Author: Kyle Wiggers / Source: VentureBeat
Amazon’s Alexa is becoming more responsive, knowledgeable, and contextually aware. In a blog post ahead of an invited talk at the NeurIPS 2018 conference in Montreal, Ruhi Sarikaya, director of applied science at Alexa AI, detailed the progress Amazon’s made in the field of conversational artificial intelligence (AI) throughout the course of the year, and a few of the recent improvements it’s rolled out to Alexa-enabled smart speakers, televisions, set-top boxes, and other devices.
“There has been remarkable progress in conversational AI systems this decade, thanks in large part to the power of cloud computing, the abundance of the data required to train AI systems, and improvements in foundational AI algorithms,” Sarikaya wrote. “Substantial advances in machine learning technologies have enabled this, allowing systems like Alexa to act on customer requests by translating speech to text, and then translating that text into actions.”
Currently, Alexa relies on a number of contextual clues to resolve ambiguity, including historical activity, preferences, memory, third-party skill ratings and usage, session context, and physical context (i.e., the Alexa-enabled device’s location). To improve its precision further, Amazon this week launched a self-learning system that “detects the defects in Alexa’s understanding and automatically recovers from these errors” without the need for human intervention by “[taking] advantage of customers’ implicit or explicit contextual signals.”
Sarikaya said that during the beta earlier this year the AI system autonomously learned to associate the command “Play ‘Good for What’” with “Play ‘Nice for What’,” correcting a user’s misspoken request for a Drake song.
“This [AI] is currently applying corrections to a large number of music-related utterances each day, helping decrease customer interaction friction for the most popular use of Alexa-compatible devices,” Sarikaya said. “We’ll be looking to expand the use…
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