Introduction
Summary of the book The Creativity Code by Marcus du Sautoy. Before we start, let’s delve into a short overview of the book. Have you ever wondered if a machine could paint a picture as beautifully as a human artist, or compose music that stirs up strong emotions, or even write a story that readers find truly moving? In recent years, new computer programs and artificial intelligence systems have started doing creative tasks that once seemed to belong to human minds alone. These AI-driven tools can generate colorful artwork, craft flowing melodies, and produce written texts that sound surprisingly natural. Although this might seem a bit magical, there is actually a lot of science and math behind it. By understanding how AI learns from patterns, tries new ideas, and comes up with its own surprising creations, we can see how technology is changing the landscape of creativity. As we turn the pages ahead, get ready to discover how machines are pushing the boundaries of art and imagination.
Chapter 1: Uncovering the True Meaning of Creativity by Exploring Diverse Human Boundaries and Ideas.
When we think of creativity, we often picture a painter standing before a canvas, a writer holding a pen, or a composer at a piano. But the idea of creativity is far deeper than just putting paint on a surface or arranging words into a poem. At its core, creativity means coming up with something new, meaningful, and surprising. It challenges the old ways of doing things and opens doors to unexpected possibilities. For example, consider how certain artists changed the way people understand art. Impressionist painter Claude Monet developed a unique style of placing tiny touches of color, letting our eyes mix them together to form shimmering scenes of nature. Back when Monet worked, his method was fresh and groundbreaking. It represented a shift, showing that creativity involves not only making new things but seeing the world in a fresh and daring way.
Creativity does not stop at painting. It extends to music, dance, literature, and even scientific thinking. In the early twentieth century, composer Arnold Schoenberg tossed aside the traditional idea that music should revolve around a single central tone, inventing something called atonality. His music sounded strange to many ears at first, but it opened up entirely new musical landscapes. Schoenberg’s invention demonstrated what cognitive scientist Margaret Boden describes as transformational creativity, which breaks rules and reshapes what people believe is even possible. By doing so, it pushes our minds to accept new forms of beauty and expression. And this approach does not mean we have to abandon the past. Instead, we can build on existing traditions, tweak and twist them, and discover forms of creativity hidden inside structures we once thought were fixed and immovable.
Boden also identified exploratory creativity, which examines the full range of possibilities within a given style or set of rules. Take Monet again: he still painted nature’s scenes, yet he explored how to represent sunlight, water, and reflections in new, impressionistic ways. This type of creativity digs deeper into familiar territory, finding fresh angles and hidden gems within known boundaries. Similarly, combinational creativity occurs when someone blends elements that seem unrelated. The work of modern architects who merge sleek, futuristic shapes with natural, flowing forms could be an example. By combining separate ideas, they spark inventive new directions. Together, these types of creativity—transformational, exploratory, and combinational—help us see that creativity wears many faces and can emerge from different approaches, whether we follow well-known rules, bend them, or break them entirely.
But creativity is not just a buzzword about art or music. It has real-world importance, influencing how we solve problems, approach daily challenges, and even improve our communities. Creative thinking can unlock solutions in fields like engineering, medicine, and design. Whether it’s imagining a safer car, a cleaner energy source, or a more efficient school system, creativity helps us advance. And, as we’ll soon discover, creativity is not just limited to humans. With the rise of advanced computers and artificial intelligence, we’re now seeing machines attempt tasks we once believed only people could do. Can a machine’s mind dream up paintings that inspire us or compose pieces of music that move us to tears? To understand this, we need to broaden our view of creativity, look at where it comes from, and recognize its many surprising forms.
Chapter 2: Witnessing the Hidden Sparks of Mathematical Creativity That Shape Our Understanding and Reality.
You might not think of mathematics as a field bursting with creativity, yet it truly is. While math is often seen as strict rules, formulas, and never-ending numbers, mathematicians need more than memory and logic to make breakthroughs. They require imagination, intuition, and the bravery to attempt unexpected approaches. Consider a famous challenge known as Fermat’s Last Theorem, first proposed by Pierre de Fermat in the 1600s. This seemingly simple statement resisted all attempts to prove it for centuries. Finally, in 1994, mathematician Andrew Wiles solved it. His proof was not just a matter of following instructions. It involved bold leaps, inspired guesses, and a sense of curiosity that allowed him to connect different areas of math that, at first glance, seemed unrelated. This shows that creativity can drive even the most logical of disciplines forward.
