Artificial Intelligence in Art
Balchugov George
117 group
Contents:
1. Introduction……………………………………………………………..…3
2. How we got here.……………………………………………………….….4
3. AI-generated art at a competition……………………………………..…4
4. The end of creativity?………………………………………………….…5
5. Conclusion…………………………………………………………………6
6. Sources………………………………………………………………..……7
7. Words………………………………………………………………………8
Introduction
It all started already in 1763. British mathematician Thomas Bayes derived a formula that makes it possible to estimate the probability of events empirically, and recalculate the probability, taking into account both previously known information and data from new observations. This became one of the first approaches in the future of machine learning and the history of the development of artificial intelligence in general.
The next point in the chronology is 1842. This year Ada Lovelace, the Countess, by the way, helped Charles Babbage to describe a computing machine and wrote what can be considered the first working program for a computer. But, in addition, Ada predicted that the future device would not only perform calculations, but also write music and paintings.
We are moving on and we are in the 20th century. Karel Chapek uses the word "robot" in 1921, and during the Second World War three events happen at once – in the UK Alan Turing comes up with the famous test, Gray Walter creates the first robots, and in the USA Warren McCulloch and Walter Pitts publish an article that became the basis for the development of neurotechnology and turned the language of psychology into a means of describing a machine and machine intelligence.
Then it became fashionable to write about whether machines are capable of thinking: Edmund Berkeley in 1949, Isaac Asimov with his "I, robot" in the 50th. And finally, at a conference at Dartmouth University in 1956, the words "artificial intelligence" were uttered, and three years later - "machine learning".
Let's skip further AI victories on other fronts and look only at what he did in the field of art. In 1973, American professor Harold Cohen created AARON, a computer program that independently created paintings. And we still don't know if it's true or not? Maybe Cohen drew it all himself.
We're moving on. 2013 and Painting Fool. A tool that can not only read, but also draw. At one of the exhibitions Painting Fool read an article from the Guardian newspaper on the war in Afghanistan, pulled out keywords like NATO, troops, British, selected ready-made images for these words in the database and created collage paintings from them. As the designers would say, it made a kind of moodboard, that is, a poster reflecting the mood of the article.
In 2015, another interesting experiment. Google introduced the Inception algorithm, which, having practiced in identifying objects by visual cues, having a database of photographs of clouds and objects with random shapes, was able to generate pictures that resemble both Walt Disney and Peter Brueghel the Elder's fantasies – for example, hybrid creatures "pig-snail", "camel-bird" and "dog-fish".
The more striking example of computational art appeared in 2018. A group of French developers Obvious "fed" neural networks 15,000 portraits of famous and little-known artists of different eras. The algorithm has drawn its own portraits, one of which is "Portrait of Edmond Belami", it was sold at Christie's auction for $432,500. From that moment on, AI officially became a participant in the art world.
How we got here
You could say this revolution began in June 2020, when a company called OpenAI achieved a big breakthrough in AI with the creation of GPT-3, a system that can process and generate language in much more complex ways than earlier efforts. You can have conversations with it about any topic, ask it to write a research article or a story, summarise text, write a joke, and do almost any imaginable language task.
In 2021, some of GPT-3’s developers turned their hand to images. They trained a model on billions of pairs of images and text descriptions, then used it to generate new images from new descriptions. They called this system DALL-E, and in July 2022 they released a much-improved new version, DALL-E 2.
Like GPT-3, DALL-E 2 was a major breakthrough. It can generate highly detailed images from free-form text inputs, including information about style and other abstract concepts.
Google too has a text-to-image model, called Imagen, which supposedly produces much better results than DALL-E and others. However, Imagen has not yet been released for wider use so it is difficult to evaluate Google’s claims.
AI-generated artsat competitions
Around the time, a smaller company called Midjourney released a model that more closely matched DALL-E 2’s capabilities. Though still a little less capable than DALL-E 2, Midjourney has lent itself to interesting artistic explorations. It was with Midjourney that Jason Allen generated the artwork that won the Colorado State Art Fair competition.
Mr. Allen’s work, “Théâtre D’opéra Spatial,” took home the blue ribbon in the fair’s contest for emerging digital artists — making it one of the first A.I.-generated pieces to win such a prize, and setting off a fierce backlash from artists who accused him of, essentially, cheating.
These apps have made many human artists understandably nervous about their own futures — why would anyone pay for art, they wonder, when they could generate it themselves? They have also generated fierce debates about the ethics of AI-generated art, and opposition from people who claim that these apps are essentially a high-tech form of plagiarism.
The end of creativity?
What does it mean that you can generate any sort of visual content, image or video, with a few lines of text and a click of a button? What about when you can generate a movie script with GPT-3 and a movie animation with DALL-E 2?
And looking further forward, what will it mean when social media algorithms not only curate content for your feed, but generate it? What about when this trend meets the metaverse in a few years, and virtual reality worlds are generated in real time, just for you?
These are all important questions to consider.
What makes the new breed of AI tools different, some critics believe, is not just that they’re capable of producing beautiful works of art with minimal effort. It’s how they work. Apps like DALL-E 2 and Midjourney are built by scraping millions of images from the open web, then teaching algorithms to recognize patterns and relationships in those images and generate new ones in the same style. That means that artists who upload their works to the internet may be unwittingly helping to train their algorithmic competitors.
Perhaps in a world where anyone can generate any images, graphic designers as we know them today will be redundant. However, history shows human creativity finds a way. The electronic synthesiser did not kill music, and photography did not kill painting. Instead, theycatalysednewartforms.
A new type of artist is even emerging in what some call “promptology”, or “prompt engineering”. The art is not in crafting pixels by hand, but in crafting the words that prompt the computer to generate the image: a kind of AI whispering.
Conclusion
Controversy over new art-making technologies is nothing new. Many painters recoiled at the invention of the camera, which they saw as a debasement of human artistry. (Charles Baudelaire, the 19th-century French poet and art critic, called photography “art’s most mortal enemy.”) In the 20th century, digital editing tools and computer-assisted design programs were similarly dismissed by purists for requiring too little skill of their human collaborators.
We live in an attention economy that thrives on extracting screen time from users; in an economy where automation drives corporate profit but not necessarily higher wages, and where art is commodified as content; in a social context where it is increasingly hard to distinguish real from fake; in sociotechnical structures that too easily encode biases in the AI models we train. In these circumstances, AI can easily do harm.
How can we steer these new AI technologies in a direction that benefits people? Maybe one way to do this isdesignAI that collaborates with, rather than replace, humans.
Sources:
· https://www.latimes.com/projects/artificial-intelligence-generated-art-ownership-bias-dall-e-midjourney/
· https://theconversation.com/ai-art-is-everywhere-right-now-even-experts-dont-know-what-it-will-mean-189800
· https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html
· https://scientificrussia.ru/articles/iskusstvennoe-iskusstvo-sposoben-li-ii-sozdat-sedevr
Words:
Artificial– искусственный;
Utter –произнести;
Capable –способный;
Inception–зарождение;
Identify–распознавать;
Cue–подсказка;
Striking –поразительный;
Participant–участник;
Effort –попытка;
Evaluate –оценить;
Claim –заявление;
Fierce –яростный, свирепый;
Backlash –обратная негативная реакция;
Accuse –обвинять, осуждать;
Essentially –по сути;
Curate –выбирать и организовывать;
Breed –поколение, порода;
Scrape –соскребать;
Unwittingly–невольно;
Redundant –избыточный;
Bias–предубеждение, предвзятость;
Thrive–процветать;
Commodify –превратить в товар;
Distinguish –различать, разграничивать;