The exterior building of The Art Institute of Chicago at Michigan Avenue. There are people visible walking at the avenue.
With Art Museum, a visitor can say phrases such as, "I want to see a painting," "Bring me to sculptures from India," or "Show me another one like that," to navigate among pieces at the Art Institute of Chicago.
rafalkrakow/Getty Images

Making an art collection browsable by voice

The Art Museum skill uses Alexa Conversations, an AI-driven dialogue management tool.

The venerable Art Institute of Chicago is now welcoming visitors again after being closed for much of last year due to the COVID-19 pandemic. On Amazon's Echo Show, however, the museum is always open, thanks to the Alexa skill Art Museum. Created using the Alexa Conversations dialogue management model, Art Museum allows people to browse more than 300 pieces of art from the institute's collection via voice commands.

Alexa Conversations, which today is now generally available to developers in the US, is the first deep learning-based dialogue manager available for development of voice skills. It uses artificial intelligence to help developers create natural, human-like voice exchanges, bridging the gap between experiences that could be built manually and the wide range of possible interactions that might happen organically.

With Art Museum, a visitor can say phrases such as, "I want to see a painting," "Bring me to sculptures from India," or "Show me another one like that," to navigate among pieces. At the same time, subtle ambient audio — that hushed sound of people milling around familiar to anyone who has spent time in a museum — lends a sense of the physical environment.

The skill, made possible by the Art Institute of Chicago's public API, won grand prize in the Alexa Skill Challenge for Alexa Conversations last fall. Customers can access the skill by saying, "Alexa, open Art Museum".

Watch the Art Museum skill in action

"It's an awesome experience, especially in a time when we all have to stay at home, to be able to browse through an art museum in Chicago," said Arindam Mandal, director of Dialog Services for Alexa. "This was one of the first skills that had a conversational experience for browsing through art, where you felt like you were in the museum."

An innovative way to navigate media

Art Museum developers John Gillilan and Katy Boungard initially created a prototype for the concept during a hackathon at the AWS re:Invent conference in 2018. When the Alexa Conversations challenge came up last year, they recognized the opportunity to build on the idea of exploring a catalog of cultural assets in a new way.

Based in Los Angeles, Gillilan and Boungard do consulting work with media companies to explore the creative potential of voice and more natural, conversational AI.

"Voice is often utility-focused," Gillilan said. "We both always approached voice technology with a content and media sensibility. That's what excites us about the technology."

Coding for voice can be deceptively complex. Take, for example, something as simple as ordering a pizza. Someone placing an order might submit two data points at once by asking for a "medium pizza with two toppings." They then might decide to revise that order by saying something like, "make that a large." When all is said and done, a developer might be accounting for thousands of dialogue paths to fulfill one pizza order.

Alexa Conversations reduces the amount of code a developer needs to write by using deep learning to extrapolate different phrasing variations and dialogue paths based on samples the developer provides. For Art Museum, this enables art collections that are dynamically built based on simple requests from users — whether or not they are familiar with the art.

"When designing Alexa skills without Alexa Conversations, you really have to map and plan for what a user might ask for at every turn,” Boungard said. "Alexa Conversations allows you the flexibility to capture that without creating specific dialogue flows."

A user could ask to see French paintings, for example, and then suddenly decide to switch things up and ask for paintings from Italy. The context management Alexa Conversations provided helped make that sort of transition seamless, Boungard said. The developers also used AWS Rekognition to pull additional descriptive tags for how people might visually describe art, such as water, or tree.

The Art Institute of Chicago welcomed the new skill. "We were excited to make our API available to the public, because we knew people would build things that we wouldn't have conceived of ourselves," said Nikhil Trivedi, the institute's director of engineering. "Katy and John's Alexa skill is one of many examples we've started to see—a tool that combines an exploration of our collection with the rich trove of audio content we have developed over the years."

The AI behind Alexa Conversations

Up until now, tool kits for voice have "institutionalized the knowledge of building experiences that are linear, and they make it really easy to achieve those linear paths. That's why when you deploy them, they don't work very well if customers deviate from those linear paths,” Mandal said.

Science innovations power Alexa Conversations dialogue management

Dialogue simulator and conversations-first modeling architecture provide ability for customers to interact with Alexa in a natural and conversational manner. Learn more.