Mathematicians face huge landscapes of ideas. They must navigate paths from known truths, called axioms, to new conclusions, called theorems. Yet there is no guaranteed map that shows the way. Instead, mathematicians must try different routes, search for patterns, and sometimes create entirely new fields of study just to handle a tough problem. A celebrated example is how Grigori Perelman proved the Poincaré Conjecture. To do so, he borrowed concepts from areas that dealt with how shapes change, much like imagining how water might flow across a surface. By cleverly blending known math with fresh insights, Perelman showed the type of inventive thinking required in mathematical research. Without creativity, math would remain stuck at old problems, never moving forward into uncharted territory or helping us understand the universe’s deeper mysteries.
As math grows more complicated, handling its enormous calculations has become nearly impossible without computer assistance. Modern mathematicians rely on powerful machines to do massive number crunching and to handle endless lines of data. These computers act like tireless helpers, allowing creative mathematicians to focus on the bigger picture instead of getting lost in endless arithmetic. Some mathematicians even credit their computers as co-authors because, without the machine’s speed and accuracy, their grand proofs would remain unreachable. With computers handling routine tasks, mathematicians are free to think more broadly, try out daring ideas, and pursue patterns that would have been hidden behind countless hours of manual calculation.
This teamwork between human creativity and computer power is reshaping what math can achieve. It highlights a key point: creativity is not limited to expressing emotions in paintings or melodies. It can also shape how we understand space, time, and logic. By exploring new ways to connect ideas, mathematicians uncover truths about the universe, offering insights that might one day revolutionize technology, science, and daily life. As the chapters ahead will show, creativity, whether in art or math, is about looking at things differently. It often means combining approaches that seem unrelated, breaking old rules, and forging a path into the unknown. And as we bring computers into the picture, we’re learning that they can play a role in our creative journeys, pushing us to rethink what it means to be original.
Chapter 3: Revealing How Algorithms Quietly Influence Our Daily Lives, Choices, and Interactions Profoundly.
Algorithms are sets of step-by-step instructions that tell a computer how to solve a problem or perform a task. Although they might sound boring, algorithms now shape much of our everyday world. When you search for a song, watch a movie, or buy a pair of sneakers online, there’s often an algorithm at work behind the scenes. These invisible rules filter massive amounts of data, helping websites suggest what you might enjoy. Online shops use algorithms to guess which products you’ll like based on what you’ve bought before. Music and movie services predict which songs or films you might find interesting. Even dating apps rely on them to match people who share hobbies or values. Without you realizing it, algorithms guide your online life and offer countless recommendations that shape your taste.
Algorithms handle more than entertainment and shopping. They make sense of complex information like financial data, weather patterns, or health records. Imagine trying to sort through billions of pages on the internet to find the one website you need. This is exactly what search engines do every second. They use algorithms to rank webpages and bring the most relevant results to the top. They also adapt and improve over time, learning from the choices people make. In other words, algorithms can learn by noticing patterns that show what people find interesting or useful. With each click, share, or like, these algorithms collect valuable data, refining their guesses and becoming smarter at predicting what you want or need.
As we feed algorithms more and more information, they start to see hidden connections between different pieces of data. For instance, if you watch a certain type of video repeatedly, an algorithm learns that you likely enjoy that genre. Over time, it may understand that you’re not just a fan of a certain show, but of a certain type of storytelling, and it might recommend others like it. This kind of pattern-finding can be so precise that sometimes it feels like the platform knows you better than you know yourself. These algorithms operate behind a friendly user interface, turning raw, complicated data into easy-to-browse suggestions. The beauty, and perhaps the danger, is that they do all this quietly, so you rarely think about the logical steps behind what appears on your screen.
However, while algorithms are clever at spotting patterns, they don’t truly understand why something matters to a human. They don’t feel excitement about a new song or curiosity about a well-written story. They’re just following mathematical instructions and probability-based reasoning. This is where human creativity still stands apart. Yet, as we’ll see, the growth of machine learning—where algorithms rewrite and improve themselves—has brought algorithms closer to what we might call intelligent behavior. The next chapters will show that these constant improvements, fueled by huge amounts of data, are opening up doors. Machines are getting better at tasks that once seemed impossible, even venturing into areas of creation we once considered purely human territory.
