Painting kaleidoscopic pieces with algorithms, doing your makeup in apps, generating non-existent individuals through programs – even though you would not be a nerd of technology, it is likely that you have heard related to these new developments of AI in your feeds. A lot of articles are related to AI’s attack on music, art, and further fields of human ingenuity.
Maybe AI art turns out to be such an interesting subject since creativity has been a major unique feature of humans. As a matter of fact, people’s desire to demonstrate themselves through the use of art may be more prevalent than contemporary people. For example, archeologists have discovered carved shells at the age of five hundred thousand in Java and they assume that it is an outcome of Homoerectus – the ancestor of us and the Neanderthals.
If creativity exists before human beings, is this likely to surpass people? Is this rapid growth of AI making the path to the further stage of evolution – creating smart device creators who make literature, artworks, and music by themselves?
In order to answer this question, Marcus du Sautoy who is both a mathematician and art enthusiast takes us on a trip, from the simple basics of computing to the math of music and the close future of art-making AI.
Chapter 1 – Creativity is mainly about exploring, merging, and changing existing structures into novel things.
Let’s be realistic: machines are more intelligent compared to human beings in a lot of ways. Computers can save more facts, and store larger statistics and they are far greater in spelling. Mainly the only thing people can still comfort themselves with is creativeness. Certainly, a machine can’t make a joke, create harmony, or author a publication – or can it? In order to respond to this question, let’s first understand what creativity means.
Being ingenious is equal to creating new things, amazing and appreciated. Claude Monet who is a French painter is well-known for his wonderful water lily paintings – however, those paintings have not only been beautiful. Putting blotches on blotches of paint rather than utilizing conventional brush hits, Monet displayed to us a novel method to cherish the interaction of color and light. That unique style of painting, known as impressionism stirred groups of creators and assisted in creating the path of abstract art from figurative art.
Similar to how our perspectives on art have transformed over hundreds of years, our perspective of ingenuity continuously changed. We frequently evaluate an original movie by the extent it varies from the ones that preceded it. Let’s look at the early 20th hundredth year symphonist Schönberg. Symphonists that came before Schönberg ignored that as the major key, tone, in other words, had been the foundation of any work of art.
Schönberg confidently ignored this principle to create atonality – giving the world surprising listening pleasures. Margaret Boden a cognitive scientist refers to this kind of revolution as life-changing creativity. This may totally knock down the things we consider possible with a certain discipline.
Additionally, Boden mentioned the other two kinds of creativeness. She tells that Monet’s performance shows exploratory creativity, meaning it demonstrates the thing is likely inside the discipline principle. Still, Monet portrayed water lilies with a symbolic method; however, he made it with a totally new, impressionist, approach.
Combinatorial ingenuity has been the skill that combines structures that might not fit together externally. Zaha Hadid is a contemporary architect who transforms her love for abstract art into impossible-looking, curvy structures. For example, she designed the Centre of Heydar Aliyev in Azerbaijan, and it seems less similar to a structure than a huge shell. Also, her structures have been a good practical example of applications of creativity. It can be seen that creativity isn’t only for artists alone.
Chapter 2 – Human creativity inspires art – and maths as well.
Creativity is equal to twisting and even sometimes going against the principles in order to create something new. However, this ability isn’t restricted to literature, art, and music alone. Observe meticulously, and you will see ingenuity in areas you would not think it would be, like the writer’s area: mathematics.
In order to know the way mathematicians have been ingenious, we need to first know the things they make.
Mathematicians utilize logical reasoning to verify axioms and theorems. Axioms have been mathematical terms that we think are correct. For instance, we think that:
√1 is equal to 1.
Theorems are novel mathematical statements that mathematicians have to prove. Perhaps we would like to demonstrate that:
√1 is not equal to 1.
In order to make that, we need to utilize logical moves to link the present axioms related to squares with the novel theorem. However, advanced mathematics has basically been further than implementing principles and sole logic.
Similar to great art, great mathematics needs thinking beyond the ordinary and explaining convincing tales. Math experts would not like to demonstrate hypotheses that are just uninteresting and apparent. They would like to provide evidence of bold, unpredicted theorems which intensify our grasp of this world. In order to do this, creativity and intuition are needed.
Grigori Perelman showed these characteristics at the time he showed evidence to the Poincaré supposition – a well-known theorem that defines all the diverse geometrical forms in this world. In order to prove that theorem, Perelman implemented the rule that is entirely a distinct field of maths. Utilizing the method in which liquid moves on the surface, he could explain the whole variety of figures that is likely to be existing. His connective ingenuity created a novel and amazing understanding of our universe.