Instead, Alexa Conversations encourages developers to work backward from the natural dialogue experience they want to create. To help with that process, Amazon has published guidelines on authoring sample dialogues, starting with creating a simple exchange and customizing from there.

"At the heart of dialogue management, which is what Alexa Conversations is all about, is looking at a sequence of utterances and interpreting what is the best intent of the user at this turn, and what action should I take?" Mandal observed.

The core of Alexa Conversations rests on a deep learning model that can interpret language without having to be trained on all possible variations of it. The model is trained through simulated human and machine dialogues, so developers don't need to bring their own training data. Instead, they provide sample dialogues, also specifying when to invoke APIs along with their required arguments, so the dialogue manager can gather the information to trigger the developer’s skill code.

Alexa Conversations can "directly go from words to predicting the APIs," Mandal said. "That’s the future of authoring spoken dialog experiences with minimal developer effort."

Gillilan and Boungard said the flexibility of Alexa Conversations encourages a whole different way of thinking about how to design and build voice interactions. As Mandal noted, many developers have gotten used to thinking about voice experiences of all types in a linear way — that will change as it becomes easier to build more natural, flexible skills.

"I've worked on stuff before that is transaction-oriented where I've had to build that scaffolding by hand," Gillilan said. "Having Alexa Conversations for those projects would have made them a lot easier."

For more information on Alexa Conversations, visit the Alexa Developer blog.

