Intelligent Graphs: A Look at the Future of Enterprise AI

DVDC is happy to host its 1Q Roundup in partnership with our friends at Data Science DC (https://www.meetup.com/Data-Science-DC/), Columbia GraphDB (www.meetup.com/Columbia-GraphDB-MeetUp), GraphDB Baltimore-Washington (www.meetup.com/graphdb-baltimore/).

GraphAware (www.graphaware.com) is in town for (www.neo4j.com/graphtour) and wanted to give us a sneak peak on some of their projects. We have three interesting presentations for this evening.

Agenda:

6:30-7:00 Food and Networking
7:00-7:10 Announcements
7:10-7:30 Skynet: Building Your Own Doomsday Robot is Easy
7:30-8:00 Graph-Powered Machine Learning
8:00-8:30 Knowledge Graphs and Chatbots with Neo4j and IBM Watson
8:30-8:40 Q&A
8:40 Data Drinks at Circa Foggy Bottom

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Talk 1:
Skynet: Building Your Own Doomsday Robot is Easy
Speaker: Kevin Van Gundy

In this talk, Kevin will focus on helping developers find the lowest hanging fruit for AI and Graphs. This talk will first discuss on where graph databases are appropriate or inappropriate in an "AI-First stack." Then, Kevin will review architectural strategies for implementing intelligent graphs at scale as well as some incremental adoption pathways to get there.

Bio:
Kevin is the Head of Enterprise Deployment Strategy @Neo4j. He is voraciously passionate about data. He loves building intelligent systems-- whether it's automated trading bots, simplicial graph transformers, or even a good old fashioned recommendation engine. Whether graph technology is optimal solution or not, helping users succeed is where Kevin finds the most satisfaction.
Off the clock, Kevin an inept aspiring mountaineer and a bike racer. Most Saturdays you can find Kevin on a mountain somewhere or more likely at his favorite espresso bar clacking away on his keyboard on his latest project.

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Talk 2:

Graph-Powered Machine Learning
Speaker: Dr. Alessandro Negro

Abstract:
Graph-based machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques. Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.

Bio:
Dr. Alessandro Negro (https://twitter.com/alessandronegro?lang=en) is Chief Scientist at GraphAware. He has been a long-time member of the graph community and is the main author of the first-ever recommendation engine based on Neo4j. At GraphAware, he specializes in recommendation engines, graph-aided search, and NLP.

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Talk 3:

Knowledge Graphs and Chatbots with Neo4j and IBM Watson
Speaker: Christophe Willemsen

Abstract:
Knowledge Graphs are becoming the de-facto solution for managing complex aggregated knowledge, and Neo4j is the leading platform for storing and querying connected data. In this talk, Christophe will describe a graph-centric cognitive computing pipeline and detail the process from the ingestion of unstructured text up to the generation of a knowledge graph, queryable using natural language through chatbots built with IBM Watson Conversation.

Bio:
Christophe Willemsen (https://www.linkedin.com/in/christophe-willemsen-4a134a54/) is a Principal Consultant at GraphAware. He is an expert on the Neo4j graph database and the Cypher query language, and, as a software engineer, has been involved in many Neo4j projects. He is the author of the Neo4j driver for php and various Java extensions for Neo4j, available at https://github.com/graphaware.

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Food kindly sponsored by GraphAware