However, even genius such as Perelman cannot accomplish work solely. With each accomplished evidence, the math field is becoming more complicated. For a field that is as ancient as civilization, that signifies that a lot of computations are currently so complex that the biggest math expert could not find solutions to those using paper and pen.
The mathematicians of today require computers for processing the huge numbers they are working with. Those devices have turned out essential. As a matter of fact, Doron Zeilberger an Israeli mathematician insists on putting his device, which is named S. B. Ekhad by him, as a co-author for his academic publications. By releasing them from boring measurements and decreasing the human error margin, computers enable math people to consider more imaginatively than no time.
Chapter 3 – Algorithms shape the current life.
Math experts and computers possess a similar thing in common which is following sequences of logical guidelines to get to their preferred result. The rules programmed in devices are known as algorithms. These may be considered as “if-then” clauses that explain to the computer the way to act. For instance, the email filter can have the principle, “if the email you got has the vocabulary ‘Viagra’, put that in Spam”.
However, algorithms make a lot further than categorizing the emails you got.
That shouldn’t be surprising. Companies such as Spotify, Amazon, and Netflix utilize algorithms for flooding you with products as recommendations. The algorithms have attempted to guess the songs, products, or movies you would like, according to your former selections.
More controversially, algorithms currently choose romantic mates for people. A dating site known as OkCupid assesses the personality characteristics you have, and what you dislike and like in choosing your partner. In a current study that was conducted among couples that married between the years 2005 – 2012, the couples that met each other online looked more delighted compared to the couples that met each other in the real life. Do numbers understand something we cannot understand?
Well, algorithms work mostly by asking different types of inquiries related to huge numbers of data. Did you think about the way a website pops up in the first place as a result of your search on Google? The website has a search algorithm that calculates the worth of websites by inquiring about the number of other websites that are related to them.
Afterward, using the exact evaluation, it inquires about the worth of those further websites. When your corporate website is related to a lot of high-worth websites such as CNN, that would rank more at the top with a search. That produces a complex cross-assessment structure that needs gathering and contrasting further data than people’s brains could ever withstand.
Besides, a lot of algorithms develop smarter the more you relate with them. It is likely that you have seen the greater you make use of Spotify, Netflix, or Amazon, the more those services appear to “know” your taste. It is because each time you make use of them, you provide their algorithms extra data for functioning with. The algorithms improve and grasp to read the data in a greater way.
Gradually, Netflix will comprehend that you have seen a particular movie not since you are a fan of romantic comedy, but since you love Tom Hanks. Rather than leading you to a romantic comedy, the platform may make you watch Forrest Gump with its recommendation. Algorithms that can improve like that have transformed the expectations of AI.
Chapter 4 – The introduction of machine learning in the “bottom-up” form has transformed the Artificial Intelligence field.
Prior to machine learning’s emergence, computer specialists were all gathered in the conviction that “One may take only what he/she placed in”. This means that an algorithm could be just as intelligent as the individual who programmed it. Hence, what was the thing that transformed coders’ minds? That had been a computer playing games.
“Go” has been an old board game from China that needs creativity, intelligence, and skill. 2 players play after one another putting white and black stone dots on the 19 to 19 board. Their aim is to take the rival’s dots by encircling them using their dots. This had long been thought to be impractical to instruct a computer on the way to play Go since this game needs complicated pattern recognition and because the number of possible games is infinite.
However, around 2016, in a computer vs. human challenge made around the world, Hassabis’s computer AlphaGo won the leading human champion of Go named Lee Sedol with a 4-1 score. What was the way AlphaGo could attain the impractical?
Hassabis’ team and himself used machine learning methods to create and improve the Go-player device. They programmed a few simple principles into AlphaGo. Afterward, they allowed the computer to write the remaining rules itself through the use of trials and errors. In programming, that’s known as the “bottom-up” method, and that’s the foundation of what’s called machine learning. Similar to humans, game after game, playing, AlphaGo learned how to win in a Go game.
Anytime the computer made an attack that made it win, it renewed its chances to be further possible to make a similar attack once more. On the other hand, at the times it made an attack that made it lose, and that turn out less possible to attack similarly once more. When AlphaGo challenged Lee Sedol, the computer had already created schemes no Go player could ever consider.
