Natural language understanding (NLU) is an active area of research within task-oriented conversational agents. The recent emergence of deep learning applications for NLU has dramatically increased the accuracy of virtual assistants and their ability to successfully conduct task-oriented dialogues with people.
Rakesh Chada introduces x.ai’s Amy, an AI assistant that schedules meetings via email by understanding customers preferences and constraints. It is optimized to minimize dialogue ping pong while maximizing success in scheduling a meeting. Amy is not a prototype. It is actively deployed in a production environment and used by paying customers. Since this artificial intelligence interacts with people through emails, NLU performance is central to its design. Incoming emails undergo automatic text information extraction, semantic understanding, and contextual resolution.
Rakesh discusses Amy’s architecture and the various challenges the team faced during its design and shares several machine learning approaches for intent classification. Rakesh concludes by exploring a novel method for error optimization in a conversational agent that exploits customer error tolerance.
Rakesh Chada is a data scientist at x.ai building machine learning systems to understand human intents from emails. He holds a master’s degree in computer science from Stony Brook University with focus on machine learning and natural language processing, where he worked on question-answering systems, Wikipedia graph mining, topic modeling, and the like under Steven Skiena.
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