. The eventual diagnosis was a rare genetic disorder called NF2, which leads to tumors of the nervous system including in the brain, spinal cord, and nerves.

Rare Diseases Are Actually Common

“This is when I first learned about rare diseases,” says Faber. “My sensory nerves were threatened, I was deaf on the left side and there was the potential for serious vision problems and mobility issues.” After consultations with doctors, Faber was told there were no drugs for his disease and eventually found a treatment through his own research and community engagement that stabilized the tumor growth and insured he would not lose hearing, mobility, and vision for the time being. Faber recalls saying to himself back then: “Instead of thinking about it as a devastating diagnosis, how can I turn it, spin it around, and create a positive impact for all people finding themselves in a similar situation?”

onno faber

Faber researched his condition obsessively, working to understand not only his own rare disease but how the broader healthcare industry diagnoses and treats rare diseases. “The first thing I learned was that rare diseases are actually very common,” he says. “When you hear that one in 50,000 has my particular disease, that sounds ‘rare’. But as soon as I learned there are thousands of rare diseases, the reality is that one in 10 people in the US has a rare disease–or about 20-30 million people.”

Despite the surprisingly large market and the fact that 95 percent of rare diseases have no FDA-approved therapy, Faber also came to learn that there is a little commercial incentive to work on rare disease treatments. This revelation inspired a new-found passion and the founding of a rare disease “drug hunting” company built around patients and families dealing with these diseases. “We asked ourselves, what types of communities need to be built?” Faber recalls. “How can we align these communities to make the 10,000 small problems a big problem and ultimately create an algorithm and engine to scale rare disease diagnosis and treatment?”

Your Friendly, Neighborhood Sequencing Machine

As Faber continued his research, another tumor appeared, pressing against his brain stem. But Faber is a natural entrepreneur. Once that tumor was successfully removed, Faber didn’t let it bring him down. Instead, he decided to take things further. “A friend of mine was a computational biologist and had a sequencing machine in his living room,” Faber recalls. “That’s the advantage of living in Silicon Valley.” Faber was able to sequence and analyze his own DNA, and that led to a project with Google. As Faber remembers, “We had 400 people coming for a weekend to hack on the data and we eventually found the first drug that I tried to repurpose for my condition.”

This experience led Faber to continue working on how to combine sequencing and drug discovery into one continuous process. He launched Rarebase in 2020 with his co-founder. “The goal,” he says, “was to find ways to make the rare disease a scalable problem, and we wanted to start with the patient and the families.”

Building Knowledge at Scale

“Today, sequencing is mostly used for disease diagnosis,” Faber continues. “At Rarebase, we also use sequencing to find treatments. We can sequence every single experiment and, for example, can determine what a particular drug does to the expression of the DNA in a specific cell type. That allows us to create hypotheses around which drugs might reverse the underlying biology of a rare disease.” The goal, ultimately, is to use the platform to matchmake between drugs and as many of the more than 7,000 rare genetic diseases as possible.

The Road Ahead

Ask Faber what he is looking forward to, and he says, “the technology will evolve to become cheaper and more scalable so we can generate more and more data,” he continues, “and, ultimately, help rare disease patients find treatments so they can have a better quality of life. The technology is there. We just have to enable access so that rare disease patients can benefit from it too.”

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