The founders of TrustGraph, Daniel Davis and Mark Adams, 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.

Topics:
0:00:00 Introductions
0:03:25 Mark's background
0:06:23 Are Knowledge Graph's more popular in Europe?
0:08:27 Past data engineering lessons learned
0:17:15 Knowledge Graphs aren't new
0:22:42 Knowledge Graph types and do they matter?
0:27:10 The case for and against Knowledge Graph ontologies
0:39:40 The basics of Knowledge Graph queries
0:45:42 Knowledge about Knowledge Graphs is tribal
0:47:50 Why are Knowledge Graphs all of a sudden relevant with AI?
0:53:45 Some LLMs understand Knowledge Graphs better than others
0:58:30 What is scalable and reliable infrastructure?
1:01:45 What does "production grade" mean?
1:04:45 What is Pub/Sub?
1:09:40 Agentic architectures
1:12:17 Autonomous system operation and reliability
1:16:50 Simplifying complexity
1:19:48 A new paradigm for system control flow
1:23:45 Agentic systems are "black boxes" to the user
1:24:55 Explainability in agentic systems
1:30:05 The human relationship with agentic systems
1:32:00 What does cybersecurity look like for an agentic system?
1:35:30 Prompt injection is the new SQL injection
1:37:00 Explainability and cybersecurity detection
1:39:40 Systems engineering for agentic architectures is just beginning

🔗 TrustGraph Links:
➡️ GitHub: https://github.com/trustgraph-ai/trustgraph
➡️ TrustGraph Config UI: https://config-ui.demo.trustgraph.ai/
➡️ Website: https://trustgraph.ai/
➡️ Discord: https://discord.gg/sQMwkRz5GX
➡️ Blog: https://blog.trustgraph.ai
➡️ LinkedIn: https://www.linkedin.com/company/trustgraph/