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/