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Applying Natural Language Self-Service to the Public Sector

Tight budgets. Funding cutbacks. Population influxes. Policy changes.

These are just some of the challenges that government agencies face daily.

Given a fixed set of resources, whether it’s money, time or people, how can government agencies scale with ever-changing demands? How can agencies do more with less?

Listen to Chief Scientist, Ian Beaver, Ph. D., as he discusses the benefits of natural language self-service and deep conversational analysis specific to the public sector. 

With case studies from Amtrak, U.S. Army and more, Dr. Beaver explores how unexpected patterns in self-service language data can detect misunderstandings between what we think we know and reality, allowing agencies to ultimately improve efficiencies and optimize customer service.  

Presentation Topics:
  • Private and Public Sector Self-Service Differences 
  • Are IVRs or Website FAQs Enough?
  • The Captive User Effect
  • The Importance of Anonymity and Privacy by Design
  • Case Studies (Amtrak, U.S. Army & More)
  • Using Natural Language Self-Service Data Beyond the Original Use Case
  • Practical Public Sector Assessment Questions

 

Ian Image

Ian Beaver. Ph.D.

Chief Scientist

Since 2005, Ian has been publishing AI discoveries on topics surrounding human-computer interactions such as gesture recognition, user preference learning, and communication with multi-modal automated assistants. He regularly presents his work at academic and industry conferences and has authored over 30 patents within the field of human language technology. Currently, Ian is leading a team working to optimize human productivity by way of automation and augmentation, using symbiotic relationships with machines. Ian received his PhD in Computer Science from the University of New Mexico. He also holds a BS and MS in Computer Science from Eastern Washington University.