The role of context in redefining human-computer interaction

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.

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Photo Credit: TungCheung / Shutterstock

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?).

Earlier this fall, Rohit Prasad, Alexa AI vice president and head scientist, announced we would be implementing new Alexa self-learning techniques to help her learn at a faster pace. 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.

For example, during our beta phase, the system automatically learned to associate the utterance “Play ‘Good for What’” to “Play ‘Nice for What’”, correcting a customer’s error and leading to a successful outcome in requesting a song by Drake. This system 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. We’ll be looking to expand the use of this self-learning capability in the months ahead.

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Our vision is for Alexa to help you with whatever you need. Alexa skills and the developers who build them are incredibly important to that vision. There are now hundreds of thousands of developers and device makers building Alexa experiences, as evidenced by the more than 50,000 skills now available. In a post published earlier this year, my colleague Young-Bum Kim described the machine-learning system we’re using to perform name-free skill interaction, which lets customers more naturally discover, enable, and launch Alexa skills. For example, to order a car, a customer can just say, “Alexa, get me a car”, instead of having to specify the name of the ride-sharing service. This requires a system that can process many contextual signals to automatically select the best skill to handle a particular request.

We recently expanded the use of this system beyond the U.S.: customers in the U.K., Canada, Australia, India, Germany, and Japan can now discover and engage with select skills in a more natural way. For example, when customers in Germany say “Alexa, welche stationen kennst du?” (“Alexa, what stations do you know?”) Alexa will reply “Der Skill Radio Brocken kann dir dabei helfen. Möchtest du ihn aktivieren?” (“The skill Radio Brocken can help. Do you want to enable it?”).

With more than 20,000 smart-home devices from more than 3,500 unique brands now compatible with Alexa, smart home use cases especially benefit, as we combine customer, session, and device context to provide more-natural experiences for our customers. For example, if you own an Alexa-compatible iRobot Roomba robot vacuum and say “Alexa, start cleaning”, your Roomba will get to work. Previously, you would have to remember the skill by saying, “Alexa, ask Roomba to start cleaning.” We have enabled this more natural interaction style for a subset of smart home skills and will gradually make this available to more smart home skills and customers in the U.S

Additionally, my colleague Arpit Gupta described in a post earlier this year our solution to the problem of slot carryover, a crucial aspect of the context carryover capability we’ve phased into the Alexa experience this year. To engage in more natural spoken interactions, Alexa must track references through several rounds of conversation. 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.

We initially launched context carryover in the U.S. earlier this year. Recently we’ve extended this friction-reducing capability to customers in Canada, the U.K., Australia, New Zealand, India, and Germany.

Context carryover makes interactions with Alexa more natural, and Follow-Up Mode amplifies this experience by letting customers utter a series of requests without repeating the wake word “Alexa.” Follow-Up Mode depends on distinguishing the “signal” of follow-up requests from the “noise” of background conversations or TV audio. My colleague Harish Mallidi described the science behind Follow-Up Mode in a paper published this fall.

Earlier this year, we made Follow-Up Mode available in the U.S., and recently we’ve expanded its availability to Canada, the U.K., Australia, New Zealand, India, and Germany. Perhaps not surprisingly, we’ve found that customers who use Follow-Up Mode have more interactions with Alexa than those who don’t.

The road ahead

As I indicated in a previous post, we’re on a multiyear journey to fundamentally change human-computer interaction. It’s still Day 1, and not unlike the early days of the Internet, when some suggested that the metaphor of a market best described the technology’s future. Nearly a quarter-century later, a market segment is forming around Alexa, and it’s clear that for that market segment to thrive, we must expand our use of contextual signals to reduce ambiguity and friction and increase customer satisfaction.

About the Author
Ruhi Sarikaya is director of applied science, Alexa AI.

