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.