Golden age of ideas

In the light of the universal impact of AI, and to paraphrase Winston Churchill as he was paying tribute to the Royal Air Force establishing air superiority over England in WWII, one might quip: "Never in the field of creativity was so much owed by so many to so few". The many, here, the users of GenAI, and the few the hard- and software pioneers and providers of AI models. Indeed GenAI is capable of producing instant ideas with a few prompts widening participation in the design process, that is, towards democratising design itself. And so, GenAI has become the ubiquitous ideation tool leaving design students and practitioners, working alone or in groups, to "edit" or "curate" GenAI's results to their taste. In other words, designers have become as much editors and curators of  GenAI outputs as originators of their own ideas. But what is then an original idea? And hasn't man always used tools of one kind or another to progress or advance civilisation?

Designing in the age of AI

AI is widening participation in the design process making design more populist in both scope and manner: "Everyone is a designer". But research suggests AI tools are most benefitting designers who already are high performers. That is, designers who know how to guide AI, how to integrate it with everyday design tasks as well as how to judge its output. Yet extensive use of AI tools, notably by entry-level or early career designers may underplay first hand observation and drawing skills as well as critical thinking and judgement. Or, why struggle with a problem when a chatbot can provide a neat, packaged response that might be regarded as "sufficient," or "good enough"? What then is the impact of AI on cognitive effort in matters of design? Could it be the case that in accelerating AI technology, "Man shapes AI and then AI shapes man"? That is, rather than based on human agency, AI models develop their own agency. That is, AI agents can also make decisions and take action to achieve a goal without human prompted. What, then is design and what makes a designer in the age of AI? If, say, human agency is finite, then what it means to be a designer is to grasp this finitude of designing, or "becoming one who is designing".

Prompting is creative communication

The creative abilities of GenAI models are artificial, not genuinely human. However, with human assistance, GenAI can produce creative content. But, as shown in experiments, GenAI (DALL-E3) output, which are clear and measurable, depend on user prompts (written instructions) as much as models*. This highlights how prompting is about communication which in turn reflects how verbalisation (words) is the most common means of human communication. This in turn suggests prompting is a creative process as well as a skill - a skill that improve with practice. In short, GenAI models need human intervention to produce independent creative output. *https://mitsloan.mit.edu/ideas-made-to-matter/study-generative-ai-results-depend-user-prompts-much-models

 

AI-improv

Ideation has affinity with improvisation, or improv, a term derived from improvised performance and inspired by earlier theater forms such as commedia dell’arte and cabaret. At its core, improv is the art of free association, of making things up on the spot where improvised scenes often incorporate suggestions from the audience. But as an art form, improv favours the prepared mind. Similarly, ideators produce great ideas from their overall experience and observation*. And so, in the context of practising ideation, generative AI can be seen as an improv collaborator because interactive chatbots immediately respond to the user prompt by offering something new that directly builds on the user input. That is, working with improv dialogue, the chatbot responds to the user prompt with edits and multiple additions ("yes, ands"). But like any improv performance, the variety and quality of the output depends on interaction between the user and the bot. That is, the more the user brings curiosity, specificity and spontaneity to the improv dialogue, the more imaginative and meaningful the results become. Yet it should be noted that chatbots are not without bias, like people, but lack personal experience and human emotions. *There is no innovation without previous work. Also, "In the field of observation, chance favours only the prepared mind" (Louis Pasteur).

Creativity: Muses or neurons?

'Every act of creation is first an act of destruction', is a quote attributed to Picasso, words which suggest creating from physical matter, or creatio ex materia . This in contrast to the notion of creating "out of nothing", or creatio ex nihilo which proposes creation by some divine act or inspiration. Both assumptions involve the nature of creativity and change, and ego, And Picasso, like the Muses of Greek mythology, projected striking egotism, all that makes for a good story. But from a scientific perspective, creativity is elements of thought across many levels of physical, chemical, and biological description. Take Dr. Nancy Andreasen, for example, whose research is about the relationship between creativity and the brain. She writes that the brain’s abilities are “near miraculous,” and the process of creating something “is neither easy nor obvious.”. And so, her hypothesis is that the brain begins by disorganising and then making connections between various encoded data which were not previously connected. In other words, our brain is a self-organising system meaning that it could easily be chaos, with quadrillions of living, moving parts (neurons), but it somehow keeps itself in order, not unlike flocks of birds or ant colonies.  Now, if every human being possesses what could be called “ordinary creativity”, which goes back to our basic instincts, then our brains learn, unlearn and relearn to recognise patterns in order to aid in our survival. "Extraordinary creativity", on the other hand, as exemplified by Picasso, seems to be operating with substantially enhanced neural processes.  *Andreasen, N. (2005) The Creating Brain - The Neuroscience of Genius. Chicago University Press. 

Unpredictable AI

AI technologies are essentially computational systems and as such fairly well understood among IT professionals. However, from social and cultural perspectives, which necessarily include design processes and outputs, these technologies are poorly understood and, as there is no single definition of AI, have become subject to conjecture, speculation and doublespeak. But there are two primary approaches, probabilistic and deterministic AI with each serve unique functions based on their design and outputs: GenAI systems are probabilistic*. Yet AI's long-term impact on, say, education and the labour market is hard to predict because transformative technologies, such as generative AI, take time to become clear. Moreover, there is uncertainty about how humans, both individually and collectively will respond to AI's societal impact, particularly as the AI industry remains largely unregulated creating vast wealth and power inequalities. Given the unpredictable nature of AI, then, designers need to consider both technology and ethics when applying AI systems. *At a high level, probabilistic AI models uncertainty and provides outcomes based on likelihoods. This means that it doesn’t always offer one definitive answer but instead provides a range of possibilities with associated probabilities. Deterministic AI, on the other hand, is rule-based, designed to yield specific, predictable outcomes without room for variability once given a particular input. https://www.dpadvisors.ca/post/the-basics-of-probabilistic-vs-deterministic-ai-what-you-need-to-know

AI-deation workflow

Artificial intelligence-based software, such as ChatGPT has become an everyday part of designers' workflows, across disciplines and cultures. Using ChatGPT can help generate ideas and develop design concepts and make the ideation process faster and more efficient*. Moreover, AI image generators can save significant amounts of time and money on rendering. Indeed, as the underlying AI technology is getting more powerful exponentially, so are the tools based on the technology. However, ChatGPT responses and outcomes, and the quality of advice and solutions, depend on prompts, custom instructions and context, and while it gets a lot right, it doesn't get everything right. And so, powerful AI tools in the hands of beginners who don't understand what AI is doing are at risk of doing mistakes, and in haste. Therefore, it is good practice to verify anything important before acting on it, especially when it comes to high-stakes decisions. In short, to harness, and fully benefit from the power of AI across design fields require challenging experience and discernment. *Caveat: While complex ideas travel slowly, simple ideas actually travel very fast. However, there's a high amount of variables wrapped up in even the simplest of ideas, if and when we let an idea rest or “sleep on it.”