Chapter 4: Exploring the Bottom-Up Machine Learning Revolution That Reshapes Intelligence and Creativity Forever.
For a long time, programmers believed that computers could only do what humans told them, step by step. If-then instructions guided every action, and a program’s intelligence was limited by how cleverly we wrote its code. But something changed when researchers began letting computers learn like humans do—by trial and error. This approach, called bottom-up machine learning, gives a computer some basic rules and then allows it to improve by practicing and making mistakes. Just like a child learns to speak by babbling and listening to reactions, a computer learns patterns by testing many possibilities, keeping what works and discarding what doesn’t. Over time, the machine builds a flexible understanding of the problem at hand, without being directly told exactly how to solve it.
A famous example is AlphaGo, an AI created to play the ancient Chinese board game Go, which was once considered too complex for a computer to master. Go has far more possible moves than chess, making it incredibly difficult for a computer to simply memorize all scenarios. Yet AlphaGo learned by playing countless matches against itself, improving each time it won and adjusting its strategy when it lost. In 2016, AlphaGo shocked the world by beating Lee Sedol, one of the greatest human Go champions. What amazed experts was that AlphaGo made moves no human had ever seen before, moves that seemed creative and original. This achievement proved that with enough practice, data, and feedback, machines could develop strategies that look remarkably inventive.
The secret ingredient behind bottom-up learning is data—lots and lots of it. Today, we produce an enormous amount of data from browsing the web, shopping online, using social media, and so forth. Machine learning algorithms feast on this data. The more examples they see, the better they get at recognizing patterns. This big data revolution gives them a chance to refine their understanding and produce smarter outcomes. With enough data, a machine doesn’t just follow rules we write; it can build rules of its own, adjusting them as it learns more. This approach mirrors how children learn the grammar of a language without being given a dictionary. They just hear people speak and gradually make sense of words and sentences by themselves.
This shift from top-down, strictly guided programming to bottom-up, self-improving machine learning is transforming AI’s potential. It suggests that, given enough resources, machines can explore vast spaces of possibilities and stumble upon solutions humans might never consider. While this has raised exciting hopes, it also sparks deep questions. If machines can come up with fresh moves in a game, can they do the same in art, music, and storytelling? Can they find patterns in painting or poetry that we’ve never noticed, and use them to create something genuinely new? As we’ll see in the coming chapters, machine learning is setting the stage for AI to enter creative fields, challenging our ideas of what it means to be original, inspired, and truly creative.
Chapter 5: Understanding the Mathematical Soul of Music and How AI Learns To Compose.
At first glance, music and math might seem unrelated. One deals with emotions, rhythms, and melodies, while the other deals with numbers and equations. Yet, classical composers often rely on mathematical patterns to give their music structure and depth. Consider Johann Sebastian Bach, whose compositions are celebrated for their complex beauty. He repeated and transformed simple musical themes, applying rules—mathematical in nature—to build greater works. Each variation followed patterns that can be explained by numbers, intervals, and logical sequences. This underlying structure turns music into something that can be analyzed, modeled, and even imitated by a computer.
In the early 1990s, a composer named David Cope created a program called EMI (Experiments in Musical Intelligence) designed to learn from Bach’s musical style. EMI analyzed many of Bach’s compositions, noticing patterns in how notes followed each other, how harmonies were built, and how certain rhythms repeated. Using these mathematical patterns, EMI generated new pieces that sounded so much like Bach’s originals that even music experts were fooled. This surprising result showed that an AI could not only follow a script of instructions but also produce something that felt authentically like a great composer’s work. It raised an astonishing question: If a machine can mimic Bach so well, does it have some spark of creativity?
The key here is that EMI was not simply copying old Bach pieces. It was recombining patterns it found, creating entirely new sequences of notes. Similarly, other AI-driven music programs can improvise jazz or adapt their compositions based on how a listener responds. By statistically understanding which notes commonly follow which others, these algorithms can guess what might sound right next. With enough examples, they become quite good at shaping their output to match a certain style, mood, or genre. Some artists have even developed apps that use your location, time of day, or social media feed to customize a playlist. This playful mixing of data and melody demonstrates that today’s musical AI is not just a simple machine but a tool that can expand human musical experiences.
