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Chandra Khatri, Chief Scientist and Head of Conversational AI at Got-It.AI

Chandra Khatri, Chief Scientist and Head of Conversational AI at Got-It.AI

re-work.co 2021

The Surge of No-Code AI Platforms, Products, and Startups: Past few years were spent on building powerful Deep Learning/AI toolkits such as PyTorch and Tensorflow. Engineers are now ready to build the No-Code AI platform and product layers on top of existing toolkits, wherein users can simply provide their data and list/select the model through config or UI. AI models will not only be trained and served but also REST APIs will be exposed to applications. Got-It AI's No-Code, Self-Discovering, Self-Training, and Self-Managing platform is an effort towards Democratizing Conversational AI. Microsoft's recent "Lobe" app for anyone to train the AI model is an effort in that direction as well.

Generation of complex database queries and API calls from natural language utterances

Generation of complex database queries and API calls from natural language utterances

arxiv.org Dec 2020

Generating queries corresponding to natural language questions is a long standing problem. Traditional methods lack language flexibility, while newer sequence-to-sequence models require large amount of data. Schema-agnostic sequence-to-sequence models can be fine-tuned for a specific schema using a small dataset but these models have relatively low accuracy. We present a method that transforms the query generation problem into an intent classification and slot filling problem. This method can work using small datasets.

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