The effective management of both knowledge and data lies – to a lesser or greater extent – at the heart of any successful application of Artificial Intelligence that yields a working, performant system. Though traditionally AI has been divided into two broad approaches, in this talk we argue that the Symbolic vs. Subsymbolic divide can be more effectively seen as a spectrum between perception and reasoning. The ideas behind Neuro-symbolic (or, more generally, Hybrid) AI have been developing for decades, and are currently maturing to the point where deployed intelligent systems can take advantage of the strengths of both sides of the divide, while seeking to avoid the pitfalls of purely data- or knowledge-driven models. After providing a brief overview of the Hybrid AI approach, we discuss several example R&D projects that adopt it, and the key results obtained in each case. We conclude by analyzing several challenges that lie on the path to engineering hybrid intelligent socio-technical systems, that are themselves both technical and social in nature.