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Our Alexa Product Advisor (part of Alexa Shopping) vision is to provide the best possible answers for a wide range of questions around product being asked by the customer. The first step in providing these answers is to form high quality classification and machine understanding of natural language questions into their core components (shape, product references, attributes, pronouns etc).Alexa Shopping is looking for an experienced Sr Data Scientist to be a part of a team solving complex natural language processing problems and customer demand insights (including segmentation analysis and personas building using big data, ML and potentially AI). This is a blue-sky role that gives you a chance to roll up your sleeves and dive into big data sets in order to build simulations and experimentation systems at scale, build optimization algorithms and leverage cutting-edge technologies across Amazon. This is an opportunity to think big about how to solve a challenging problem for the customers and understand their requirements for products.You will work closely with product and technical leaders throughout Alexa Shopping and will be responsible for influencing technical decisions in areas of development/modelling that you identify as critical future product offerings. You will identify both enablers and blockers of adoption for product understanding, and build programs to raise the bar in terms of understanding product questions and predict the shaping of customer utterances as we move from simple to complex utterances.The ideal candidate will have extensive experience in Science work, business analytics and have the aptitude to incorporate new approaches and methodologies while dealing with ambiguities in sourcing processes. Excellent business and communication skills are a must to develop and define key business questions and to build data sets that answer those questions. You should have a demonstrated ability to think strategically and analytically about business, product, and technical challenges. Further, you must have the ability to build and communicate compelling value propositions, and work across the organization to achieve consensus. This role requires a strong passion for customers, a high level of comfort navigating ambiguity, and a keen sense of ownership and drive to deliver results.
US, WA, Seattle
Amazon Performance Advertising sits at the intersection of e-commerce and advertising, developing new native advertising experiences that are a critical area of strategic focus for the company. Amazon Sponsored Brands is an always-on advertising product that amplifies brand content to shoppers researching on Amazon through prominent cost-per-click ads. As we move up the purchase funnel, and create more touch points for shoppers, Sponsored Brands plays a key role in the discoverability and reach of brand content. Amazon Sponsor Brands is looking for a scientist to lead innovation for our global advertising marketplace. At the heart of our advertising business are systems for optimizing the ad marketplace involving sourcing and targeting, experimentation infrastructure, AI methods for inference and control, as well as metrics-driven closed loop optimizations.Job Responsibilities:· Design, develop, and productionize end-to-end machine learning solutions.· Work closely with software engineers and data scientists on detailed requirements, technical designs and implementation of end-to-end solutions in production· Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management· Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving· Provide technical leadership, research new machine learning approaches to drive continued scientific innovation· Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences· Help attract, lead, and mentor technical talent.Impact and Career Growth:· You will invent new shopper and advertiser experiences, and accelerate the pace of Machine Learning and Optimization.· Influence customer facing shopping experiences to helping suppliers grow their retail business and the auction dynamics that leverage native advertising, this role will be powering the engine of one the fastest growing businesses at Amazon.· Define a long-term science vision for our ad marketplace, driven fundamentally from the needs of our customers, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This is a role that combines science leadership, organizational ability, technical strength, product focus and business understanding.Why you love this opportunity:Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.
US, VA, Arlington
We are developing advanced technologies that enhance the experience of shoppers in physical stores. Designed and custom-built by Amazon, existing products such as the Amazon Dash Cart and Amazon Go integrate a variety of advanced technologies including computer vision, sensor fusion, and advanced machine learning.We are seeking talented people who want to join an ambitious research and development program that who will help us define the future of physical retail. As a Principle Applied Scientist, you will architect systems that involve addressing complex technical challenges while meeting stringent performance and robustness goals. You will mentor other engineers and ensure they are producing state-of-the-art solutions while also continuing to advance their knowledge and capabilities. You will play an active role in guiding the development of research infrastructure for real-time machine learning and you will play a key role in translating technology solutions into real-world systems in partnership with other product organizations.You will tackle challenging situations every day and you’ll have the opportunity to work with multiple technical teams at Amazon. You should be comfortable with a high degree of ambiguity and have the communication skills necessary to articulate strategic approaches that both address immediate product needs and which set your team up for future success. Along the way, we guarantee that you’ll learn a lot, have fun, and make a positive impact on many customersIn this role:• You will encounter challenging, open-ended technical problems to solve, with solutions that span the entire implementation stack from devices to real-time algorithms to cloud-based services.• You will be able to demonstrate your strong technical and communication skills as well as your leadership abilities to deliver brand new customer experiences on behalf of Amazon.• You must be curious in the face of ambiguity and able to develop prototypes and perform experiments to create robust, scalable, distributed and fault-tolerant features to enhance the experience of Amazon customers around the globe.NOTE re: LOCATION: The main location for this job is the Washington DC metro region.
US, WA, Seattle
Within Alexa Shopping, we are forming a new, insurgent team to better serve Alexa customers who are less engaged with Amazon stores. This is a large cohort of customers who want to use Alexa as their Shopping assistant and we want to meet the particular needs of these customers. The first job is to understand these customers better, how they shop in and out of Amazon stores, how they use Alexa and Alexa Shopping features, and what gaps remain between their needs and the features we have today.A day in the lifeYou will use data science to better understand how we can fulfill Alexa Shopping’s aspirations to be the anywhere shopping AI for with a focus on customers who are less engaged with Amazon. In the first months on the job, you will hire a new team and undertake a deep dive on this customer cohort using data on Amazon shopping behaviors, Alexa feature usage and primary research on their needs. This deep dive will lead to specific marketing program, feature and product ideas. You will influence the most senior leaders in the Alexa Shopping organization to steer marketing and feature roadmaps in order to better serve these customers. In the first year, you will write several PRFAQs for new feature and product ideas and you will advocate for them at the highest levels of the organization.About the hiring groupAt Alexa Shopping, we strive to enable shopping in everyday life through technology innovations in both voice and screen applications, from organizing shopping to covering in the moment shopping needs. We allow customers to create, add to, and collaborate on shopping lists that can be used in any store and to instantly order whatever they need, by simply interacting with their Smart Devices such as Echo or Fire TV. Our products range from package tracking to shopping lists and instant ordering, and with these capabilities we seek to make advertising more actionable and useful to customers. The business is both focused on generating value for shoppers as well as advertisers.Job responsibilities· Use data science to understand the shopping behaviors and needs of Alexa customers who are less engaged in Amazon stores· Hire and develop a nimble team of insurgents· Identify marketing and product gaps between our current feature portfolio and the needs of this customer cohort· Develop and experiment with product, feature and program ideas (e.g. write PRFAQs) and advocate for them at the highest levels of leadership in the organizationAmazon 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, WA, Bellevue
Join us in a historic endeavor to make Computer Vision accessible to the world with breakthrough research!The AWS Computer Vision science team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops.AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world.Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, and 3D scene and layout understanding.We are located in Seattle, Pasadena, Palo Alto, Atlanta (USA) and in Haifa and Tel Aviv (Israel).Inclusive Team CultureHere 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 host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office. You will be responsible for developing and disseminating customer-facing personalized recommendation algorithms. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization.You will design deep learning algorithms that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside more senior team members to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization.Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide.Key responsibilities· Develop deep learning algorithms for high-scale recommendations problem· Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement.· Collaborate with software engineering teams to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency.· Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.