The founders of TrustGraph, discuss their journeys with big data, knowledge graphs, and data engineering. Knowledge graphs are hard to learn - no matter what Mark says, and he gives everyone a crash course on them, why querying graphs is tricky, and what makes for reliable data services. The conversation ends with a discussion of what makes for "explainable AI" and the future of AI security.
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Top comments (1)
The discussion on 'scalable and reliable infrastructure' raised an important point—does the industry have a clear path to making Knowledge Graphs production-ready at scale, or are we still in an experimental phase? Also, with 'explainable AI' becoming more critical as agentic systems scale, how do we balance the need for transparency with the increasing complexity of these autonomous architectures?