[Student IDEAS] by Michelle Diaz - Master in Management Student at ESSEC Business School

Overview:

An article that looks into the relationship between AI and Human creativity. By defining what makes creativity distinctively human, tracing the ways in which AI is a means to further creativity, and examining how AI may someday compete with human creativity.

What is Creativity?

Nothing seems more human than creativity—the trait that underpins human innovation, from the arts to the sciences, and pretty much everything in between. According to the Cambridge dictionary, creativity is defined as “the ability to produce original and unusual ideas, or to make something new or imaginative.” However, physicists regard equations not only for their originality, merit, or consequence, but also for their elegance. On the way in which an equation can embody a truth. Ingenuity is defined not only by the solution-like quality it may have, but by the subjective emotion it can elicit.

One of the greatest love stories in medicine is that between William Steward Halsted, considered to be the father of American surgery, and Caroline Hampton Halsted, his scrub nurse. The story goes that around the winter of the 1890s, Caroline complained of dermatitis on her hands and arms due to the solutions used in the operating room. Given William’s regard for Caroline, he made a request to the Goodyear Rubber Company to make thin gloves as an experiment. And these became the rubber operating gloves used today, thin and reliable. Coupled with hand hygiene, it significantly reduced infections and deaths in the operating room. A simple innovation borne out of necessity and concern – a creative and consequential solution sparked by an understanding of context and the human need to alleviate emotional turmoil. 

As it stands, artificial intelligence remains incapable of the emotion that makes creativity human. It can neither comment on the elegance of Einstein’s theory of special relativity nor attribute any solution it proffers as stemming from pain, devotion, or the desire to simply evoke emotion. But ask any AI specialist today, and they will heartily tell you that AI already has a relationship with creativity, one that is beneficial to both parties and largely in service of human creativity. Not quite out to compete with human creativity. Not yet anyway.

Defining Creativity

Over the years, creativity has been defined in multiple ways. All with the explicit aim of capturing and accounting for every facet of the concept. From breaking it down into different types, to analysing its consequences, and clearly defining the common thread of what makes a certain idea, behaviour, or endeavour creative. 

One of the most known and cited definitions was given by Simonton in 2012. It follows the criterion used by the United States Patent Office to determine whether an invention can come under patent protection. An idea or invention must be original, useful, and surprising to achieve either full, or partial patent protection. Another famous definition was used by Boden in 1998, in her article on creativity and its connection with artificial intelligence. According to her, there are three types of creativity, all involving the generation of novel ideas, and each type is capable of bringing about the emotion of surprise:

Three Types of Creativity

1. Combinational Creativity

• Definition: When two new ideas share a common inherent structure.
• Example: Poetic imagery and analogy.

2. Exploratory Creativity

• Definition: Characterised by the creation of new ideas through the exploration of structured conceptual spaces.
• Example: Can include scientists, artists, and musicians who all learn the rules and styles of thinking involved in their respective fields – superficially tweak these established norms to achieve something new.

3. Transformational Creativity

• Definition: Involves the transformation of the space itself, so that new structures can be generated that were before considered impossible – has the capacity to bring about a shock, that is, ideas that, depending on the degree of transformation, may be more difficult to accept. 
• Example: Something like humanity’s once upon a time dream of landing a man on the moon. An idea seemingly so out there that it is shocking, but a vision easy enough to conjure that one might deem it improbable instead of impossible.

By categorising creativity in these three ways, Boden was able to evaluate computer models in accordance with the categories. She additionally differentiated creative endeavours as either being:

Types of Creative Endeavour: 

1. Psychological

• Definition: New to creators themselves.
• Example: When an author writes a story they’ve never written before.

2. Historical 

• Definition: New to society.
• Example: Einstein’s theory of Relativity.

In this way, computer models could be built to mimic the process by which creative ideas could be generated.

Distinctively Human Creativity

However, the key component of whether an idea can be considered creatively successful is the context in which it is made. Great poetry does not simply follow a good rhyming scheme but comments on the human condition in a way that resonates with people—which is to say, artificial intelligence when applied to creativity must be directed. For instance, in the fields of visual art or music composition, AI can be programmed to produce something that is transformational, in that it can create something new and unexpected and be deemed creative. But whether it can be considered a work of art that is largely admired by society is another question. It is imperative for AI to understand, evaluate, and adjust accordingly to contextual cues to compete with this innately human aspect of creativity. 

How AI Attempts Human Creativity

Artificial neural networks (ANN), a set of networks that are inspired by the human brain, try to address this question of recognition. ANN emulates and uses a reduced set of frameworks from biological neural systems. More specifically, these models imitate the electrical activity of the human brain and nervous system. The ANN models are neurons in a complex and nonlinear form. The neurons are connected to each other by weighted links. There is a supervised procedure that comprises three layers: input, hidden, and output. Now, instead of simply asking the algorithm to create something novel and unexpected, it uses an algorithm designed in accordance with the human brain, with narrowed-down data sets chosen by human beings. 

For example, in music, as in several creative fields, there exist melodies and patterns that can influence human emotions in certain ways. A study done in China using AI capitalizes on this fact. Fundamentally, music has three distinct parts: time, pitch, and texture. Using machine learning and deep learning paired with facial recognition and music, they created a model that could assess the effects of certain types of music on a customer’s behaviour. Essentially, they tracked if the music played would affect how long a customer would stay in a certain store. And it did. It confirmed that digital transformation brought about by AI could be used as an environmental stimulus to affect customer behaviour.  

