The Power of Language

Natural language processing (NLP), the branch of artificial intelligence or AI is what gives computers the ability to understand text and spoken words in much the same way human beings can. That is, NLP drives computer programs that translate text from one language to another, respond to spoken commands, summarise large volumes of text rapidly, as exemplified by spam detection, Google Translate and Chatbots. NPL also has the ability to convert text to an image using text-to-image generating AI models such as ChapGPT and Stable Diffusion. The power of NLP, however, should come as no surprise - after all, language models have been around for decades. Indeed, natural language is the main means of communication, between humans, between humans and computers, and even between computers. Moreover, the human brain is good at pattern recognition or making connections between seemingly unrelated things and this ability is boosted by AI, and just by using words (text data or text prompts). AI, then, is a transformative tool, indeed, an ideation tool. An open question remains though: are complex AI models (machine learning) truly doing something new or just getting really good at statistics? Well, since machine learning uses “statistical techniques” it can easily be construed as rebranded statistics. But the way statistics is used by statisticians is different than the way it is used by the machine learning community. That is, and according to US statistician Leo Breiman, statisticians use data modelling whereas machine learning practitioners use algorithmic modelling*. Both models can be used to understand data and make predictions. But machine learning lets nature, data and trial-and-error speak about the function that drives inputs to outputs in a complex system: "Let the data do the talking". In contrast, statisticians believe they can guess about this mechanism using best practices making upfront assumptions about the process that generated the data.*  https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.full

AI has no sense but makes sense

Let's assume for a moment that all design ideas, past and present, would be collected and digitally represented in a giant data bank. And that the digitised data would be retrievable and processed by artificial intelligence, or rather machine learning, a subfield of AI. That is, to use algorithms trained on data to produce adaptable models that can perform specific tasks such as sorting images or analysing big data that would result in sets of recommendations. For example, recommendations that would bridge the gap between architectural design, engineering, and construction enabling architects to work more efficiently by automating all repetitive, mundane and time-consuming operations and thereby free up time and resources to reflect on practice as well as further experimentation and speculation. Indeed, the design process is both iterative and reflective, that is, to reflect on one's actions so as to engage in a process of continuous learning (Schön 1983). But teaching computers to be creative, or rather learn from experience and adjust to new inputs, is inherently different from the way humans learn. Generative artificial intelligence, GenAI, however, can help us generate new ideas and insights by analysing large amounts of data. So the current leading opinion is that GenAI can guide, support and augment the creative process and help problem-solving. A fact that also underlies many of the most popular AI-assisted services – from Google to Netflix to Xbox’s Game Pass service. The number of AI-generated recommendations, however, can be overwhelming to the point of distraction and so it is important for the complex machine learning models to make specific and customised as well as contextualised recommendations. Machine learning, then, through a recommendation system, has the potential to become a powerful design tool. A tool not to replace a designer, but to assist and therefore enhance their work.

Playfulness and fun

Advances in digital technology, such as CAD, 3D printing and generative artificial intelligence, GAI, have marginalised traditional ideation tools, notably freehand drawing and sketch modelling by hand. Moreover, digital software employed at the ideation phase may have the advantage over analogue tools in that it can facilitate more complex shapes, forms and layouts at a faster and more cost-effective rate. Also, computer software allows ideas (content) to be presented in digitised form that resembles final appearance. Indeed, in the digital culture clients may well expect the shown idea (proposal) to be pretty close to the final outcome, or, "What You See Is What You Get". That is, when presenting ideas, the digital medium offers designers more persuasive power to win over the client than a rough sketch on the back-of- the-envelope. In other words, ideation through digital means suggests a reduction in the ambiguity embedded in the analogue sketch. However, the march of digital technology, driven by demand for increased efficiency and certainty in outcome, and already at the ideation stage of the design process, may overlook the fun and playfulness designers experience when working and thinking with analogue tools. This dilemma, however, if perceived as such, would have to be addressed by the designers themselves.

