Ideation: free will versus determinism

What we understand a design idea to be is a complex question of material and social import, and an intricate play of the tangible and intangible identities. This suggests ideation is a question of hybrid experiences that might not be strictly casual because, like consciousness, what we think of as human consciousness is non-algorithmic and a product of quantum physics effects inside our brains and generating conscious thoughts. Now, an algorithm is simply a series of predictable steps to reach an outcome, and in the study of philosophy, this idea plays a big part in questions about free will versus determinism. So, are our brains simply cranking out math-like processes, or is something wild happening that allows us true free will, meaning the ability to ideate meaningfully and make different decisions that affect our lives?

Communicating ideas

The way we communicate ideas is crucial to ideation. It is a skill, and how we use words and images for idea communication also shape how the idea is perceived by others. While "A picture is worth a thousand words", images alone are open to interpretations whereas the choice of words and phrases, at worst can trigger negative reactions to the idea. Therefore, conceive the idea presentation as an interplay of words and images where language and pictorial representation are interdependent and supportive of each other. In other words, idea communication can be construed as a word-picture conversation that facilitates the idea gaining traction and acceptance.

Computational ideation

Although AI is currently limited in its creativity, using machine learning algorithm as an ideation tool is slowly gathering traction. For example, there are open-source AI models with image-generation capabilities that use AI to design sculptures or create paintings that mimic great works of art. These capabilities aren’t just relevant to fine art, however, but have the potential to explore and test out new ideas and accelerate prototypes across design disciplines. Although new forms of algorithmically driven creativity are being developed, most of the AI field is focused on manually designing the building blocks of an intelligent machine, such as different types of neural network architectures and learning processes. But it’s unclear how these might eventually get bundled together into a general intelligence. Moreover, experts point out that teaching computers to be creative is inherently different from the way humans learn to create, although there’s still much we don’t yet know about our own creative methodology. Instead, others argue, more attention could be paid to AI that designs AI. That is, algorithms will design or evolve both the neural networks and the environments in which they learn by analogy with biological evolution.Yet, as suggested by IBM technologists, the goal is not to recreate the human mind but to develop the techniques of interacting with humans that inspire creativity in humans. That is, the augmentation of creativity, and how to get better efficiencies. So, AI can offer many benefits serving as a smart, efficient and inspirational assistant. Or, AI as an ideation tool.

Distributive ideation

Artificial Ideation, Aid, or the use of artificial intelligence to help generate ideas is in the early stages of development and dependent on further advancement in hardware and software programs, including programmable processors, open data (cloud storage, and historical data sets) and deep learning and artificial neural networks. Yet Aid is attractive in that it has the potential for offering a more efficient ideation process, and both in terms of range and speed of new ideas for innovation. But training artificial intelligence models in huge data centres, here ideas centres, might restrict or hold back Aid serving local needs or conditions (data discrimination or input bias). This suggests a decentralised model of Aid, or distributive ideation that would rely on decentralised rather than centralised computing power to facilitiate and encourage ideation at, say, smartphone level. Although distributive ideation would have less processing power than the hardware accelerators used in data centres, it would facilitate bottom-up rather than top-down ideation encouraging wider participation and collaboration in the design process. Also, distributive ideation would consume less energy and therefore have a positive impact on reducing carbon emissions. (This blog was triggered by researchers in University of Cambridge's Department of Computer Science and Technology set out to investigate more energy-efficient approaches to training AI models.)

Ideation as thinking model

Design ideation, where 'idea' is understood as a basic element of thought could be perceived as a combination of first and second order, or level thinking. That is, from first thought that springs to mind ('surface level thinking') to thinking about the consequences of the idea ('deep level thinking'). Or, to use the analogy of a two-stage explorative rocket, from initial ignition, or spark of ideas, which is fast and has immediate impact, to a sustained effort to develop and effectively communicate the idea bringing the idea into orbit, so to speak, for further discussion and decision making (third level thinking) . The second stage would typically need more than one written sentence or a single rough sketch. Accordingly, the ubiquitous 'brain storming' session would be considered first order thinking whereas desgin ideation would entail second order thinking too. Or, to use another metaphor, from ideas surfing to ideas diving.

Ideation from home

The free flow of ideas highlights how ideation realistically doesn't only happen in the studio, the office or at home, but in many 'other places'. But working remotely from home has been criticised for reducing new ideas and innovation. That is, or so the argument goes, work becomes more siloed, close team interaction diminish over time and new ideas struggle to get in whilst groupthink flourish. Accordingly, to counter remote working from home, people should be back in the office where they can see each other in person, talk to each other and connect face-to-face. Yet the trend to work from home seems strong, facilitated by technology, from video conferencing to Cloud collaboration. But the question remains; what happens to ideation in the working from home scenario. Because ideas are not just abstract but visual and concrete too which call for diverse ways of working and communicating including hands-on activities and person-to-person collaboration.

Good ideation habits

Although experiments suggest that scientists problem-solve by analysis, whereas designers problem-solve by synthesis, there's is synergy in combining the two with each discipline learning from each other's approach to problem solving. So, to find a solution to a hard problem or perform a hard task, scientists typically structure the problem or task in a way that allow success. For example, a hard task such as interpreting a complex set of data might involve a structured process of retrieving, selecting and checking background facts (in some contexts, designers’ ability to understand and interpret data is becoming increasingly important). The better the scientist knows these facts, and the more effectively they devise an efficient plan to evaluate them, the more readily they will solve the problem. As they do more problems, the facts come to mind more easily, and they follow familiar plans to evaluate each. In general, structuring hard problems get better with experience. This is one reason that practice makes scientists more efficient and successful at hard tasks, and why experts outperform novices. Finding work habits that encourage this process helps staying focused. That includes staying with the task and taking breaks. It’s not helpful to insist on trying to get everything done at once, if it just isn’t working. Taking a break, then, might allow new concepts and structures to be considered allowing time for gradual development, or incubation to help problem solving. Furthermore, interacting with others can help conceptualise a problem in new ways. Talking to people with diverse backgrounds, perspectives and viewpoints can be a powerful way to break out of a rut and make progress. Moreover, spontaneous interactions and making time for informal discussion over work can be helpful and avoid isolation. In short, problem-solving habits formed by scientists could be applied in the design field, and many designers have been inspired to adopt or develop such habits. Source: https://www.nature.com/articles/d41586-021-00606-x?utm_source=pocket-newtab-global-en-GB