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Saturday, December 29, 2018

Role of Context in Human Machine Interaction

Its always about context.  Every interaction has context, it can be deep or very simple.  Context also contains metadata, that is data that is required by the context,  Whether it be delivering an analytic business solution,  an assistant understanding and answering a question,  or a complex deep learning answer to a classification request.   It is also has a memory.   In some cases the long term memory of a database, or just the short term memory of the last statement or question posed in an interaction.

In the Alexa developer blog:

The Role of Context in Redefining Human-Computer Interaction  By Ruhi Sarikaya

Alexa Research  Alexa Science

In the past few years, advances in artificial intelligence have captured our imaginations and led to the widespread use of voice services on our phones and in our homes. This shift in human-computer interaction represents a significant departure from the on-screen way we’ve interacted with our computing devices since the beginning of the modern computing era.

 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. In an invited talk at the second NeurIPS workshop on Conversational AI later this morning, I’ll focus on the role of context in redefining human-computer interaction through natural language, and discuss how we use context of various kinds to improve the accuracy of Alexa’s deep-learning systems to reduce friction and provide customers with the most relevant responses. I’ll also provide an update on how we’ve expanded the geographic reach of several interconnected capabilities (some new) that use context to improve customer experiences.

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.

As conversational AI systems expand to more use cases within and outside the home, to the car, the workplace and beyond, the challenges posed by ambiguous expressions are magnified. Understanding the user’s context is key to interpreting a customer’s utterance and providing the most relevant response. Alexa is using an expanding number of contextual signals to resolve ambiguity, from personal customer context (historical activity, preferences, memory, etc.), skill context (skill ratings, categories, usage), and existing session context, to physical context (is the device in a home, car, hotel, office?) and device context (does the device have a screen? what other devices does it control, and what is their operational state?).  ....  '

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