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SE Radio 715: Sahaj Garg on Designing for Ambiguity in Human Input

SE Radio 715: Sahaj Garg on Designing for Ambiguity in Human Input

Software Engineering Radio - The Podcast for Professional Software Developers vor 4 Monaten

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48:02

Sahaj Garg, co-founder and CTO of Wispr, a
voice-to-text AI that turns speech into polished writing, talks
with host Amey Ambade about designing systems for the ambiguity
that's inherent in human input (text, voice, multimodal). Sahaj
focuses on concrete architectural and training strategies for
building robust AI systems. This episode examines the problem of
ambiguity, where it shows up, building robust systems,
personalization, communicating uncertainty, and evaluation. The
conversation starts by exploring the difference between inherent
and reducible ambiguity, major categories of ambiguity including
lexical, syntactic, and pragmatic, and the additional sources of
ambiguity in voice, such as homophones and accents. Garg details
how to build systems through model training, including providing
additional context and constructing datasets for good annotation.
They discuss personalization with a focus on "revealed
preferences"—learning from user behavior without explicit
feedback—and fighting the problem of AI writing that "regresses
to the mean." Finally, they consider how to communicate
uncertainty to users without degrading the experience, as well as
methods for evaluating ambiguity resolution through offline and
online signals.