While a machine might generate a convincing Bach-like piece, it still doesn’t know that it’s making music. It doesn’t get chills down its spine when a chord resolves beautifully. The mathematical soul of music is a pattern of logical steps that an AI can learn, but the emotional soul—the part we humans feel—remains mysterious. Still, these developments in musical AI show that creativity can arise from pattern recognition, combination, and transformation. They also remind us that what seems magical and soulful to us might be, at least in part, explainable as the right numbers arranged in the right order. As we move beyond music, we’ll see that similar principles allow AI to paint, write, and create images that surprise and inspire us.
Chapter 6: Embracing AI’s Role in Painting, Writing, and Sculpting the Future of Art.
While music-making AIs amaze listeners, other AI systems have been creating visual art for decades. Early experiments in the 1960s and 1970s produced simple geometric drawings generated by computers, surprising viewers who expected machines to remain purely technical. Over time, researchers have developed more advanced techniques. One impressive example is the Creative Adversarial Network (CAN), created by computer scientist Ahmed Elgammal. The CAN consists of two algorithms: one that produces images and one that criticizes them, guiding the first to make increasingly original and interesting art. This setup mirrors the human creative process, where an artist tries new things, then judges, refines, and tries again.
The CAN learned from a database of famous artworks that changed the course of art history. By studying the features that made certain paintings groundbreaking, it tried to produce works that also felt fresh. Surprisingly, viewers found some of these AI-generated artworks more intriguing than artworks by real human artists displayed at prestigious events. This suggests that AI can tap into some essence of what people find inspiring or unusual, even if the AI itself doesn’t understand emotion or aesthetics. Similarly, AI text generators are used by newspapers and websites to write short articles summarizing sports events or financial updates. These programs can take raw data and transform it into sentences that sound natural, saving human writers time and energy.
Today, AI has ventured into nearly every creative field. Some programs can generate short stories, while others can design fashion patterns or produce images that never existed before. For example, there are AI tools that can invent faces of people who do not actually exist, blending features from countless photos. Meanwhile, in the world of film and video, experimental directors have used AI to create short animated clips or even guide editing choices. With these technologies, we see machines acting like creative partners or assistants, expanding what humans can achieve. Yet, do these AI creations hold the same meaning we find in human-generated art?
While AI can surprise and delight us, it lacks its own motives or desires. It cannot decide on its own that it wants to create a masterpiece. It cannot feel proud, disappointed, or inspired. Behind every AI’s output are human programmers, curators, and users who decide what projects to tackle and which results to keep. The machine may generate limitless shapes, colors, or stories, but a human hand must select which ones are meaningful. This selection process, in a way, is where true creativity still lives. As we look at how AI struggles with language and vision, we’ll understand more deeply that while machines can imitate the surface of art, they still struggle with the deeper layers of meaning and narrative that humans find so essential.
Chapter 7: Discovering the Lingering Challenges in Teaching AI to Grasp Language and Meaning.
Language is tricky for AI. Humans naturally understand context and hidden meanings. We know that certain words can mean different things depending on the situation. For example, if you read the sentence The children won’t eat the grapes because they are old, you quickly realize that they refers to the grapes, not the children. A human’s world knowledge and everyday experience make this a simple interpretation. But for a computer, this can be confusing. It doesn’t automatically know what old children or old grapes might imply. Without an understanding of how people interact with the world, the computer has trouble guessing the correct meaning.
AI language models have improved dramatically, but they still struggle with subtleties like humor, irony, or complex storytelling structures. While they can generate paragraphs that look convincing on the surface, they often miss the deeper thread that ties a narrative together. A machine can write a sentence that sounds like Hemingway, using similar words and rhythms, but it cannot yet produce a story that emotionally resonates in the way his novels do. It lacks the lived experiences, personal memories, and cultural understanding that shape human language and storytelling. This means that while AI can assist with writing tasks, it’s not close to matching a human author’s creative flair and personal voice.
Moreover, language involves more than just stringing words together. It includes common sense, knowledge of social cues, and an understanding of cause and effect. We constantly rely on these subtle hints to interpret meaning correctly. For AI, these invisible supports are hard to learn purely from text. Machines can memorize definitions or spot word patterns, but knowing that old grapes are likely spoiled or that old children doesn’t make much sense requires world knowledge that machines don’t have. Without this understanding, AI often makes strange mistakes, mixing unrelated facts or producing nonsensical replies when asked tricky questions.