The further data the AI such as AlphaGo owns to instruct itself, the more intelligent it gets. Hence, Machine learning would be indebted a lot to a large sum of data, that could be available nowadays – 90% of those had been formed during the final 5 years! That bulk information, combined with the skill of codes to revise themselves utilizing that knowledge, has created the likelihood of machines getting more intelligent compared to us.
Chapter 5 – Math, music, as well as programs, are intimately related.
David Cope who is a classical composer delivered Bach in 1993, an album that contained novel piano works characteristic of 18th hundredth-year symphonist J. S. Bach. However, the works were not authored by Bach, nor were those authored by Cope. Those were authored by Emmy, the musical software produced by Cope for mimicking Bach’s style of composition.
That AI accomplished a great job that even expert Bach fans were deceived. During a performance at the Oregon University, listeners confused a work from him with the indigenous Bach – considering a less renowned composition of the original Bach was fake.
Is there a way for a program to create a piece of music that is more similar to Bach compared to Bach originally?
Algorithms are used by classical composers to make musical difficulty. They begin with a basic theme or melody, and afterward, they change this plot according to numerical principles. With the use of math, they form differences and supplementary audio to create the symphony.
Symphonists with a powerful signature manner have been attracted to specific quantitative motives compared to others. For example, Mozart mostly utilized the Alberti pattern of bass. That pattern has 3 notes in the order of 1-3-2-3-1-3-2-3. The program was taught to select the quantitative patterns of Bach, and she could utilize them to create symphonies that were similar to how Bach sounded.
A different music AI is an instrument known as the Continuator and it can select as well as duplicate jazz music’s patterns. Analyzing several jazz compositions, its algorithm understood that several sequences and notes are possible to pursue other ones. The Continuator has learned to improvise using the likelihoods measured from the training data. When you open a jazz piece with it, that may maintain that riff similar to how an individual jazz player could do.
Also, pop music has been discovering the potential of algorithms of music. The 2016 Massive Attack album Heligoland is with the app known as Fantom and this app utilizes your place, Twitter feed, and time zone to form a continuous, customized mixture of songs for you to listen to. Having a further democratic way, Brian Eno the experimental symphonist has created his music apps allowing you to relate with and adjust his ambient symphonies.
Currently, you are aware that computers and music are connected via the math tongue of algorithms, maybe it’s simpler to observe the way a program may compose a song. However, music isn’t just the sole artistic field machines have learned.
Chapter 6 – AIs are as of now used to produce art, literature, and music.
We are now aware of computers that make up jazz riffs and write classical music. It appears that musicians are making sufficient utilization of the advancing abilities of AI already. What does the last part of the world of art?
Devices that produce visible art aren’t new. In early 1965, Georg Nees, a Siemens engineer, coded a device to produce its own drawings. Nees’s program began from a stable dot on the display, drawing twenty-three linked strokes of various lengths in various ways. The outcome was captivating sequences of geometrical patterns.
A computer scientist named Ahmed Elgammal from Rutgers University recently created a more advanced art-making AI. Elgammal created a GAN which is able to sort and create patterns of visible art.
The GAN has been a structure that has algorithms and an algorithm grasps and modifies as a result of the response of another. The GAN of Elgammal emulates the 2 rival structures of humans’ ingenious minds: which are the originator and the criticizer. One algorithm creates images, while the other one criticizes their uniqueness.
The criticizer program was taught with WikiArt data to recognize pictures that tagged instants of big creative transformation in the history of art, like water lilies from Monet. It then makes use of this information for evaluating and directing the pictures created by the originator algorithm. It appears that humans approve of its decision – guests at the 2016 Art Basel regarded the work of GAN as further inspirational compared to the people’s artworks displayed!
Also, AIs have gotten into the writing world. A lot of broadcast corps are now utilizing text processing algorithms to produce news headlines. With the raw data they have, those programs may now write short, articulate texts that accompany the arrangement of a normal news bulletin. That’s particularly beneficial for stock market reports and sports reports, the place where the number of data produced every day turned out very unexciting for people to withstand.
Modern writing programs are like musical AIs and they may even grasp writing with the pattern of a specific author. The AI may hammer out a paragraph, similar to a page or chapter of Ernest Hemingway by studying word choice and sentence structure. As a matter of fact, the writer of this book declares that a three hundred fifty-word part of his book was authored by a computer code!
As we’ll see in the next chapter, though, image-making and storytelling still have some irregular challenges for AI.
Chapter 7 – Vision and language still offer big problems for AI.
