A Primer on AI Powered Conversational Experiences and Language Models
summary
Covers a concrete flow: build an NLU bot with Dialogflow to extract intents, entities and slot filling and deploy it multi‑channel (voice, chat, email) at scale supporting millions of conversations; shows a real refund resolved by the bot. Deep dive on OpenAI GPT‑3 with experiments: achieve usable intent classification with as few as ~15 examples per intent, GPT‑3 can suggest new labels and predict multiple intents, and a head‑to‑head with two commercial engines yields similar accuracy when using the same curated English dataset.
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chapters (28)
- 0:00Introduction and speakers
- 1:49Resiliency story and AI context
- 2:50Challenges in conversational AI
- 5:25Intent classification deep dive
- 7:12Dialogflow scenario walkthrough
- 8:59Message types and customer experience
- 9:42Language models evolution and GPT-3
- 10:44Deep dive into GPT-3
- 12:07GPT-3 intent classification experiment
- 14:19Comparing commercial providers
- 15:22Bias and responsible AI design
- 16:49Future of AI and diversity call
- 17:32Q&A transition
- 18:55Introduction to observability
- 19:37Resilience in software systems
- 22:51Observability vs monitoring
- 26:26Metrics and alerting example
- 30:44Designing for observability
- 33:28Takeaways and conclusions
- 36:15Introduction to cybersecurity risk
- 37:49Cybersecurity risk program overview
- 41:43Engineering and data perspective
- 43:48Data discovery and analytics
- 45:34Workflow automation and orchestration
- 48:24Visualization and strategic insights
- 51:14Board-level reporting
- 52:15Adopting the risk program
- 55:22Closing and Q&A
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