Is creativity safe?
With artificial intelligence ever increasingly sucking up jobs in low skilled industries could artificial intelligence one day take the creative industry too?
Will the East or West win the race to a superior AI system that will usher in new era
Yuval Noah Harari claimed famously “the idea that humans will always have a unique ability beyond the reach of non-conscious algorithms is just wishful thinking” , we suggest that the capacity for moral and emotional reasoning, for now at least, cannot be replicated even by complex machine learning algorithms. Yet I have no shadow of a doubt this ability is closer than we anticipate.
As the world enters what has been called the fourth industrial revolution—an era dominated by technology, digital media, and now increasingly, by machine learning—a new debate is emerging around capacities for creativity.
A large questions arrises, are humans the sole sources of creative output? In other words Is Creativity Safe?. With rising rates of automation, large corporations are fraught with ethical dilemmas associated with introducing machine learning to the workforce. Machines can already engage in many of the mundane tasks that were traditionally assigned to manufacturing sectors of society. But as algorithms are further refined and iterated on at making decisions and solving problems, some of the more complex procedures undertaken by professionals are also at risk of being replaced.
Creativity is at the heart of when making critical decision during moments of uncertainty. Machines are being programmed to skilfully analyse patterns in their immediate environments and make informed decisions based on predictive analytic models. Machines are also being programmed to generate creative works of music, painting, and literature of which is had been primelarry a human endeavour.
There are two types of creativity which are vulnerable to artificial intelligence. The first encompasses the difficult decisions that professionals must make when they encounter uncertainties. To select an appropriate course of action based on given and often, limited —information requires creative thinking and acting. This type of creativity aligns with “Pro-C” creativity – creativity that is deployed within existing domains of knowledge.
And the other kind of creativity highly sensitive to disruption is everyday creativity. Everyday creativity, or what is known as “little-c creativity” refers to individual capacities for doing things in novel ways. Creativity of the little-c variety is primelarry the practice that UAL teaches. From Graphics, music, sculpture, animation and photography for example, it is characterising something unique about the human society.
Much of this little-c creativity depends on emotional input, affective co-regulation, and human agency. In short, little-c creativity requires complex human consciousness that have not yet been successfully communicated to artificial intelligence systems. Are we finally reaching an age where machines are learning, thinking, and deciding on their own? Or, is creativity merely engineered according to what society currently values? To answer these questions requires at first an under-standing of what creativity is.
Professionals use creativity when presented with unique problems that appear in unexpected situations. A surgeon, for example, must at times accommodate new problems during an operation. Deep learning machine programs are now capable of adapting to unexpected situations. The emergence of self-driving cars might be the most timely example. However, unlike human beings, artificial intelligence is programmed to make decisions without conscious restraints. Some even argue that machines are better poised
to maximise positive outcomes because they are bound to precise algorithms.
TESLA SELF DRIVING WITH ALL COLLECTED INFORMATION ON DISPLAY
Moving forward, I present two key definitions that will help contextualise creativity as both a human enterprise and the possibilities of creative artificial intelligence. The first draws on the individualist approach developed during first-wave and second-wave creativity research. As suggested, the individualist approach is concerned with the individuals involved in creative processes. The individualist perspective, therefore, principally focuses on the person as a unit of analysis, and forgoes operationalisation in terms of external social and cultural forces. According to the individualist definition: “creativity is a new mental combination that is expressed in the world” (Sawyer, 2012, p. 7). The individualist approach to creativity holds three main assumptions. First, creativity must be something new, unique, or original. Regular basic mathematical equations, for example are not creative. However, by crafting new combinations of existing equations, or solving problems in a different way to your initial teachings can be creative. Importantly, understanding known data requires application of personal knowledge to uncharted terrortiries.
The second, assumption of the individualist approach is that creativity must be externalised. To capture creativity in the wild, ideas and thinking patterns must be expressed externally so that they are made visible to the researcher and to the public at large. Therefore machine learning programs that paint, write, compose music, and play games present opportunities for measuring the creative potentials of artificial intelligence.
Some domains of work require more sophisticated forms of pattern recognition, and in turn, creativity, than others. As consumers, we already rely on a wide range of automated programs to help organise our lives and custom tailor everyday experiences. These pro- grams have become so commonplace that we deploy them almost effortlessly. Some are perhaps more obvious than others. For instance, Google Maps updates live traffic routes to get us promptly to our desired destination. Spotify, Apple Music, and YouTube provide song/video recommendations that align with our current tastes. The familiar voices of Apple’s Siri and Amazon’s Alexa are programmed to learn the most common commands and in turn quickly find resources to aid us in planning events and making informed decisions about the future. The list of intelligent programs that we use in our everyday lives goes on and on: Online search engines estimate our preferred search by filling in half- typed words, shopping bots use collected data to guide us towards products they think we
might want to purchase, video streaming platforms like Netflix and Amazon Prime suggest entertainment that relate to our previous watches etc. Of all these though social networking platforms like Facebook and Instagram have taken deep root and become the most invasive. A few years ago the large-scale political consulting firm Cambridge Analytica collected the personal data of millions of Facebook users for targeted profiling that tugged at the moral, ethical, and legal sensibilities of millions of people around the globe.
