des.ai.gn - Augmenting Human Creativity with Artificial Intelligence

Democratizing innovation became a term of MIT Professor Eric von Hippel, who, since the middle of the 70s, engaged himself with research and publications on the idea that users of products and services could create for themselves what they lacked instead of waiting on companies to provide it for them. In the last two decades or so, there has been a growing trend in the concept of immersing end-users in the innovation process, and now firms employ crowdsourcing and innovation competitions to come up with a flood of ideas. Nevertheless, it is established that several enterprises fail to realize these benefits due to four impending difficulties.

First, the initiatives to democratize innovation could lead to excessive evaluations. Thus, for example, crowdsourcing could bring numerous suggestions, many of which are later simply buried or never considered because firms cannot filter these or combine fragmentary or less original ideas but which, in synergy, could represent a major threat.

Second, companies may get blinded by the bandwagon effect, also known as the curse of expertise. The people dominantly carrying out the concept generation and concept identification - the integration representatives - are typically those who have more or less difficulties in generating or accepting new concepts simply because they possess an extensive rational knowledge domain.

Third, people who are not from a concrete domain can come up with fresh concepts and solutions but, in all likelihood, are unaware of the specifics of the given field, which would otherwise enable them to create the fleshed-out concept. Their minds lack the ability to turn abstract concepts, concepts that are often the messiest around, into structured patterns.

And last but not least, companies are unable to look at the big picture or the general outlook. Managers link together many compatible customer needs, but they cannot make a set of product configurations that will be beneficial to the community as a whole.

1. Promote Divergent Thinking

In this case, generative AI is appropriate for promoting divergent thinking since it can generate ideas based on the relations made between distant concepts. Here's an example of how we utilized Midjourney, which is a text-to-image model that is capable of finding analogical correspondence between objects in images by extracting semantic features and using them to recreate a picture that was generated from a text prompt by a person. Midjourney, ChatGPT, and Stable Diffusion were used as examples in this article; however, they are only a few of the many available generative AI technologies. When we asked Midjourny to generate an image of an elephant and a butterfly, it offered the image referred to as "phantasy" in this article.

We then fed the elaborate image from Midjourney to the next text-to-image generator, Stable Diff ion, to create prompts. The main ideas created through Stable Diffusion are presented below: chairs and artisanal chocolate candies.

Such an approach to generating a large variety of designs quickly and cheaply helps a firm to quickly check the viability of numerous product ideas. For instance, the generative AI that can be used in a clothing manufacturing firm with an inclination toward T-shirt design would ensure that the company constantly updates its products as per the current market trends.

Let us look at another instance of how this technology can help combine information and come up with concepts or ideas an individual or a team may never have thought of. I oversaw the generation of ideas with ChatGPT, which is a type of AI referred to as a generative model or large language model. We requested it to be created through trisociation, which is a process in which three unrelated concepts are joined together; this method is derived from bisociation, which is the joining of two divergent ideas to form a creative idea. Our team gave ChatGPT the following prompt: One of the arranged roles is ideator.

In the same round, ChatGPT created the nouns "airline" and "chair." When asked what we wanted, we said "university," and we got a sketch of an innovative business model that envisages convenient and cheap student and academic travel to conferences and workshops around the world, in addition to a library of books that the student can read during the flight. It suggested that the firm be named Fly and Study or Edu-Fly.

2. Challenge Bias and Expertise:

In the early stages of developing a new product, typical designs were created by generative AI, which gives the idea a designer should look beyond the preconceptions of the possibility of desirability in a product in terms of both function and form. This method can lead to solutions that humans might never thought using a traditional approach.

These inputs can assist in combating biases like design fixation that is over-reliance on the standard form of designs, functional fixedness, lack of ability in the mind to see that something other than its traditional use is possible, and the Einstellung effect, where earlier experiences hinder an individual from perceiving new solutions to problems.

Here is an example of this process. In fact, orchestrating positive emotions among organizational members can be a less demanding part than the actual task of building positive, emotion-focused relationships with outsiders. To Stable Diffusion, we requested generic designs of toys that relate to crabs but did not give them any practical specifications. Then, we reflected on the functional capabilities first when we had the designs in front of us. For example, in the set of crab toys drawn below, the cartoon in the top left could be turned into a toy that has a climber wall; the second cartoon could be a toy that shoots a small ball from one corner of the room to the other. The dish close to the middle featuring a crab on a plate can become a slow feeder dish for pets.