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We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide visible benefit to customers, this is your opportunity.Come work on the Amazon Prime Air Team!We're looking for an outstanding engineer who combines strong knowledge of aerodynamics with expertise in using CFD simulations to drive vehicle design. As a member of the high-fidelity aero team, you will have a direct hand in shaping our future drone designs and will interface closely with several other teams, including the conceptual design, wind tunnel, and controls teams.Responsibilities will include using CFD to aid in:· Vehicle-level conceptual and detailed design· Propeller design· Aerodynamic database generation· Wind tunnel support and validationDeveloping pre- and post-processing tooling is another key component to the role, so strong programming skills are encouraged.Export License ControlThis position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on Amazon’s ability to apply for and obtain an export control license on your behalf.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, VA, Arlington
Excited by using massive amounts of data to develop Machine Learning (ML) and Deep Learning (DL) models? Want to help public sector, medical center, and non-profit customers derive business value through the adoption of Artificial Intelligence (AI)? Eager to learn from many different enterprise’s use cases of AWS ML and DL? Thrilled to be key part of Amazon, who has been investing in Machine Learning for decades, pioneering and shaping the world’s AI technology?At Amazon Web Services (AWS), we are helping large enterprises build ML and DL models on the AWS Cloud. We are applying predictive technology to large volumes of data and against a wide spectrum of problems. Our Professional Services organization works together with our AWS customers to address their business needs using AI.AWS Professional Services is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. If you have experience with AI, including building ML or DL models, we’d like to have you join our team. You will get to work with an innovative company, with great teammates, and have a lot of fun helping our customers.If you do not live in a market where we have an open Data Scientist position, please feel free to apply. Our Data Scientists can live in any location where we have a WWPS Professional Service office.We’re looking for top architects, system and software engineers capable of using ML and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.The primary responsibilities of this role are to:· Design data architectures and data lakes· Provide expertise in the development of ETL solutions on AWS· Use ML tools, such as Amazon SageMaker Ground Truth (GT) to annotate data. Work with Professional Services on designing workflow and user interface for GT annotation.· Collaborate with our data scientists to create scalable ML solutions for business problems· Interact with customer directly to understand the business problem, help and aid them in implementation of their ML ecosystem· Analyze and extract relevant information from large amounts of historical data — provide hands-on data wrangling expertise· Work closely with account team, research scientist teams and product engineering teams to drive model implementations and new algorithms· This position can have periods of up to 10% travel.
US
Are you passionate about building successful Data transformations within the Public Sector? At Amazon Web Services (AWS), we’re hiring highly technical Data engineers to collaborate with our customers and partners on key engagements. Our consultants will develop and deliver proof-of-concept projects, technical workshops, and support implementation projects. These professional services engagements will focus on customer solutions such as Data and Analytics, HPC and more.In this role, you will work with our partners, customers and focus on our AWS offerings such Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena and more. You will help our customers and partners to remove the constraints that prevent them from leveraging their data to develop business insights.AWS Professional Services engage in a wide variety of projects for customers and partners, providing collective experience from across the AWS customer base and are obsessed about customer success. Our team collaborates across the entire AWS organization to bring access to product and service teams, to get the right solution delivered and drive feature innovation based upon customer needs.You will also have the opportunity to create white papers, writing blogs, build demos and other reusable collateral that can be used by our customers. Most importantly, you will work closely with our Solution Architects, Data Architects and Service Engineering teams.The ideal candidate will have extensive experience with design, development and operations that leverages deep knowledge in the use of services like Amazon Kinesis, Apache Kafka, Apache Spark, Amazon EMR, NoSQL technologies and other 3rd parties.This is a customer facing role. You will be required to travel to client locations and deliver professional services when needed (expected travel time is 20%)Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
US, WA, Seattle
Would you like to shape the future of the video entertainment industry for movies, TV and live sports events? Does solving complex problems within large scale, production systems excite you? If you answered yes, we have an opportunity for you!Prime Video is disrupting the traditional television and movie industry with a growing library of high-quality media. Prime Video launched in 2007 and has quickly become a strategic priority for the company, reflected in the service’s recent expansion into over 240 countries and territories worldwide.This is a big opportunity to apply Computer Vision and directly impact millions of customers.A day in the lifeIn your day-to-day activities in this role, you'll embrace the challenges of a fast paced market and evolving technologies, and develop Computer Vision and Machine Learning models to extract deep 2/3-D video-understanding of Prime Video content. You will be encouraged to see the big picture, be innovative, and iteratively develop technology to impact millions of our customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.About the hiring groupThe PV-CVML team is a group of Applied Scientists working on a diverse set of 2/3-D video understanding problems while partnering with various teams across Prime Video (PV). The most unique aspect of our team is the broad set of exciting problems we get to work on for our multiple stakeholders across the entire video-streaming vertical. If you want to work on technically cutting-edge problems with massive customer impact, then our team is the perfect fit for you!Job responsibilitiesAs a member of our team, you will apply Computer Vision and Machine Learning to problems that have cross-organizational technological impact. Your work will focus on cleansing and preparing large scale datasets, training and evaluating models and deploying them to production. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable with digging in to customer requirements as you are drilling into design with development teams.We would like you to build models that can perform 2D/3D scene-understanding of all video-content available on Prime Video using computer vision, natural language processing, deep learning and advanced machine learning algorithms. We need to solve problems across many cultures and languages and have a huge amount of human-labelled data as well as operations team to generate labels across many languages to help us achieve these goals. Our team consistently strives to innovate, and holds several novel patents and inventions in the motion picture and television industry. We are highly motivated to extend the state of the art.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.