Idea theft?

The quote "good artists copy, great artists steal" is often attributed to Pablo Picasso, but it is actually a paraphrase of a statement made by T.S. Eliot, who said, "Immature poets imitate; mature poets steal." But what is the difference between stealing and copying in creative contexts? One interpretation is that to steal is to be inspired whereas to copy is to imitate. Another, and according to artist Adam Kurtz, is that the difference is intent: ”Imitation is laziness or refusal to accept your influences. Inspiration is recognizing that influence and turning it into something new". So, you may copy, or steal an idea, perhaps change it a bit and then claim it as your own. But also, there is a difference between imitation and best practice. Because learning best practice may involve copying, say, traditional techniques or methods used and handed down by masters of the past. So, paradoxically, to generate and develop your own ideas in order to innovate you may both copy and steal from what already exists. And potentially more so with help of artificial intelligence, AI, that is, using generative AI as a powerful ideation tool for "appropriating" others' ideas. But also using AIdeation to explore further or be inspired by others' ideas. So the notion of "idea theft" seems distorted. That is, culture, both material and non-material has largely evolved through the borrowing and reinterpretation of previous ideas. Moreover, AI is not capable of stealing ideas in the same way that a human can. Idea theft, then, highlights the ethics of ideation. That is, for creatives to give credit, proper attribution or reference to someone or something that has influenced their creations.

The Aha! moment

The proverbial Aha! moment, or sudden insight, as typically experienced by creative practitioners, is a phenomenon that has been studied and theorised about by neuroscientists and cognitive psychologists for over a century. Most recent studies, however, show that insight is not a sudden flash that comes from nowhere. In fact, it is the result of the unconscious mind piecing together loosely connected bits of information stemming from prior knowledge and experiences and forming novel associations among them. That is, in contrast to analytical problem solving, which involves the use of a systematic process or logical reasoning to arrive at a solution to a problem, and where individuals can typically fully explain the steps taken to arrive at the solution, with insight, individuals cannot readily reconstruct the procedure followed to reach the solution..That is, problem solving by insight versus analysis are different in that creativity is not an insight moment although insight can produce creativity. The relationship, then, between insight and creativity is a controversial one. And so, the Aha! moments remain one of the most intriguing and unexplained processes of the human mind  Source: https://www.intechopen.com/chapters/65968

New AI tools signify ideation paradigm shift

The last twelve months have seen a rapid advance of artificial intelligence, AI, with the release of  consumer software applications such as ChatGPT and DALL.E. And as with most new pieces of technology, designers get new ideas from them. While current generations of AI use pattern detection or rule-following to help analyse data and make predictions, the advent of transformer architectures, developed by Google and Open AI has unlocked a new field: Generative Artificial Intelligence, GAI. which is a type of AI that can generate original content in the form of text, images, audio, video etc. However, GAI is not without risk, including issues relating to privacy and security, bias, transparency, copyright, and equal access. And so while ChatGPT can act as a fast and powerful research tool, designers must still ensure that they validate any critical information from reliable sources. Yet the benefits of GAI are obvious: For example, text-to-image generation can assist in rapidly creating visual representations of design concepts, including rapid prototyping allowing architects to explore various design alternatives and refine their ideas before investing significant time and resources in detailed design development. The large language model can also facilitate effective communication and collaboration among architects, clients, and other stakeholders conveying their ideas more clearly. In graphic design, by using ChatGPT, the tasks of choosing, say, colour palette, finding a catchy headlines, or deciding on a layout become easier and faster. Or, in product design, the model can generate multiple design options for a specific design task, giving the designer a variety of alternatives to choose from. All in all, the increasing use of GAI, both in education and professional practice signifies a paradigm shift in the world of AI where GAI has the potential to become an effective co-designer for content creation across disciplines.