In tern this single highly public scandal opened the floodgates on Facebook and the Data practices used by the majority of the world wide web, leading to large court cases and droves of ordinary people leaving the platform and becoming ever more wary of their doing’s online. It’s highly likely that computers and search engines know us more intimately than our family, friends and spouses. Social media algorithms using our data can tap into our inner most thoughts and desires. Online searches of sensitive medical issues, housing markets and stock exchanges provides useable data to technology behemoths that then leverage our queries for targeted add campaigns. But this type of pattern recognition is not creative exactly (Or at least it isn’t what we would call Big-C or little-c creativity) Although the evolution of automated programs has been shown to increase the efficiency and precision of everyday tasks, the more complex forms of pattern recognition associated with creative processes are still quite basic – though rapidly evolving. Importantly, the mundane tasks of automated programs will perhaps one day replace many “higher level” jobs, but do not yet touch the more creative endeavours for which machine learning has huge potential. Machines can already best human players at games like chess and even “learn” to create digital works of art, software available that automatically improves a photograph in an editing suite, or is capable of increasing the quality of music production, or visualising the blueprint of an architectural design. One of the most profound little-c learning algorithms in development is a program called Google Deep Dream. Deep Dream generates paintings “independently” by coding digital images and applying pre-selected parameters to create works of art. The results are an unusual mix of images that resemble nightmarish phycadelic dreamscapes. Deep Dream encodes user-provided digital images and interprets them by using previously stored data in its neural network.
GOOGLE DEEP DREAM CREATION – KYLE MCDONALD
Even with all these advances, sophisticated machine learning programs that can predict specified outputs and render creative artworks are utterly and completely dependent on input from human minds. For a machine to create a painting, it must first be shown existing images. To recommend an appropriate fashion style, it must first be given options from which to choose. AI is fed information from an existing knowledge base. This sounds rather similar to a Human, by taking on existing information from a collection of knowledge. Yes it is highly similar but it is essential to recognise that human programmers selectively apply this information to the AI. The computer program cannot think for its own, even if it is programmed to deploy randomised outputs from pre-programmed inputs, because the inputs are purposefully selected. In the world of human intelligence, the domain is an open system. We are an evolving organism of cultural values forged collectively by the inhabitants of society throughout our time on this living earth. In contrast, the domain of the machine world is a closed system. The only experiences allowed entry are those that are programmed into it and further, what knowledge it has access too. In short, the artificial domain is created from the limitations of the human mind. Any further iteration is dependent on the inputs of human creators. Yet I would prompt you to consider the mass of information that is Google and consider that if an AI was taught how to navigate this information and interperate it, where would it get too, what would it become. We as humans are rather limited in the amount of knowledge one can hold. A computer is merely limited to its processing power and having a brain which is effectively limitless raises worrying prospect.
Academics have been exploring human creativity since the middle of the twentieth century, “early” work in artificial intelligence and deep learning is very exciting, yet highly problematic and some cases controversial.
It’s very clear there is a growing worry that machine learning programs will disrupt a variety of professions that require high levels of human interactions and cognition. Human creativity is rooted in an array of social, emotional, and cognitive mechanisms.
Creativity can be observed as an individual process or nested within broader sociocultural systems.
Each situation is a critical, and unique, tied to human beings – artificial intelligence has yet to fully infiltrate this. Technologies that pushed humanity further like the agricultural to the industrial revolutions served the purpose of making life more comfortable. These technologies freed us from physical and cognitive resources that were once tied to us fulfilling basic needs. Our hardwired survival instincts shifted dramatically when we transitioned from nomadic lifestyles to agricultural settlements thousands of years ago—a humans no longer needed to hunt, gather, and storing resources daily simply to survive. When we learned to exploit the local natural resources and create managed crops and livestock, a huge amount of energy was spared. We could now spend more time furthering our emotional, cognitive abilities and use whatever energy left to explore other technologies.
Now, in what some are calling the fourth industrial revolution, machine learning programs still serve the intended purpose of making our lives easier. From predictive algorithms that can accurately and efficiently diagnose a variety of diseases, to the more basic task of navigation, music recommendation, and spell checking. AI is designed to reduce our cognitive load by making decisions for us. At the same time, our very human curiosities
push the limits of artificial capabilities to provide avenues for designing programs that paint, compose, and play. In this new era where robots learn to perform and outperform certain human tasks, people wonder whether machines can iterate and develop new forms of thought and recognise complex patterns better than we can. With AI though, there is no drive emotionally something that we humans need for generating creative and useful ideas. An intelligent algorithm that accurately perceives, understands, and regulates emotions, to our knowledge, has not been fully developed.
“Maybe art doesn’t need to just be for humans, to please our sense of what is beautiful,” Uchida said. “What would A.I. produce if it was making art for itself?”
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