This is not a completely novel way to come up with unusual products: Many of the architectural themes in enterprises like Disney World and the function of many of the ridable features have been motivated by attempts at re-creation of scenes or characters in a narrative. However, generative AI tools can assist in initiating a company's fanciful strategic planning.

3. Assist in Idea Evaluation

There are other areas in the front end of innovation that can also be helped by generative AI tools, some of which are raising the level of abstraction of ideas and judging as well as selecting and, at some times, re-mixing ideas. Mull over the concept of an idea competition with the theme to focus on combating food waste. ChatGPT assessed the pros and cons of three raw ideas. Thus, relevant solutions are as follows: (1) smart packaging with temporal labels (labels that change either the dates or colors depending on the conditions in the places where they are located); (2) an application to promote food donations; and (3) public awareness through a campaign to explain the essence of various types of date labels and the meanings they convey in terms of food freshness and edibility. The interaction generated by ChatGPT contained the advantages and disadvantages of such ideas and seemed like a logical conversation between two interested individuals embracing the topic.

When ChatGPT analyzed dynamic expiration-date packaging, as an example, it suggested that this would assist consumers in differentiating between foods' shelf-life and Food manufacturers would also help in providing consumers with packages of small quantities that have a potential to be restocked more frequently in grocery stores. ChatGPT stated that due to the dynamic expiration dates, there is a high possibility that the process of manufacturing and packaging the products would have to change drastically, hence the costs for both the manufacturers and the consumers.

Therefore, ChatGPT concluded that the food-donation app means that people will use it before it expires and give unopened, edible food to people who need it. It pointed out the fact that the app may be useless, it needs a large consumer base, and the transportation and distribution of food from different unapproved sources may be risky.

It said that the pros of the education program for consumers were as follows: Consumers got more clues to the meaning of the various labels involving expiration, enabling them to make better decisions concerning food expenditure and food wastage. However, it also noted that this education program could be complicated since not all food items have definite best-before dates. It warned that while raising users' awareness of various forms of expiration dates may entail further expense to the program, especially when large scale, ranging from campaigns to educative material, the understanding achieved is highly advantageous.

There are some pros and cons of generative AI, which also helps humans evaluate dimensions of creativity like specificity, workability, novelty and many more. ChatGPT is asked to assess the example using these criteria.

Here is the assessment created by ChatGPT of the dynamic expiration data packaging:

Novelty: Smart packaging has been created to enhance the durability of foods and other perishable goods, and dynamic date labels are relatively new ideas.

Feasibility: It may be very difficult to practice as new packaging media and techniques will be needed. They also have to engage the food manufacturers and the regulators and distributors through which their products are sold.

Specificity: It is a local solution because it solves a common problem of food wastage by offering consumers better expiration dates for their food.

Impact: Potentially can help in the preservation of food which reduces food waste and increases food hygiene. Consumers would only utilize meager products until expiry if they received more correct expiration dates for the products.

Workability: The overall formation of a formal framework would probably have needed expertise and time, and the full benefits of the strategy may not be taken advantage of for two to three years.

Here is given by ChatGPT for the assessment of the app, which is for donating food that is close to expiration:

Novelty: Of course, nowadays there are other programs and organizations that are aimed at fighting the issue of food waste and distributing excess food among people.

Feasibility: The practice of donating food is already well developed, and there are models and the necessary structures for it.

Specificity: Precise in its goal of tackling food waste and problems of food waste management.

Impact: It has the potential to have a big impact, for it tries to solve two problems: hunger and food waste. In this way, it would be possible to solve two problems: food waste and hunger among those entities that are unable to afford means for a proper meal.

Workability: It is quite practical in that it can be easily applied and is not very expensive from the perspective of the company's finances. This would call for collaboration with the food producers, suppliers, and the food banks and, more significantly, a willing populace to receive and distribute the food items.

Here is the response by ChatGPT which evaluates the idea for users that are educated on different type of dates which is expired.

Novelty: Attempts have been made to sensitize consumers to what expiration dates mean and how they should store food to reduce the rate of expiration.

Feasibility: Highly feasible. It can be in form of awareness creation, production of information, education, communication, and initiatives.

Specificity: On the other hand, its objective is quite focused on ensuring that the consumers are informed with regards to the various kinds of dates that are associated with foods and the ways how to handle the preservation of food products to avoid spoilage.

Impact: Therefore, making consumers aware of the existence of the various dates and the proper methods of storing food makes them more conscious of food wastage and its prevention.