Ideation and reflective design

The application of generative AI, GAI to design ideation raises the question what is the imapact of GAI on reflective design. That is, designers typically reflect on their actions, both during the design process ("thinking on one's feet") and after it, in what Schön calls reflection-in-action and reflection-on-action (The Reflective Practitioner, 1984). Moreover, reflective design, which implies critically thinking about the design process, suggests that designers need to reflect on the unconscious ways in how technology, such as GAI shape designers'  decision making, actions, and design proposals. For example, what is the impact of GAI, such as communication with a text-to-text model or text-to-image model, on designer ability to uncover hidden biases or limitations on design practice. In Reflective Design (Sengers et al, 2005) the authors argue, when designing with technology: 'Each person has preconceived notions shaped by their personal journey and that affects their practice as a designer. Thus, one needs to unpack personal ideologies and determine how they influence the reflective design processes.' Moreover, the authors state: 'Technology shouldn’t be viewed as the optimal medium for design; technology imposes its own set of constraints and values to its users which should be addressed.' The notion of the reflective practitioner, and reflective design, then, serve as a guide for designers in the age of AI too.

Solo or group ideation?

Design is typically a collaborative activity, at least for major projects that involve many stakeholders. But what about the early stages of the design process, in the phase of generating ideas? Does ideating on one's own result in substantially different ideas compared with ideating in a group (typically five or more people), including so called "brainstorming"? Research suggests that in the workplace people become less productive in a team compared to working alone. This phenomenon has been labelled "social loafing" whereby people are said to be less likely to feel personally accountable for the outcome of a task when they are working in group. So would the concept of social loafing apply to ideation? In other words, is there a change in motivation among ideators when the ideation task is shared in group work? This, of course, would depend on the task at hand, or the composition or dynamics of the group. Moreover, working together can motivate people to perform well but it can also lead to a loss of motivation because the individual contribution is not as visible. As in "who came up with the idea first, and who can take credit for it?". So ideating in group may be a mixed blessing. A way out of this dilemma would be to start ideating alone followed by discussing individual outcomes with the group.

The power of sound

Ideators typically generate ideas through words and images: "In the beginning was the word" or, "a picture says more than a thousand words". Or the dialogue between language (verbal) and visualisation (non-verbal), also described as the picture-word-cycle. Interestingly, the architect Frank Lloyd Wright, in 1930, worked on his lectures for Princeton University: 'I must say I have enjoyed writing them'. The six lectures didn't need images, he added. 'Personally, I've never cared much for illustrated lectures. If we might have some good music to begin with and end with - that might help'. Yet Wright's writing and public lecture series provided a new outlet for his ideas. Moreover, on the power of music, the pianist and conductor Daniel Barenboim, in the first of his five BBC Reith Lectures, in 2013, argued, in examining the transformative power of music in the world: 'In the beginning, there was silence. And out of the silence came the sound'. Furthermore, it has been said that in the beginning was sound. Sound began the whole thing, and in sound resides tremendous power. It opens doorways to other realities, for with the production of sound, energy can move from one system to another. Now, there's a thought for ideators: The power of sound stirring imagination and innovation. Source: Wright quote: Alofsin, A (2019) Wright and New York. New Haven: Yale University Press. Barenboim: https://danielbarenboim.com/the-phenomenon-of-sound/

AI feeding ideation

Generative artificial intelligence, GAI is already being employed as a tool by designers in what is referred to as machine co-creativity and augmented design, with the technology not only facilitating ideation but also used as a visioning tool (text-to-image software) for community engagement for public project. GAI, moreover, is capable of developing and handling the more practical aspects of design including detail plans, such as figuring out where plumbing and power lines belong in buildings..But there are limits to GAI applications, notably issues of quality and data bias - AI ‘learns’ by analysing existing data so it is afflicted by evidence of in-built bias - and underline the risk of, say, an entirely AI-planned housing project. That is, while GAI is useful as a tool to help create an architectural image, or to generate a seamless to-do-list for housing developers, it is not a blueprint for a complete building, at least for now.

Unmeasurable ideas

'A great building', wrote the architect Louis Kahn (1901-1974), 'must begin with the unmeasureable, go through measurable means when it is being designed, and in the end must be unmeasurable'. The same thinking, arguably, may be applied to ideation. That is, ideas that spring, or emerge out of dreams or relatedness. And whether the idea takes centre stage or appears in the margins, first thoughts seem unmeasurable. But, in the development of an idea, measurable means are employed to make the idea visible or comprenhensible, and this in order to express and communicate the idea, visually or verbally. And only as far as the idea is made real, that is, realised, does it become measurable. But not necessarily so because ideas often carry intangible qualities in which case the idea, in the end, must remain unmeasurable. Could it be then that the measureable idea begins where dreams end?

