The discussion around General Artificial Intelligence is now mainstream. The recent achievements of inductive reasoning research, e.g., GPT-3 and Dall-e, have raised several questions in the academic community that span from ethics to sustainability, passing by the remaining problem of interpretability. Arguably, the issue lies in the fragmentation of different areas of AI, which trends like Neuro-Symbolic Reasoning and Knowledge-Infused Learning are trying to fix.
Stressing on the role of context, the research initiatives above are rediscovering the value of interconnected data. In these regards, the data management community is partaking the debate, supporting the development of data systems and technologies like knowledge representation, automated reasoning, and (recently) knowledge graphs. In this talk, I offer a humble data management perspective, which builds on the three pillars of data management: data (intuitively) to collect and model, questions (aka queries) to express and answer, and systems that allow storage of the former and answer the latter. I will illustrate my analysis throughout the Meme Analytics Project, an ongoing initiative that incarnates well the hardness of human-level intelligence.