Incorporating Common Sense and Semantic Understanding within the Assistants
With advancements in Deep Learning and data collection techniques, we have built artificial agents which more functional such that they can perform significantly better than humans on well-defined and unambiguous tasks such as Atari games. However, they do poorly on tasks that are dynamic and seem straightforward to humans such as embodied navigation and performing open-ended conversations, even after training with millions of training samples. One key element which differentiates humans from artificial agents in performing various tasks is that humans have access to common sense and semantic understanding, which is learned from past experiences. In this talk, I will be presenting how incorporating common sense and semantic understanding significantly help the agents in performing a complex task such as house navigation. I will also showcase that the semantic embeddings learned by the agent mimic the structural and positional patterns of the environment.
Chandra Khatri is a Senior AI Scientist at Uber AI driving Conversational AI efforts at Uber. Prior to Uber, he was the Lead AI Scientist at Alexa and was driving the Science for the Alexa Prize Competition, which is a $3.5 Million university competition for advancing the state of Conversational AI. Some of his recent work involves Open-domain Dialog Planning and Evaluation, Conversational Speech Recognition, Conversational Natural Language Understanding, and Sequential Modeling.
Prior to Alexa, Chandra was a Research Scientist at eBay, wherein he led various Deep Learning and NLP initiatives such as Automatic Text Summarization and Automatic Content Generation within the eCommerce domain, which has lead to significant gains for eBay. He holds degrees in Machine Learning and Computational Science & Engineering from Georgia Tech and BITS Pilani.