AI as co-design partner

Co-design, or participatory design is an approach to design which focuses on processes and procedures of design. It has been used in many settings and at various scales. Rooted in work with trade unions in the Nordic countries, this approach has also a political dimension of user empowerment and democratisation. From this persepctive, co-design requires the end user's participation: not only in decision making but also in idea generation. Moreover, it is argued that designers create more innovative ideas and concepts when working within a co-design environment with others than they do when creating ideas on their own. Interestingly, AI has now entered co-design environments through large language models, LLMs, such as chatbots, which are a type of artificial intelligence algorithm that uses deep learning techniques and masive data sets to understand, summarise, generate and predict new content. LLMs, then, are able to process vast amounts of text data, mostly harvested from the Internet providing a basis to generate and communicate new ideas and concepts. Moreover, LLMs use natural language to simulate human conversation.The model, then, as a conversational chatbot becomes effectively a co-design partner.

Synthetic ideation

Synthetic media is a catch-all term to describe video, image, text, or voice that has been fully or partially generated using artificial intelligence. So, synthetic media includes ideas that have been fully or partially generated and presented using generative artificial intelligence, GAI. Conversely, non-synthetic media can be any other media, that is, media that is produced by human input. Take a freehand idea sketch, for example. Drawn entirely by a human, the rough sketch would fall under non-synthetic media, or non-synthetic ideation. Now think of a human-generated freehand sketch which has been digitised and where an GAI image editor is used to add a feature to the sketch. This new (altered) image is partially generated by AI (the picture being modified by algorithms), and we can thus say the idea, as represented by the image is “synthetic.” Ideas thus generated, or synthetic ideation may pose the question; "who is better at ideation - GAI or human?" GAI has brought a revolution in image editing, that is, a technology that turns manual image editing processes into automtic or semi-automatic actions. GAI, then, in drawing on a vast and varied image bank (datasets), and at speed, is a powerful ideation tool. Yet GAI is not a simple substitute for human decision-making and to fully appreciate the idea, the discerning eye (judgement) will be human, at least for now.

Return of analogue design tools

The pandemic put constraints and limitations on in-person studio and workshop activites. But UK's top-rated architecture schools, The Bartlett and The Architectural Association, AA, at their respective end-of-year exhibition 2023, demonstrated a strong return to studio-based working and the use of analogue tools, such as freehand drawing, collaging, and physical modelling. At the Bartlett, the summer show incorporated a diverse range of student physical models, hand drawings and installations encouraging a thoughtful dialogue between students and visitors about the future of architecture and its part to play in shaping the world we live in. Similarly, the AA Projects Review expressed the variety of ways that the AA units and programmes explore physical objects. As part of the projects created by the students, they represented a year of physical experimentation across the school, and exploited a vast range of techniques and materials. Models within these projects were the result of a hands-on, iterative process that uncovers new methods of exploring the built environment. Thinking, doing and making in anlogue modes in architecture schools, then, have returned in a significant way, which is not to say that the use and power of digital tools have diminished but rather that analogue and digital tools and outcomes complement each other. Indeed, in addressing the many challenges to architects today - environmental, socio-cultural or economic, the students of both schools displayed skillful and imaginative use of both analogue and digital tools to materialise, fabricate and communicate their designs. Both shows are on view online.

Pandora's box

According to Silicon Valley mythology, Alphabet, the giant of Big Tech, and owner of Google, got so angry when TikTok, the video hosting service stole a march on Google-owned YouTube and gifted it to mankind. And so, Alphabet decided humans needed to be punished for this. Alphabet commanded Google, its search engine arm, to create a new chatbot using generative artificial intelligence, GAI. Crafted from silicon, GAI received the gifts of wisdom, beauty, kindness, peace, generosity, and health from Alphabet and the other tech giants. Or so it was thought. But GAI was also taught lying and trickery as evidenced in social media, such as Facebook, Twitter and Instagram. Yet the tech giants decided that GAI carried gifts for the humans. Humans, moreover, were created to be curious, and couldn’t stay away from GAI and the urge to apply it to everyday life. But horrible things then flew out of GAI, including greed, envy, hatred, pain, disease, hunger, poverty, war, and death. All of life’s miseries had been let out into the world. The last thing remaining inside GAI was hope. Ever since, humans have been able to hold onto this hope in order to survive the wickedness that GAI had let out. Moral sense: “Pandora’s box”, originally an artefact in Greek mythology, now means anything that is best left untouched, for fear of what might come out of it.