Workability: Very usable and can be easily applied to a specific business environment with comparatively small spending. Some of the prevention strategies include working with other food industry groups, the government, and other relevant organizations to come up with awareness materials.

Indeed, when deciding the three concepts, with ChatGPT's evaluations, it would not be that difficult to compare or sort them or even give a score based on the priorities.

4. Support Idea Refinement:

Important problems can be solved by Generative AI tools that face many contests: for example, grouping a large number of ideas to get a stronger one. Consider an instance where an organization tries to win a contract for a food waste reduction program, which can be used as a tool to group the three concepts. These are given as follows:

• The first solution can be the dynamic expiration dates, which aim to reduce food wastage. This consists of technology that is incorporated into packaging that can adjust the life of the product in real time. With the help of sensors, the expiration data can be updated on the basis of changing environmental factors, giving consumers an accurate estimate of the expiration of the product.
• "An app that allows consumers to donate food near its expiry will feed the needy while preventing the food from going to waste by giving unopened, still good food items to those in need, via organizations or institutions that will take them."
• "An education campaign that can inform the user on the kind of dates that are labeled on foods can assist the users in making beneficial decisions on the foods to purchase, consume or even discard since the labeled dates may indicate the safety of the food or the need to dispose it."

Finally, we provided ChatGPT with three programs: food waste, recycling, and reduction, and revealed the final question: Now combine all three programs into one food-waste program. In short order, it produced the following description: 'The solution is to develop smart packaging with changing expiration dates and introduce a program for food donation and the public awareness campaign on the usage of expiration dates of food products. This solution implies a decrease in food-waste levels, new technologies promoting the usage of surplus food, and proper education promoting sustainable usage of foods with minimal waste.

5. Facilitate Collaboration with and Among Users

In creating more ideas or designs of new products, generative AI may perform the role of enhancing interactions between a company's designers, on the one hand, and the users of a particular prospective product, as well as between the users themselves, on the other hand. That is, they make creating new offerings more affordable when clients work hand in hand with the firm.

For instance a business can provide users with tools for designing the product to enable the creation of a unique end product from the customer's input. Perhaps users' designs can be submitted to the crowdsourcing platform, where the merits of the design will be weighed by the crowd. Only the best designs could then be taken and incubated by other people to come up with better versions.

To explain the potential, it illustrates how a flying car- an invention that has been attempted for over a century without much success- might be designed. We gave Stable Diffusion this prompt: The brief was to design a flying automobile; thus, at Stable Diffusion, we produced several designs, and our chosen paper design was a vehicle in the lower right corner of the following image:

Next, we invited Stable Diffusion to reverse that and turn the design into a different concept, making the car look like a robot eagle.' The following images are the variations that the base generative AI model created instantaneously - ranging from the top left design that most represents a robot eagle to the rather realistic flying car design on the bottom right image.

A second example explains how designers can utilize tools of this kind to work together on variations of the structure's theme. Starting with the flying automobile design, all created by AI, they requested the tool to create designs that were more like a dragonfly design, tiger design, tortoise design, and eagle design (refer to the figure).

Another approach is for human collaborators with ideas of the product to engage in a tool like ChatGPT to form specificities of the product and in another tool like Stable Diffusion to get visual designs from a set of inputs initiated by a prompt that successively adds on information about the product. We gave ChatGPT a similar prompt to what we had given to Stable Diffusion: An example could be elaborating on a product: a flying automobile.

ChatGPT provided this description: 'The flying automobile is a vehicle that is designed and constructed for the most exciting adventure any moments because of the outlook that varies that of a stylish car that has smooth outlook and polished surface on the exterior but has helicopters' rotors hidden in it.

It goes without saying that when we gave that description to Stable Diffusion, it offered the image on the left. Then, we prompted ChatGPT with the instructions to expand the description and add the information that the product should resemble a dragonfly and should have illumination markers for night flights. It came back with the following: "Stretching its body, widening its wings and having concealed rotors, the transport looks like a realistic dragonfly that has come to life. The applied markers placed along the wings' and body's contours glow in the dark, which also contributes to the transport's visibility.

In other words, Stable Diffusion reinterpreted that description in terms of the various versions that kept the feasible design while adding the aspects of illumination based on the layout of a dragonfly's wings. The images below on the right are examples of how increasing the number of images in an item enhances the Item's relevance.

However, since the Dark Ages, presenting their concepts in either written or in drawn form limits a vast population from presenting newer concepts.