Building a natural language interface for data has long been a dream for many in the data and AI spaces. However, prior to the Transformer model (and subsequent rise of Generative AI), AI was simply not sophisticated enough to handle natural-language queries in a meaningfully valuable way. Now that AI has advanced so much, the natural language interface for data is more valuable than ever before. This talk explores three key questions. First, what would a natural language interface for data actually look like? Second, what kind of value would it add to organizations using the Modern Data Stack? Third, what will the challenges look like when it comes to working with a natural language interface for data? Sarah Nagy will share real-world learnings from Seek's customers for each of these questions.
A former quant, Sarah founded Seek AI in 2021. Prior to starting Seek, Sarah most recently led the consumer data team at Citadel's Ashler Capital. Prior to joining Citadel, Sarah led the quant arms at two startups, Edison and Predata, which both successfully exited. Sarah started her career as a quant at ITG developing algorithmic trading strategies. Sarah has a Master in Finance degree from Princeton and dual Bachelor's degrees in Astrophysics and Business Economics from UCLA.