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