A deep dive into the future of natural language understanding systems with ex-Alexa and current Head of AI at Got It AI, Chandra Khatri
Presented by Deepgram
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Transformer-based NLU and the future of NLU systems
Chandra Khatri is the Head of AI at emerging startup, Got It AI. He joins us to share the future of NLU systems and how transformer-based models (explained in detail for the layperson) are heralding a new line of capabilities, making a new kind of customer experience possible.
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 wherein his team is building one of the first Autonomous Conversational AI systems. 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.
