AI, Knowledge Representation and Graph Databases - Key Trends in Data Science

Knowledge Representation is a key focus for most modern AI texts. Many AI experts feel that over half of their work is understanding how to find the right knowledge structures to build intelligent agents that can continuously learn and respond to changing events in their world. In 2012, a paper published by Google started a consolidation of the many diverse forms of knowledge representation into a single general-purpose structure called a labeled property graph. This talk will describe the key events behind this movement and show how a new generation of data scientist will be needed to build and maintain corporate knowledge graphs that contain a uniform, normalized and highly connected data sets for used by researchers and intelligent agents. We will also discuss the challenges of transferring siloed project-knowledge to reusable structures.

Bio:

Dan McCreary is a Distinguished Engineer working for Optum. He has a background in data architecture, NoSQL, AI, NLP and Knowledge Representation. He has worked for both Bell Labs and with Steve Jobs at NeXT computer. Dan co-founded the “NoSQL Now!” conferences in 2014 and is the co-author of the book "Making Sense of NoSQL". Dan currently leads the knowledge graph projects within Optum. He is also interested in STEM education and volunteers with the CoderDojo projects in the Twin Cities. His latest project is teaching AI using low-cost racing cars. LinkedIn: https://www.linkedin.com/in/danmccreary/