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. You can follow him on Twitter @Ruhi_Sarikaya.

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Amazon is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong machine learning background to help build industry-leading Speech technology. Our mission is to push the envelope in Alexa's capability to understand customer's speech by not just transforming speech to text, but understand the non-lexical component of communication by speech, for example intonation, pitch and speed of speaking, hesitation noises, gesture, and facial expression to make Alexa more human-like.As a Scientist, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in understanding and solving prosody. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding.The ideal candidate is clearly passionate about delivering experiences that delight customers and creating solutions that are robust. Creating reliable, scalable and high performance products requires exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience building large-scale distributed systems. We value academic collaborations and encourage our scientists and engineers to publish in conferences and do open source contribution.
US, CA, San Francisco
The AWS Center for Quantum Computing is a multi-disciplinary team of scientists, engineers, and technicians, all working to innovate in quantum computing for the benefit of our customers. We are looking to hire a Quantum Research Scientist to understand and mitigate crosstalk and loss channels in superconducting qubit and hybrid quantum acoustic devices and their packaging. Candidates with a track record of original scientific contributions will be preferred. We are looking for candidates with strong engineering skills, resourcefulness, and a bias for action. Organization and communication skills are essential.Work/Life BalanceAt the AWS CQC, we understand that developing quantum computing technology is a marathon, not a sprint. Mental and physical wellness is encouraged within our team and throughout AWS. The work/life integration within Amazon encourages a culture where employees work hard and have ownership over their downtime. We are exploring more structured wellness elements including for example meditation, running group meet-ups, and other wellness tips.Mentorship and Career GrowthWe are committed to the growth and development of every member of the Center for Quantum Computing. You will receive career-growth-minded management and mentorship from a software and science team and also have the opportunity to participate in Amazon's science mentorship programs.Inclusive and Diverse CultureThe AWS CQC is intentional about attracting, developing, and retaining amazing talent from diverse backgrounds to build a world class team. We’re looking for a new teammate who is enthusiastic, empathetic, curious, motivated, reliable, and able to work effectively with their peers. With quantum computing being a new and growing initiative within AWS, you would have an opportunity to make an impact on our budding team culture.Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
US, WA, Seattle
What do you like most about shopping at Amazon? Huge product selection? Amazon’s catalog has billions of items. Despite its mind-boggling size and variety, it remains the best in the business. That’s no accident! We use state of the art machine learning techniques and cloud computing technologies to keep it that way. A vast and high quality product catalog is a key strategic asset for Amazon that sets us apart from our competitors. Come help us make the world’s best product catalog even better and influence the way millions of customers shop.In this role, you will own scientific solutions to a large set of customer-facing product catalog issues that influence critical business processes and product discovery. you will have an opportunity to lead state of the art machine learning algorithms on large datasets. You will need to lead & build Amazon scale applications running on Amazon Cloud that both leverage and create new technologies to process large volumes of data that derive patterns and conclusions from the data.We are seeking an Applied Science Manager who has a strong background in applied Machine Learning and AI, deep passion for building data-driven products; ability to communicate data insights and scientific vision, and has a proven track record of leading both applied scientists and software engineers to execute complex projects and deliver business impacts.In this role, you will:· Lead a group of applied scientists to deliver machine-learning and AI solutions to production· Advance the team's craftsmanship and drive continued scientific innovation as a thought leader and practitioner· Develop science roadmaps, run monthly/quarterly/yearly planning, and foster cross-team collaboration to execute complex projects· Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management· Hire and develop top talent, provide technical and career development guidance to both scientists and engineers in the organization
US, CA, Culver City
Prime Video is an industry leading, high-growth business and a critical driver of Amazon Prime subscriptions, which contribute to customer loyalty and lifetime value. Prime Video is used daily by a massive audience on Amazon's websites and through a variety of devices including the Kindle Fire, game consoles, smart TVs and Blu-ray players.The Prime Video Marketing Analytics team uses machine learning, econometrics, and data science to optimize Amazon’s media spending strategies on Amazon Originals and third party content, driving customer loyalty as well as enhancing lifetime value of our Prime members. We are looking for an applied data scientist to build innovative models for measuring the impact of marketing spending on customer engagement and Prime member acquisition. Key responsibilities of Prime Video applied scientists include the following:· Playing an integral role in developing a roadmap to expand and enhance marketing analytics of Prime Video· Optimizing media planning on Amazon Originals to grow Amazon Prime user engagement and acquisition· Improving model usability by analyzing customer behavior and by gathering data from business owners and other tech teams· Incorporating new data sources and implementing creative methodology innovations to improve model performance· Creating and tracking accuracy and performance metrics· Helping build production systems that take inputs from multiple models and support decision makingTo summarize, the applied scientist will join our team to generate scientific insights to guide Amazon’s digital-video marketing strategy. We use detailed customer behavioral data (e.g. streaming history) and detailed information about content (e.g. IMDb-sourced characteristics) to discern causality of advertising spending on customer engagement and Prime member acquisition, based on which we derive optimize media planning of multi-million dollar marketing budget.