Over the last several decades, Citizen Science and crowdsourcing have matured as approaches to carrying out scientific research across a wide range of disciplines. At the same time, revolutionary strides have been made in machine learning and artificial intelligence (ML/AI) technologies. However, with larger instruments and new, ubiquitous sensor technologies, the data-verse continues to expand in volume and complexity. Making sense of these data at scale necessitates novel approaches that combine the complementary strengths of human and machine intelligences. We now can benefit from a confluence of technologies that can enable the integration of public participation in scientific research with computer science, data science, and ML/AI, as well as from the innovations in cyberinfrastructure and open science needed to support the cooperation between humans and machines. The combination of computational methods and technologies with citizen science and crowdsourcing also may raise potential ethical, equity, motivational, privacy, security, and data bias issues, which are critical to consider.