If productive creativity involves putting pieces of information together in logical & useful ways, then why can't (AI) be written to accomplish it?

Productive creativity, which involves combining pieces of information in logical and useful ways to generate new ideas, solutions, or works, is a complex cognitive process that has been traditionally associated with human intelligence. While artificial intelligence (AI) has made significant advancements in various creative domains like art, music, and writing, achieving true productive creativity comparable to human creativity remains a significant challenge. There are several reasons why AI faces limitations in productive creativity: Lack of understanding: Human creativity often emerges from a deep understanding of the domain and the ability to draw upon diverse knowledge and experiences. While AI can process vast amounts of data, it lacks genuine comprehension and context in the way humans do. Novel connections: Human creativity often involves making unexpected and novel connections between seemingly unrelated pieces of information. AI, especially rule-based and narrowly focused systems, may struggle to make these creative leaps without explicit instructions or examples. Lack of motivation and emotion: Human creativity is often driven by emotions, motivations, and personal experiences. AI lacks consciousness, emotions, and personal motivations, which can limit its ability to generate truly innovative and emotionally resonant creative outputs. Goal-driven vs. open-ended: Many AI systems are designed to optimize for specific objectives or tasks. While they can be excellent at solving well-defined problems, they may struggle with open-ended, exploratory creative processes that lack clear objectives. Ambiguity and uncertainty: Creativity often involves embracing ambiguity and dealing with uncertain outcomes. AI tends to be more effective in deterministic environments where outcomes can be predicted with high certainty. Subjective evaluation: Assessing the quality and value of creative outputs often involves subjective judgment, which can be challenging for AI systems to handle. While AI has made impressive strides in creative tasks like generating art, music, and writing, these systems typically rely on large datasets and statistical patterns rather than true understanding and creativity. Researchers continue to explore ways to enhance AI's creative capabilities, but it remains a complex and multifaceted challenge to replicate the full spectrum of human productive creativity in machines.