Tame vs Wicked problems

Many design problems have fairly straightforward solutions without needing a particularly ‘creative’ approach – these are often technical in nature and called tame problems. In contrast, wicked problems are complex sets of problems and even when of a technical in nature difficult to define with no right solution. However, some problems have properties of both tame and wicked problems and some problems are neither wicked nor tame but remain in a state of puzzlement. Yet the distinction between tameness and wickedness resonates with the difference between traditional AI, such as Google's search algorithm, and generative AI, such as ChatGPT. The main difference between traditional AI and generative AI lies in their capabilities and application. That is, traditional AI systems are typically designed with a specific set of tasks in mind, primarily used to analyse data and make predictions based on predefined rules, while generative AI goes a step further by creating new data similar to its training data.  In other words, traditional AI excels at pattern recognition, while generative AI excels at pattern creation. This suggests that GenAI can be a tool for dealing with wicked problems which, unlike tame problems typically do not have a correct solutions. However, the uncertainty or ambiguity surrounding wicked problems requires multi-dimensional evaluation of any resolution. https://www.forbes.com/sites/bernardmarr/2023/07/24/the-difference-between-generative-ai-and-traditional-ai-an-easy-explanation-for-anyone/

Ideas to market

Generative AI has changed, and is changing design ideation. Today, it is nearly impossible to avoid AI-powered search engines, such as ChatGPT when generating ideas. Indeed, AI is quickly changing how people search and use the internet more generally. Instead of getting a set of links to follow from conventional search engines, such as Google, AI chatbots provide user queries with short direct answers. And so, the latest GenAI chatbots can produce text, image or sound content without the need for human intervention. Arguably, however, few of AI generated ideas are truly unique or original as GenAI outputs are derived from the input data used to train the AI tool. Yet there are limitations to human creativity too. Indeed constraints present challenges and foster innovation. Therefore the held view that GenAI, as an ideation tool augments human creativity. But whether generated by humans or AI, ideas made public are not protected by copyright unless the idea copied is an expression of a specific work. This suggests that designers who seek to exploit their ideas sufficiently express them to meet the criteria for copyright. Moreover, in a competitive market, the idea need not only be expressed as a specific work but also brought to market, and fast. However, having turned the idea into a product or service, and made it available to the market before anyone else doesn't guarantee commercial success.

AI tools, AI agents and heuristics

When engaged in ideation and problem-solving it can be difficult to differentiate between AI Tools, AI agents or heuristics. Simply, AI tools, such as ChatGPT work in a reactive manner in response to prompts whereas AI agents operates independently without human intervention. In contrast, heuristics are adaptive tools involving experimentation and trial and error. Or, to use an aviation metaphor, AI tools are like co-piloting, AI agents similar to auto-piloting, and heuristic tools comparable to flying manually. Key considerations when deciding between these tools include: task predictability; complexity of decision-making; and need for adaptability. The choice, however, depends on user needs. AI agents, for example, in automating decision-making processes, don't need step-by-step instructions but fall short of true autonomy. That is, in real-world applications, AI agents are faced with technical challenges and issues of trust and security. For everyday design thinking, this suggests using either iterative prompting to unlock the power of GenAI tools (a process of refinement) or using heuristic methods grounded in pragmatism and domain expertise, particularly to ensure decisions are made with ethical considerations and human oversight. However, designers typically use hybrid problem solving strategies and so prompt engineering may require heuristics too. Sources:  https://relevanceai.com/relevance-academy/when-to-use-agents-v-tools  https://www.ibm.com/think/topics/ai-agents  https://mikecarruego.medium.com/choosing-the-right-algorithm-machine-learning-vs-heuristics-dc0b65e97d98

Practice makes ideas

Design ideas, or creative ideas in general, are typically considered new if revealing novel forms, patterns, methods etc. and perceived by others as being meaningful or useful. Ideas, then, motivate and drive many aspects of human activity. Yet often creativity is thought of as a special faculty, or gift even reserved for certain individuals, notably in the arts. But while creativity may come instinctively to some, everyday experience shows that creative ability or capacity is not only open to the few but to the many if not everyone. Moreover, creativity can be cultivated through sustained and deliberate practice. That is, simply making time for creative pursuits and activities, from doodling and clay modelling to playing with paper and crafting prompts. Designers, then, learn the core fundamentals of their discipline, including ideation, and then practise practise, practise. And so, to produce good ideas, designers generate a lot of ideas. In short, practising ideation. Interestingly, the habit of practising also appear in machine learning, ML, which imitates intelligent human behaviour,. That is, ML starts with data (including ideas converted into datasets) - and is trained on data. And so, the more data, the better the ML model.  See also: https://ideation-workshop.blogspot.com/

Preconditioned ideas

Design ideas sometimes need technological or cultural preconditions to be fulfilled for innovation to happen. For example, social media platforms would not have happened without the invention of the Internet. However, such preconditioned-driven innovation has a long history. Galileo, the Renaissance astronomer (1564-1642), for instance, used his skills as a lens-maker to greatly improve existing telescopes which, together with the use of illustrated printed books, enabled him to prove his theory of a heliocentric universe. Galileo, then, both as a thinker (conceptual innovator) and a maker (experimental innovator), is a powerful example of the key role that technologies play in enabling advances in scientific knowledge. Also, Galileo's achievements show how innovation, as a problem-solving concept, is preconditioned on previous ideas, research or experiments. Moreover, designers, through analysing and interpreting historical innovations may identify which ideas have been successful and which haven't. As a result, establishing a positive relationship to history, past ideas pave the way for future innovation. 

Ideation as function of human language

Ideas, as linguistic phenomena function at several levels of human language. Karl Popper, the philosopher (1902-1994), talks about higher and lower functions of language. Accordingly, the lower functions of language are self-expression and signalling whereas the two most important higher functions of human languages are the descriptive function and the argumentative function. Popper holds that the lower two functions are always present when the higher ones are present. Moreover, he regards the role of argument as important as observation and experiment. Now, if relating Popper's line of thought to ideation, one might say that idea generation corresponds to self-expression and signalling whereas idea communication compares to the descriptive function and the argumentative function. That is, designers generate ideas (self-expression) and communicate ideas (descriptive function and argumentative function), which constitute the ideation process. And so, self-expression is always present in the idea argument which reflects the transient nature of ideas, that is, ideas may always be replaced by other ideas. In other words, argumentation is an important part of ideation, or "proving" the idea.

Playfulness

Playfulness is evident in children all over the world. Children play to create, learn and communicate. And playfulness plays a creative role in design practice too. Indeed it can be seen as a methodology to support the design process. In this, playfulness reflects an attitude to designing that encourages creative thinking and making, from motivation and exploration to stimulation and collaboration. Moreover, playfulness encourages design teams to focus on creative potential rather than criticism of each others' ideas. A current example of playful design is the Play Pavilion in Kensington Gardens, London, a temporary summer venue open to people of all ages.The pavilion, with its single dome volume, acrylic skin and bright colours, is a joint project by the Lego Group, the Serpentine Gallery and the architect Peter Cook, a co-founder of the Archigram collective in the 1960. Cook describes the pavilion as a “theatre of formative play” where architecture becomes a stage for unstructured creativity. The project also expresses Cook's idea that building should be joyful, social, and open-ended, a reminder that architecture can be soft, silly, experimental, and still deeply intelligent. https://www.serpentinegalleries.org/whats-on/play-pavilion-commission-by-sir-peter-cook-in-partnership-with-the-lego-group/