Chris Doyle

CAI Meet Unsupervised Learning: Tools and Best Practices for Optimising NLU Performance for Improved CX

This session examines the demands on AIOps teams to maintain accuracy and NLU performance against the overwhelming volume and variation of user queries, the difficulty of translating this into reliable training data (without breaking things) and doing so in a timely manner that addresses both the demands of the business (for new scope and functionality) and the desire to improve overall satisfaction of conversational self-service experiences.

Leveraging a real-world example, we will present solutions for: • identification and treatment of confusion in NLU design • measurement of the accuracy of your model over time • minimising “learning delay” • reduction of learning workloads for humans • application of changesets into new NLU models • regression protection

In this live demonstration, prepared for both business and technical teams, we will evaluate the NLU design and benchmark the bot accuracy, produce a change set for identification and treatment of areas of confusion, automatically push the change set into the NLU model and evaluate the regression impact to confirm that the changes can be made safely.

Chris has a 20+ year history of leading delivery of software and business change projects across a variety of industries and functions, including government, insurance, airline, utilities and contact centre. His experience includes 6+ years of delivering advanced Conversational AI and chatbot implementations. From running projects for small agile teams through to large scale enterprise transformation initiatives, Chris combines a background in enterprise architecture and engineering, with a deep understanding of business and technical needs, offering a unique ability to bridge the gap between senior stakeholders, practical operational needs, technical constraints and delivery.

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