Prompting for ideas

The fast development of generative artificial intelligence, GAI - notably through chatbots, suggest that algorithms will increasingly become the starting point for ideation. That is, chatbots are useful for ideation in content creation. And so, rather than start idea sketching with a blank piece of paper, or making a sketch model out of, say, card, designers will go straight to keyboard prompting using chatbots such as ChatGPT. To ideate, then, is to prompt. But to do so is complementing rather than substituting analogue ideation tools because the human-to-human work aspect of designing is not easily replaced by GAI. The outcome, however, may go through further prompt iteration and becoming the AI generated idea, or "aidea". , or developed further through human design thinking and doing. Indeed, most recent research shows that working together with ChatGPT on complex creative tasks improves the individual's creative performance And so, GAI can be used to support and enhance human creativity

Photogenic drawing

Long before smartphones and digital cameras were everywhere, inventors in the 1830s and 1840s were experimenting with new ways to capture pictures. One innovator, William Henry Fox Talbot (1800-1877), a true polymath, on a visit to Italy in 1833, and using a camera lucida, this simple draftsman’s aid produced a refracted image of the Italian landscape superimposed on the pages of his sketchbook. It seemed a simple task to trace the features of the village buildings, lake, and distant mountains with his pencil. But Talbot was frustrated by his poor drawing skills that day with the camera lucida leading him to recollect his experiences ten years earlier with another drafting aid, the camera obscura—a small wooden box with a lens at one end that projected the scene before it onto a piece of frosted glass at the back, where the artist could trace the outlines on thin paper. The camera obscura, too, had left Talbot with unsatisfactory results, but both experiments prompted Talbot to jot down thoughts about experiments he could conduct at home to see if Nature, through the action of light on material substances, might be brought to draw her own picture. He called his new discovery “the art of photogenic drawing.” Talbot’s early photogenic drawings, however, remained fugitive, for they were only partially stabilized with a solution of salt. A more permanent means of “fixing” the image with hyposulfite of soda was proposed by Talbot’s friend the eminent scientist Sir John Herschel; “hypo” was adopted by Talbot for most prints beginning in the early 1840s and is still used today as a fixer for black-and-white photographs. Moreover, Talbot demonstrated the commercial viability of his invention by means of a photographically illustrated book, The Pencil of Nature, published in parts beginning in 1844. But more than this; whether a personal project or a brief set by a third party, photography can help generate, develop and communicate ideas. https://www.metmuseum.org/toah/hd/tlbt/hd_tlbt.htm

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, GAI, however, can help us generate new ideas and insights by analysing large amounts of data. So the current leading opinion is that GAI 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

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 upholds looking at a lot of books, stimulation by long walks, or allowing herself to do nothing - where 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, GAI, for example, AI image generators, is enabling GAI to develop ideas and concepts as well as to refine visual outputs based on mimicking of what is already there. GAI as an ideation tool, however, is only as good as the datasets it is trained on and therefore produce images that tend to be rather repetitive or "in the style of". But GAI's ability to mine vast open datasets on which GAI models are trained can be problematic in that such datasets also carry creative ideas, for example, visual style and likeness which have no copyright protection. GAI, then, poses both risks and opportunities for designers: risk, in posting creative work on the interent without getting due credit or compensation - or, opportunity, in using GAI technology to augment human imagination. However, in the bigger picture, where design is both a competitive and collaborative activity, and where creativity signifies empowerment, GAI and human intelligence can complement each other. Moreover, when successfully combined, GAI + human intelligence become collective intelligence, as suggested by Nesta’s Centre for Collective Intelligence Design.