Alexa Will Understand Users More Clearly From Now, Thanks To Machine Learning

Alexa has been the talk of the town this year with new features and improvements being added consistently by Amazon. There is almost everything that the voice-controlled assistant can do and for the commands that are still limited, the new features based on machine learning will make things easier for the users as Alexa can start recognising and understanding what the users wants to say with intelligent responses.

The main motive here is to make Alexa have minimal friction during conversation with the users and get to know them better during a conversation. This is exactly what the Alexa AI director of applied science, Ruhi Sarikaya, explained about in a blog post today:

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. Increasingly, though, as customers expand their conversational-AI horizons, they expect Alexa to interpret their requests contextually; provide more personal, contextually relevant responses; expand her knowledge and reasoning capabilities; and learn from her mistakes.

For example, if a customer says “What’s the weather in Seattle?” and, after Alexa’s response, says “How about Boston?”, Alexa infers that the customer is asking about the weather in Boston. If, after Alexa’s response about the weather in Boston, the customer asks, “Any good restaurants there?”, Alexa infers that the customer is asking about restaurants in Boston.

Earlier this week we launched in the U.S. a new self-learning system that detects the defects in Alexa’s understanding and automatically recovers from these errors. This system is unsupervised, meaning that it doesn’t involve any manual human annotation; instead, it takes advantage of customers’ implicit or explicit contextual signals to detect unsatisfactory interactions or failures of understanding. The system learns how to address these issues and automatically deploys fixes to our production systems shortly after.

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