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PRO WORKSHOP (AI): Evolution of Conversational AI: From Rules to Transformers Such as BERT and GPT-3

- PDT
AI DevWorld -- PRO Stage 4

Chandra Khatri
Got It AI, Chief Scientist and Head of AI

Chandra Khatri is one of the leading experts in the field of Conversational AI and Multi-modal AI. Currently, he is the Chief Scientist and Head of AI at Got It AI while also being the CTO of BITSAA Silicon Valley Chapter. He is best known for leveraging cutting edge research and technologies for transforming products thereby impacting hundreds of millions of users.

At Got It AI, he is leading the efforts of transforming the AI space by leveraging state-of-the-art technologies in order to deliver Self-Discovering, Self-Training, and Self-Optimizing products. Under his leadership, Got It AI is democratizing Conversational AI and related ecosystems through automation. Prior to Got-It, Chandra was leading various kinds of applied research projects at Uber AI such as Conversational AI, Multi-modal AI, and Recommendation Systems.

Prior to Uber AI, he was the founding member of the Alexa Prize Competition at Amazon, wherein he was leading the R&D and got the opportunity to significantly advance the field of Conversational AI, particularly Open-domain Dialog Systems, which is considered as the holy-grail of Conversational AI and is one of the open-ended problems in AI. Prior to Alexa AI, he was driving NLP, Deep Learning, and Recommendation Systems related Applied Research at eBay. He graduated from Georgia Tech with a specialization in Deep Learning in 2015 and holds an undergraduate degree from BITS Pilani, India (2012).

His current areas of research include Artificial and General Intelligence, Democratization of AI, Reinforcement Learning, Language Understanding, Conversational AI, Multi-modal and Human-agent Interactions, and Introducing Common Sense within Artificial Agents.


Conversational AI has been transforming various industries such as Automation, Contact Center, Assistants, and eCommerce. It has undergone several phases of research and development. Prior to the 1990s, most systems were purely based on rules. Then came machine learning based systems, however, it was still hard to manage multiple domains and scenarios. To address these issues, "Skills-based" and "Domain-Intent-Slot" based systems were proposed. Post 2013, Transfer learning and Deep Learning based systems further enhanced the performance substantially by scaling the system to millions of users across a variety of applications. Despite significant progress in the past decade, most systems rely on large amounts of data annotation for Language Understanding, configurations for Dialog Management, and templates for Language Generation. Within the last two years, Transformers based models such as BERT and GPT-3 have been used to depict the power of unsupervised learning and generative systems across all aspects of Conversational AI: Speech Recognition, Language Understanding, Dialog Management, and Language generation. In this talk, I will showcase how Conversational AI has evolved from rules to unsupervised and generative systems and what we can expect in the short and long-term future.