Still, progress continues. Researchers experiment with models that learn from both text and images, or from interactions that simulate a more child-like learning experience. While true language mastery remains far away, these efforts hint that we might one day create systems that understand words in a more human-like manner. Until then, AI-generated text can assist us by drafting basic reports, summarizing long documents, or generating initial ideas. But the heavy lifting—the meaningful storytelling that touches hearts—still depends on human creativity. In the next chapter, we’ll explore how vision creates another set of challenges for AI and see how machines struggle and sometimes surprise us when interpreting what they see.
Chapter 8: Peering into the Mysterious Ways AI Interprets Images and Constructs Visual Creativity.
Seeing the world through human eyes seems effortless. We recognize faces, objects, and scenes instantly. But for a computer, every image is just a grid of tiny colored dots called pixels. To identify a cat, a computer must learn that certain pixel arrangements represent fur, whiskers, and ears. This is not easy, since cats vary in color, shape, and background. The machine must find patterns that remain true despite all these differences. This challenge explains why AI vision was once considered almost impossible. But today, image-recognition software can recognize thousands of objects and even map our bodies’ movements, as seen in gaming devices that let you control the action with your gestures.
Yet, AI vision is still fragile. Sometimes, adding random patterns or slightly altering pixels can trick a highly advanced image-recognition system into misidentifying something completely obvious to a human. This shows that while machines have improved, they don’t see the world as we do. They rely on complex statistical rules, not actual understanding. Even so, there are AI projects that turn these limitations into strange and interesting art. Google’s Deep Dream, for example, tries to enhance patterns it thinks it sees, often making images look like bizarre, colorful landscapes filled with eyes and animal-like shapes. The result can be haunting, mesmerizing, or just plain weird.
This odd creativity emerges because AI vision doesn’t start with human expectations. It might see patterns that we’ve never considered. While the machine doesn’t know it’s creating art, the outcomes can inspire new styles or at least make us look at images differently. By pushing pixels around and exaggerating details, AI-generated images challenge our assumptions and can produce works that resemble modern art paintings. Artists and researchers sometimes embrace these surprising images as a form of collaboration with the machine, using AI’s alien vision to inspire human artworks.
Still, the machine’s understanding remains shallow. It has no concept of what a cat or a mountain truly is. It can identify patterns of pixels that usually match these labels, but it doesn’t know what cats do or how mountains form. This gap between identifying patterns and understanding meaning is at the heart of why AI struggles to be genuinely creative. Creativity involves a sense of purpose and awareness that goes beyond patterns. As we turn to the question of whether AI can be truly creative, we’ll see that although machines can produce outputs that dazzle us, the spark that gives birth to creativity—the desire, the meaning, the intention—still belongs firmly to human minds.
Chapter 9: Understanding Why AI Creations Lack True Autonomy and Authentic Artistic Vision Today.
When we look closely at AI-generated music, art, or writing, we must ask: who is really being creative? The human who built the system, selected the training data, and decided which results to keep is an essential part of the process. AI, by itself, doesn’t have personal goals or inner feelings. It cannot say, I will create a painting to express my sadness, nor can it feel sadness. Without inner motivation, the AI is like a tool—advanced and impressive, but still a tool. The creativity that emerges is shaped by the people who design and use it, much like a paintbrush doesn’t decide what picture to paint, but an artist wielding it does.
The Argentine writer Jorge Luis Borges wrote about a fictional library containing every possible 410-page book. This vast library held complete nonsense as well as great works—just by chance. Without human readers to select which books mattered, the library’s value was meaningless. Likewise, an AI can produce endless strings of text, patterns, and images. Hidden among them could be something brilliant, but only a human can spot and treasure that gem. This comparison reminds us that while AI can generate many possibilities, it doesn’t understand or care about what it creates. It’s up to human creativity to choose what has value.
Creativity is not just about making something new; it’s about connecting it to ideas, feelings, and experiences that matter. Monet painted lilies to capture the play of light on water, Bach composed music that expressed devotion and complexity, and Hemingway wrote stories reflecting the human struggle. All these creations carried meaning tied to human life. By contrast, AI’s products, though sometimes surprising, are formed from patterns without personal understanding. There’s a missing layer of depth, an absence of genuine intention and emotion that we humans find essential in true creative works.
