Hands help advance the idea

The digital age has transformed many aspects of our everyday experience - not least what we do with our hands when busying ourselves with digital devices. But what function does continual hand activity have? The most obvious answer is that we need our hands to do things. The hand is associated with agency and power. Hands serve us. They are the instruments of executive action, our tools. The idea of the hand as a tool, however, isn't new - it was common also in classical times. Where Anaxagoras had argued that humans are intelligent because they have hands, Aristotle, and many after him, countered that they have hands because they are intelligent, as the hands perpetuate our will instrumentally. In contemporary society, we are encouraged to counter our apparent alienation in the excesses of the virtual world by returning to traditional activities such as knitting, gardening, or general tinkering. Using our hands to make things is a remainder of the grounding, satisfying bodily techniques of the past. And when designers talk about why they practise handcraft, such as freehand drawing and model making, the central motifs tend to be the importance of personal choice, the sense of autonomy and self-improvement. But also excitement - the excitement of discovery, or the ubiquitous Aha! moment. Or, in the words of Bertrand Russell, the philosopher and logician (1872-1970): 'Nothing in the world is more exciting than a moment of sudden discovery or invention, and many more people are capable of experiencing such moments than is sometimes thought.'. This blog entry inspired by, Leader, D (2016) Hands: What We Do with Them - and Why.

"AI Slop"

GenAI has triggered what has become known as “AI slop” – images and text created using generative AI tools. Coined in the 2020s, the term has a derogatory connotation akin to "spam", "junk" or digital clutter" that signify unwanted, poor quality AI content in social media or in online search results. However, Meta’s chief executive, Mark Zuckerberg, said that new, AI-generated feeds were likely to come to Facebook and other Meta platforms: 'I think we’re going to add a whole new category of content, which is AI-generated or AI-summarised content or kind of existing content pulled together by AI in some way.'  Although AI-generated feeds on social media carries risks and ethical concerns, "spammy content" viewed critically may inspire ideation. Indeed ideation springs from many sources and the more we learn, experience, and try, the better we get at generating creative and meaningful ideas.

Fringe ideas

In her book An Anatomy of Inspiration Rosamund Harding (1898-1982), an English musical scholar, sets out to reverse-engineer the mechanisms of creativity through the direct experiences of famous creators across art, science, and literature. In so doing, Harding finds common threads of creativity emphasising its combinatorial nature and its reliance on eclectic knowledge. She holds: 'Originality depends on new and striking combinations of ideas. It is obvious therefore that the more a man knows the greater scope he has for arriving at striking combinations.' Harding continues: ' Success depends on adequate knowledge: that is, it depends on sufficient knowledge of the special subject, and a variety of extraneous knowledge to produce new and original combinations of ideas.' Moreover, she writes: 'The variety of interests tends to increase the richness of these extra ideas — ‘fringe-ideas’ — associated with the subject and thus to increase the possibilities of new and original combinations of thought'. Harding's findings suggest support for knowledge-based ideation while debunking the genius-myth of creativity*. That is, in-born creative ability is not enough by itself without a solid foundation of knowledge obtained by experience or study. But more than this, in the age of artificial intelligence, designers draw inspiration from a raft of genAI applications, such as Dall-E, which, given their combinatorial nature, help produce what Harding calls 'fringe ideas'. *Research trends, originally outlined by Graham Wallace (1926) suggest five major stages of creativity: Preparation (idea generation), Incubation (gestation period), Illumination (the "Aha! moment"), Evaluation (idea development) and Verification (idea communication).

The idea of Now

The concept of now represents the inevitability of transience - the relationship between space and time - a kind of fleeting sense of reality. The space-time reference point of now suggests both spatial and temporal uncertainty as well as subjective experience. That is, the concept is not absolute but is relative to the observer's frame of experience. Yet defining the true nature of now takes on elusive yet provocative and fruitful associations. It has crossed cultures and civilisations, people and places, and countless interpretations have been proposed, as exemplified by Buddhist impermanence, the ‘feeling of things’ of Japanese aesthetics or Picasso's verdict that 'If a work of art does not live in the present it does not live'. The transitory essence of now suggests that ideation is an agent for continuous change where the idea is in the present time or moment yet related to the past pointing to the future. This also suggests that the ideation process can be viewed through a philosophical lens, say, of the ontology of being and becoming, or Hegel's view that everything is in a process of change. And Heraclitus noted the endless flux of existence: 'It is not possible to step twice into the same river.'