Think of this statement: “The children won’t eat the grapes because they are old.” Who or what is old? Different from you, a human has some experience with children and grapes – a computer can’t answer this question easily. It has no ultimate approach to determine who the “they” in this statement signifies.
Normal language has a lot of vagueness. How we understand a statement mostly depends on the setting, needing prior knowledge of the universe. Because computers lack the kind of natural knowledge that humans gain through experience, linguistic nuance is mostly lost on them. Due to this exact reason, they find it difficult to make a meaningful narrative structure. Computer programs may be able to write a few words that seem like Hemingway, however, they are not close to understanding at all, and talk less of weaving, a story like that of The Old Man and the Sea.
Aside from language, vision is another big problem area for AI. As it turns out, computers are very bad at seeing the bigger picture. This is because visual programs recognize images by asking questions about the pixels that form the image. Let’s say for every picture of, for instance, a cat, the composition of pixels is totally different. The program needs to learn to relate the pixels to one another and judge if those relations are symbolic of a cat, or another thing.
An advanced visual recognition such as that can be found in the Xbox One Kinect motion sensor. The Kinect can recognize and draw 31 different body parts by comparing the depth and distance of each pixel to those around it. Yet, its skills are nowhere close to human vision. This is the reason why a lot of websites make you choose images of cars, road signs, or cats in order to identify that you’re not a robot.
Curiously, AIs can use their odd nonhuman vision to form captivating art. Google’s DeepDream program functions by feeding an AI an unclear picture, then asking it to increase the image’s features according to what it anticipates it to be. Because the AI is taught from images on Google search that usually consists of people, animals, or other objects, it is likely to over-interpret the blurry image– seeing eyes, hands, and faces when there are not even there.
The outcomes are colorful, psychedelic images that could be considered modern art. However, does that mean that DeepDream is an artist?
Chapter 8 – AIs are beneficial creative tools; however, they’re not yet ingenious by themselves.
Although there’s still a chance for technical development, no suspicion that AIs are starting to make captivating music, writing, and art pieces. Designs such as the kaleidoscopic pictures of DeepDream amaze coders after those programs even, evidence that, in the recent operation of computers, you may even obtain further than what you have given.
However, inventiveness has been related to further than handling input and producing output. Getting things of inventive value out of algorithmic computations of computers still needs humans.
J. L. Borges who is an Argentine writer offers a beneficial comparison inside the concise story “Babel Library”. In his story, he refers to a library that has all four hundred ten-page books that you could never imagine, starting with the initial four hundred ten pages of War & Peace from Tolstoy and ending with four hundred ten pages full of the “N” letter. However, since this library owns only any publication that has four hundred ten pages, most of the books like the book of “N” have only a small value. It needs humans to look for books having meaning, finding a jewel similar to War & Peace.
Similar to the way the Babel Library has countless books without concern for their content, devices may handle countless data without bothering related to the meaning of it. However, creativity is totally about meaning for humans. Areas in which we discover our common humanity are art, music, and literature and this can create new understandings of our world.
Furthermore, we are ingenious with the free will we have. Bach, Hemingway, and Monet did not produce their outputs since somebody else demanded them to do so. These people produced what they did since they sensed the desire to convey themselves. As far as abilities, there has not been an AI that produced any art pieces yet that has its willpower. Those codes can paint, can write as well as compose since people have coded them for that. This is the reason that it is not correct to define them as creative by themselves. Nevertheless, that is humans’ creativity, producing these programs initially.
Machines possibly will not be creative similar to humans until they become conscious just like humans. For now, there’s no means of whether or the way machine awareness would occur. However, when this happens; maybe the literature, art, and music produced by aware devices would give us the best understanding of artificial minds.
The Creativity Code: How AI Is Learning to Write, Paint and Think by Marcus du Sautoy Book Review
The abilities of modern AI surpass a lot of our former expectations all thanks to machine learning. Although it is still a struggle for them to identify images and understand language, computers already produce captivating pieces of art, music, and literature. But, until they learn to do so consciously and with a motive, they will still be creative tools rather than being creative agents themselves.
Know the algorithms in charge of this life.
Huge companies such as Amazon, Google, and Netflix utilize algorithms that are always evolving to guide your consumer options. Also, they track the way you browse external to their domain to determine what they can sell to you. Through learning about the data those corporations gather related to you, and the rules with which those companies’ algorithms work, you could measure the impact they own on the life you live – and make cautious choices to avoid it.