Challenges in the Chatbot
Abstract
Chatbots will probably predominantly own the future of man-machine interface. We see them progressively taking over in Customer Care centers, Command and Control systems, and as Personal assistance. From the underlying framework point of view, one of the most interesting and challenging aspect is how to support bot builders in their quest for modularity and reuse, while at the same time keep the robustness of the bot intact, and the usage by the end user seamless and smooth. In this talk we will discuss these industry challenges, and see how IBM Watson Conversation tackles them.
Speaker
Ateret Anaby Tavor is a research staff member and the manager of the Language and Conversation group in IBM Research (with part of the team belonging to Watson Assistant). Her main responsibly focuses on devising the next generation of AI for Watson Assistant, Applying Machine Learning, NLU, and Algorithmic approaches to revolutionize the way chatbots are being built and maintained. Ateret also oversees research done in her team in the domains of Deep Learning and NLG. When joining the Watson group Ateret has led the team on launching IBM Watson Tradeoff Analytics, an innovative cognitive service that was part of the Watson Developer Cloud. Tradeoff Analytics combined smart visualization and analytical recommendations for easy and intuitive exploration of decision tradeoffs. Prior to this assignment Ateret’s research was focused on the future of business modeling tools.
Ateret has gained a lot of experience in Software Engineering, Machine Learning, Decision Analytics, and Operational Research. Ateret has numerous academic publications and patents.
She received M.Sc in Information Management Engineering (Cum Laude) from the Technion – Israel Institute of Technology.
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