All this to say that one can train a model using a data set of the most known and beloved musical pieces across the centuries and it will manage to render melodies that will resonate with human beings. In this way, the creativity involved is combinational, exploratory, and historical. 

It is almost akin to Doctor Halsted bringing about the invention of thin medical gloves, wherein the data provided to the algorithm is the context of the good doctor’s concern for his eventual wife, thus emphasising the human component in the creative endeavour. Essentially, AI can create works of art or demonstrate creativity by having access to data that provides human preferences. This type of work effectively improves upon human creativity, actively resulting in something useful like knowing the type of music to play to encourage customers to stay longer at a certain shop.

Example: Artificial Intelligence Creative Adversarial Network (AICAN)

Another aspect of creativity is the combination of transformational and psychological, a creation that manages to balance surprise with societal acceptance and be new to the algorithm itself. In this regard, there have also been advances. An astounding example would be the work of computer scientist Ahmed Elgammal at Rutgers University in 2017. He used generative adversarial networks (GAN) that can do more than recognize existing images of objects, but also generate novel images, simply by inverting the "image coding/recognition" procedure. He developed a new GAN known as the Artificial Intelligence Creative Adversarial Network (AICAN) which was able to not only judge its own work, but also look for styles that have yet to exist. In essence, it could create. 

The key part of this endeavour was that he trained the algorithm on a database of over 80,000 images from Western art in the periods between 1400 and 2000. The machine produced its own style by knowing about the styles that already existed in that period. Now for the judgement: the AICAN images were shown at the 2016 Basel fair in competition with works by human artists, and the judges preferred the AICAN images. The judges collectively regarded the images as creative. The case to be made, however, is that the newly produced AICAN images followed the style of heavy abstraction, which follows in line with the history of art. 

As such, an argument could be made that in this instance, the artwork fell just in line with the creative categories of transformational and psychological, given the domain of western art paintings. Regardless, the results were impressive and indicate a positive for AI in creativity. But while it is brilliant that AI can create something entirely new and manage to adhere to human preferences, it remains that there was a human touch to the data: the algorithm was created by humans, and trained on a data set curated by humans. 

Hence, it misses the purpose born of an artist’s context that is usually involved in the production of art. Moreover, western art is a domain with a wider tolerance for astonishment and abstraction far removed from human experience. A domain wherein the purpose of art is allowed to be less important than its ability to elicit an imaginative response from humans.

Where AI Fails to Achieve Human Creativity

The opposite is required for great stories, the narratives and writing that eventually become a part of the literary and cinematic canon need to be attached to the human experience. Indeed, for stories to work, they must go beyond the technical elegance of the writing, they must speak to some sort of human truth. Even the most fantastical of stories, like the Harry Potter series or the Lord of the Rings, revolve around certain universal truths: the experience of adolescence and the ethical conundrum of what is good or bad. In essence, great stories must have a narrative that humans can identify with.

In this realm, there are two use cases of AI that have garnered a lot of attention. 

Two Use Cases:

1. Collaborative Approach: Facilitation of Human Creativity

Most commonly used across different storytelling mediums. For instance, the incorporation of digital storytelling in television, film, gaming, and the entertainment industry fall under this category. In essence, these AI interventions are to be incorporated into the genre of Choose Your Own Adventure (CYOA), a type of story that gives its audience a hand in the direction of the narrative. 

A familiar example would be Netflix’s Bandersnatch, a standalone episode of the popular Black Mirror series, wherein the audience is presented with different choices at different points throughout the narrative that affect the ending. 

The use of AI in this genre is through narrative generation systems, wherein the system responds to user input and allows for interactivity. The difference between the traditional application of CYOA is that, previously, the author had to create the different predetermined storylines upon writing the story. But the use of AI, will allow computational algorithms to process and produce audiovisual media more seamlessly in service of the author’s vision.

This will allow for a wider variety of endings with different nuances in place—making the arc of the storyline more diverse and less predictable for the viewers. 

2. AI Authors Work that Resonates with Society: Creativity that Passes as Human

This second approach is in line with the combination of transformational and psychological creativity; these forms of AI try to be content creators themselves. Such works have largely been experimental and academic, but some projects have made it into the public domain. 

Some examples include projects like Ross Goodwin and Oscar Sharpe’s AI screenwriter, along with Botnik Studios AI. The stories these projects have developed are far from the coherent, emotional, and storytelling standards of the commercial entertainment industry.

But like AICAN images, they have managed to intrigue enough to garner an audience. Perhaps, with time, these stories will eventually evolve from curious, abstract, and compelling in their strangeness, to emotionally moving narratives that account for human context and emotion.

Key Takeaway

Distilled, the relationship between AI and creativity can be categorised in two ways: the empowerment of human creativity and the pursuit of a creativity that can pass as human. Currently, AI has made more strides in the first category, working more so in partnership and with the guidance of human creativity to achieve new heights. 

The latter category which proffers AI as having the ability to compete with human creativity by correctly accounting for emotion, nuance, and societal context, remains in progress. The questions is how long. But for now, creatives all over can capitalize on the ways in which AI is another means for them to exercise their creative juices and dazzle their target audiences—to lean in, and treat AI as what it currently is: a powerful boost to human creativity.