Diffeo AI <> Tradecraft Forum

Mar 13, 2019

Arlington, VA

We are excited to share the second annual AI <> Tradecraft Forum. Leaders from NGA, US INSCOM, US CYBERCOM, MIT Lincoln Labs, Microsoft Federal, and Basis Technology will present on how AI, automation, and augmentation are helping make sense of ever-growing volumes of data.

Renaissance Hotel in Arlington, VA
Wednesday, March 13, 2019
8am to 3pm – Breakfast & Lunch Included!
Registration is Free


Presentations:

Dr. Pat Biltgen, Author of Activity Based Intelligence

David J. Gauthier, Director, Commercial and Business Operations Group, NGA Source
“Reaching All the Data for GEOINT”

Walter Paz, Department of the Army, G2
“OSINT Challenges, Successes and the Road Ahead”

Hannah MacKenzie-Margulies, Basis Technology, Rosette Product Manager
“Multilingual Analytics for Open Source and Dirty Data”

Andrew E. Newton, Technical Director, J2, US CYBERCOM,
“To Index or Not to Index? That is the Data Lake Question”

Robert Shelton, Jr., CTO, Microsoft Federal
“How AI and Continuous Monitoring Affect Force Protection”

Dr. Ian Soboroff, Group Leader, Retrieval Group, Information Access Division
“TREC: Measuring the Effectiveness of Information Analytics”

Bryan Stoker, Tech Director, NSA Operations Center (NSOC)
TBD

Hands-On Afternoon Workshops:

Open Source Web Research: Hands-On PAI Tradecraft and Tools
Focusing on the countries of India and Russia, Diffeo’s analyst team will teach the latest PAI exploitation techniques to research foreign government interest in critical DoD supply chains and political leadership goals.
Tools include: managed attribution with Authentic8/Toolbox, Cyber Triage, Recorded Future, Rosette, and more.

Machine Learning Tutorial: Python Vector Embeddings for Entity-Entity Relations
Diffeo’s machine learning team will provide a hands-on tutorial based in an iPython notebook adapted from our colleague Sameer Singh’s tutorial on vector embeddings for entity-to-entity relations. This clever use of matrix factorization combines unstructured documents with relations from a structured knowledge base to create dense vectors that encode the semantic meaning of relationships


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