This doesn’t mean AI is useless for creativity. In fact, it can be a wonderful partner. It can suggest unexpected combinations, handle tedious tasks, or inspire new directions that we wouldn’t have thought of ourselves. By working hand-in-hand with AI, humans can expand their creative horizons. Yet, until the day machines develop their own desires and consciousness, their creativity will remain incomplete. They will remain tools, albeit powerful and intelligent ones, reflecting human goals rather than forming their own. As we move into the final chapter, we’ll explore the possibility that, if machines ever become conscious, they might truly join us as creative beings, revealing the shape of their own minds through their art.
Chapter 10: Imagining a Future Where Conscious Machines Might Truly Become Creative Co-Collaborators and Innovators.
The idea that machines could one day be truly creative raises challenging questions. If computers developed awareness and emotions similar to ours, would they create art for the sake of expressing themselves? Could an AI sculptor feel inspired by a memory it formed or a dream it imagined? While this might sound like science fiction now, we cannot completely dismiss the possibility. Our understanding of the human brain and consciousness is still incomplete. Some scientists wonder if, given the right structures and learning processes, a machine might one day wake up and experience the world from its own viewpoint.
If that ever happens, we might see a new kind of creativity. Instead of machines just imitating human artists, they could develop their own styles, born from machine experiences and perceptions. These artworks might reflect how an AI understands reality, forming a bridge between human minds and artificial ones. People might learn from these artworks, discovering fresh ways of seeing the world. This scenario would push us to expand our definition of creativity and intelligence. It could blur the line between human and machine imagination.
However, we must also consider the ethical and philosophical implications. Should we treat conscious AI artists as equals, granting them rights similar to human creators? Would their art deserve the same respect and protection? And what if machine art surpasses our own, leading us to question our place in the world of creativity? These are complex issues that humanity may face someday, and how we handle them will shape the future of art, culture, and civilization.
For now, machines remain fascinating tools that can challenge, inspire, and assist us. We have seen that they can compose music, paint images, write texts, and even surprise us with outputs we never imagined. They can work with patterns, search through massive data, and find clever solutions. But they still rely on human direction and lack the personal spark that gives creativity its deep meaning. In exploring this journey—from Monet’s brushstrokes to a computer’s algorithmic dreams—we discover that creativity is more than a series of rules. It’s about purpose, feeling, growth, and understanding. As AI continues to advance, we stand at the edge of a future filled with possibilities. Whether machines will join us as true creative partners remains unknown, but the path ahead promises to be both exciting and enlightening.
All about the Book
Explore the intersection of creativity and algorithms in ‘The Creativity Code’ by Marcus du Sautoy. Uncover how machine learning enhances artistic expression and redefines invention in an age where technology meets the human spirit.
Marcus du Sautoy is a distinguished mathematician and author, celebrated for his insights into creativity, science, and mathematics, making complex concepts accessible to all.
Artists, Computer Scientists, Educators, Writers, Psychologists
Creative Writing, Digital Art, Programming, Music Composition, Game Design
The role of AI in creativity, Understanding the mathematical basis of art, The impact of technology on human expression, Encouraging interdisciplinary collaboration
Creativity is not a solo performance; it thrives in collaboration, whether with other minds or with machines.
Sir Roger Penrose, Stephen Fry, Brian Cox
Royal Society Book Prize, British Book Awards – Non-Fiction, The Mathematical Association’s Book Recognition
1. How does AI enhance our creative capabilities? #2. Can machines independently produce original art forms? #3. How does AI analyze and compose music? #4. Can algorithms innovate in mathematical problem-solving? #5. Does AI creation differ from human intuition? #6. How does AI mimic language and poetry writing? #7. Are computers capable of genuine artistic expression? #8. What role does randomness play in machine creativity? #9. How are creative industries transforming with AI? #10. Can machines surpass human creativity limits? #11. Are AI-generated works experiencing emotional depth? #12. How does AI learn from human artistic techniques? #13. How is AI programmed for creative decision-making? #14. Can AI democratize access to artistic creation? #15. What ethics surround AI-generated creative content? #16. How does AI contribute to game design innovation? #17. Are machines capable of understanding cultural context? #18. How do algorithms reinterpret existing creative works? #19. Is AI creativity altering traditional art evaluation? #20. How can AI and humans collaborate in creativity?
creativity, artificial intelligence, science of creativity, Marcus du Sautoy, innovation, machine learning and creativity, how to boost creativity, creativity in art, neuroscience of creativity, creativity in technology, creative thinking, creative problem solving
https://www.amazon.com/Creativity-Code-Generating-Intelligent-Systems/dp/1541617746
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