GenAI as spectacle

Generative AI enables machines to produce content that appears to mimic human-like creativity. As such GenAI models, as tools are great creative assistants. But while GenAI models excel at mimicking human-like responses, they lack genuine comprehension. That is, GenAI models use complex mathematical and statistical methods to generate responses that appear intelligent but lack genuine understanding or reasoning. The illusion of understanding means that GenAI output is surface, or representation without any deep contextual understanding, as experienced on social media platforms such as Instagram or Copilot. To better grasp the concepts of semantic understanding, reasoning and appearance of GenAI, a philosophical interpretation may help. For example, Guy Debord  (1931-1994), an arch-critic of consumerism and theorist of The society of the Spectacle (1967) - elaborates a system of social relations mediated by images where the totality of social relations becomes mediated by appearances. That is, experience of events is replaced by a passive contemplation of images (which are determined by other people) exemplified by the culture of advertising, consumption, and celebrity. Debord's term spectacle has become widely used for the modern condition. GenAI, then, through Debord's lens of a world mediated by images, may become the new social spectacle. The spectacle, moreover, evokes differences between appearance and reality. Shakespeare's Macbeth, for example, demonstrates how appearances cannot be trusted because they are moldable, meaning they offer no insight into the reality of a person. As spectacle, then, GenAI may alter perceptions of visual representations whereby appearances are based on copies instead of the original, or copies without the original.

Data-driven ideas

In the early days of design research - 1960s, a scientific approach to to the design process was promoted as the result of new technical developments such as computers and automation. Although the design science relationship was, and remains controversial, scientific knowledge, and its application is still relevant to understanding how designers think and work (Nigel Cross, 2023). A recent example is GenAI models trained on terabytes of web crawl data from across the internet to create new content. Being data-driven means that GenAI's output is derived from data analysis, which is part of the scientific method. In the data-driven approach, GenAI represents a break from the more traditional view of design as a creative practice supported by artistic, intuitive processes or personal opinions. Yet GenAI does not exclude the designer from the ideation process because GenAI operates on a probabilistic framework, it does not possess true understanding or consciousness like a human. That is, GenAI is not capable of learning, or understanding the concepts underlying its own responses to human prompts, which are input elements for GenAI to generate results. Indeed to achieve human-centered desired outcomes, writing effective prompts is considered a skillful craft. And so, although GenAI evolved from computer science, the technology is guided by human prompts. GenAI, then, reflects human-machine interaction -  a tool that facilitates ideation.

Easy Aideas

Design ideas typically are about incremental change or improvement over something that already exists rather than about something that is truly original or radically different. For incremental innovation, then, generative artificial intelligence has emerged as a powerful ideation tool that facilitates a seemingly endless flow of creative content. But how does GenAI tools like ChatGPT and Copilot compare with human ideation without GenAI? GenAI foremost advantage is it generates huge amount of diverse content - both text and images using pre-existing data from across the internet. Then there is the ease of use of GenAI as it responds to written prompts in a conversational style and in multiple languages - and at great speed. More, as GenAI is going mainstream it has the potential of facilitating problem solving on a global scale. But there are weaknesses with GenAI models.. For example, writing effective prompts may seem simple but rests on prerequisite knowledge, language skills and, yes human imagination. Even so, the user friendliness of GenAI and the creative and original appearance of its output carry the risk of over-reliance on GenAI. Indeed users might find themselves having more ideas than they know what to do with. Also, it is not transparent what data GenAI models are trained on - raising ethical or copyright issues. Or, the self-referential loop of GenAI data might, paradoxically result in more similar output over time narrowing the scope for plurality or novelty. But values, assumptions and biases are embedded in GenAI tools, and so more empirical evidence is needed to fully evaluate the pros and cons of GenAI systems. Yet the appeal of GenAI is overwhelming and by fusing AI-generated ideas with human judgement and refinement, it is fair to say that GenAI is enhancing human creativity, including ideation.