Ideas evolution

Ideas, as elements of thought, are characterised by cultural diversity. This diversity may be traced to the human origin story. Anthropological research, for example, has revealed 50,000-year-old hunter-gatherer exchange networks in Eastern and Southern Africa which has led Cecilia Padilla-Iglesias, a Cambridge-based (UK) anthropologist, to pose the question: 'Did Humanity Really Arise in One Place'. This suggests that these early networks were efficient at developing complex cultures and spreading innovations which also suggests that homo sapiens quintessential adaptation is to exchange ideas, genes, and culture with one another. This may further suggest that modern human cultural and biological diversity emerged as a mosaic rather than a linear process, a process which may help explain humanity’s evolutionary success. And so, Iglesias concludes, 'humans have developed beautifully complex and dynamic behaviors, beliefs, and technologies that have allowed us to thrive wherever we have traveled'. Indeed, ideas have travelled and evolved with the evolution of humanity.

Boredom sparks ideas

Boredom is not necessarily miserable and harmful, as designers know. In fact, they want to get out of a boring state, so they indulge in novelty-seeking unique thinking, which brings out creativity. Indeed, boredom is not something to fear, but to embrace. Graphic designer Paula Scher of Pentagram design consultancy, for example, finds that ideas come in all kinds of ways and that she gets her best ideas when stuck in traffic, in the back of taxis: 'Boredom as the key to getting the best ideas'. Moreover, to stir creativity, Scher is inspired by looking at a lot of books, stimulated by long walks, or just by allowing herself to do nothing. That is, she lets her mind wander, rather than occupying it with apps on the phone: 'Spending time on your phone won’t provoke any new ideas, that’s for sure', she claims. 'One needs to be in a state of play to design', she continues, 'I generally push something as far as it can be pushed. For me, that’s the fun'. https://www.stirworld.com/inspire-people-design-icon-paula-scher-on-embracing-noise-to-recapture-the-creative-edge

Ideation as collective intelligence

The advance of generative artificial intelligence, GenAI, is enabling the development of ideas and concepts as well as refining visual outputs based on mimicking of what is already there. GenAI as an ideation tool, however, is only as good as the datasets it is trained on and therefore produce images that risk being rather repetitive or "in the style of". But GenAI models ability to mine vast open datasets on which they are trained can be problematic in that such datasets also carry creative ideas, for example, visual style and likeness which have no copyright protection. GenAI, then, poses both risks and opportunities for designers. The risk is posting creative work online without getting due credit or compensation. The opportunity lies in augmenting human imagination. Positively, however, in the bigger picture, where design is both a competitive and collaborative activity, and where creativity signifies empowerment, GenAI and human intelligence can complement each other. Moreover, when successfully combined, GenAI + human intelligence become collective intelligence, as suggested by Nesta’s Centre for Collective Intelligence Design.

Ideation by AI = AIdeation

Ideas are related to imagination and imagination to creativity. And creativity leads to either invention or innovation where invention amounts to absolute creativity, that is, the outcome is unique or original* whereas innovation is creativity through variation or incremental change. That is, innovation based on modification or combination of what already exists and constitutes the vast majority of "new" products and services for the marketplace. In this the marketplace demonstrates its capacity to assimilate, modify and commodify almost any idea. Ideation for innovation, then, can be seen as a creative thought process through variation. But variation is also key in the development of artifcial intelligence, AI..That is, generative artificial intelligence, GenAI learns continuously from the accumulating data and outcomes and is getting better and better with time increasing its ability to change, adapt and grow based on new data. As a result GenAI at maturity is capable of indefinitely generating new ideas that are combinative derivations of already known forms and patterns. Innovation by artificial versus human intelligence, then, is not mutually exclusive, that is, both  are capable of achieving compatible results. However, convergence of human and artificial intelligence suggests that a designer response to the advancement of AI is to emphasise the subjective and the intuitive, that is, to give evidence of an essentially human action, to mark the subjectivity of the idea itself. Indeed, AI only acts based on prior data and, lacking human intuition would struggle to have an answer to new unique circumstances. This further suggests that GenAI is built for innovation rather than invention. GenAI models, then, through their capacity for variational creativity, have the potential to become a major ideation tool for innovation in the marketplace. That is, ideation by AI, or AIdeation. * What is described as original design of use is ultimately defined by intellectual property (patents, copyrights, and trademarks).