In the ever-evolving world of artificial intelligence, one question lingers: Can human and AI creativity harmoniously coexist and complement each other to create a richer, more diverse landscape of innovation? Inspired by "Imagination Machine" by Martin Reeves & Jack Fuller, I embarked on a journey to discover the potential of combining human and AI imaginations. With the advanced AI chatbot GPT-4 as my guide, I explored the possibilities of this creative convergence. Contrary to the beliefs of some, AI systems like GPT-4 have already demonstrated an impressive ability to mimic human-like reasoning and imagination. For instance, GPT-4 was able to provide plausible explanations for why customers who liked Rambo also liked Fast & Furious, which challenged the "Imagination Machine" authors' assertion that AI could not understand such underlying dynamics.

Human creativity is the elixir that's powered civilisations down the ages. It's brought us untold breakthroughs in all sectors of the economy, from agriculture to healthcare, energy to mobility. Our imagination continues to be our saviour as the world's ageing population faces tough socioeconomic and environmental challenges. Imagination entails creating mental models of things that don't yet exist. This kind of innovation brought us the printing press, the steam engine, the light bulb, the telephone, the aeroplane, the TV and the PC.

The aim of this paper is to propose a new overarching challenge for AI: the design of imagination machines. Imagination has been defined as the capacity to mentally transcend time, place, and/or circumstance. Much of the success of AI currently comes from a revolution in data science, specifically the use of deep learning neural networks to extract structure from data. This paper argues for the development of a new field called imagination science, which extends data science beyond its current realm of learning probability distributions from samples. Numerous examples are given in the paper to illustrate that human achievements in the arts, literature, poetry, and science may lie beyond the realm of data science, because they require abilities that go beyond finding correlations: for example, generating samples from a novel probability distribution different from the one given during training; causal reasoning to uncover interpretable explanations; or analogical reasoning to generalize to novel situations (e.g., imagination in art, representing alien life in a distant galaxy, understanding a story about talking animals, or inventing representations to model the large-scale structure of the universe). We describe the key challenges in automating imagination, discuss connections between ongoing research and imagination, and outline why automation of imagination provides a powerful launching pad for transforming AI.