The unprecedented rise in the adoption of artificial intelligence techniques and automation in many contexts is concomitant with shortcomings of such technology with respect to robustness, interpretability, usability, and trustworthiness. Crowd computing offers a viable means to leverage human intelligence at scale for data creation, enrichment, and interpretation, demonstrating a great potential to improve the performance of AI systems and increase the adoption of AI in general. Existing research and practice has mainly focused on leveraging crowd computing for training data creation. However, this perspective is rather limiting in terms of how AI can fully benefit from crowd computing. In this vision paper, we identify opportunities in crowd computing to propel better AI technology, and argue that to make such progress, fundamental problems need to be tackled from both computation and interaction standpoints. We discuss important research questions in both these themes, with an aim to shed light on the research needed to pave a future where humans and AI can work together seamlessly, while benefiting from each other.