Echo Show 10, Charcoal, UI.jpg
A a team of designers, engineers, software developers, and scientists spent many months hypothesizing, experimenting, learning, iterating, and ultimately creating Echo Show 10, which was released Thursday.

The intersection of design and science

How a team of designers, scientists, developers, and engineers worked together to create a truly unique device in Echo Show 10.

During the prototyping stages of the journey that brought Echo Show 10 to life, the design, engineering, and science teams behind it encountered a surprise: one of their early assumptions was proving to be wrong.

The feature that most distinguishes the current generation from its predecessors is the way the device utilizes motion to automatically face users as they move around a room and interact with Alexa. This allows users to move around in the kitchen while consulting a recipe, or to move freely when engaging in a video call, with the screen staying in view.

Naturally, or so the team thought, users would want the device to remain facing them, matching where they were at all times. “You walk from the sink to the fridge, say, while you're using the device for a recipe, the device moves with you,” David Rowell, principal UX designer said. Because no hardware existed, the team had to create a method of prototyping, so they turned to virtual reality (VR). That approach enabled Echo Show 10 teams to work together to test assumptions — including their assumption about how the screen should behave. In this case, what they experienced in VR made them change course.

Echo Show 10 animation

“We had a paradigm that we thought worked really well, but once we tested it, we quickly discovered that we don't want to be one-to-one accurate,” said David Jara, senior UX motion designer. In fact, he said, the feedback led them to a somewhat unexpected conclusion: the device should actually lag behind the user. “Even though, from a pragmatic standpoint, you would think, ‘Well, this thing is too slow. Why can't it keep up?’, once you experienced it, the slowed down version was so much more pleasant.”

This was just one instance of a class of feedback and assumption-changing research that required a team of designers, engineers, software developers, and scientists to constantly iterate and adapt. Those teams spent many months hypothesizing, experimenting, learning, iterating, and ultimately creating Echo Show 10, which was released Thursday. Amazon Science talked to some of those team members to find out how they collaborated to tackle the challenges of developing a motorized smart display and device that pairs sound localization technology and computer vision models.

From idea to iteration

“The idea came from the product team about ways we could differentiate Echo Show,” Rowell said. “The idea came up about this rotating device, but we didn't really know what we wanted to use it for, which is when design came in and started creating use cases for how we could take advantage of motion.”

The design team envisioned a device that moved with users in a way that was both smooth and provided utility.

Adding motion to Echo Show was a really big undertaking. There were a lot of challenges, including how do we make sure that the experience is natural.
Dinesh Nair, applied science manager

That presented some significant challenges for the scientists involved in the project. “Adding motion to Echo Show was a really big undertaking,” said Dinesh Nair, an applied science manager in Emerging Devices. “There were a lot of challenges, including how do we make sure that the experience is natural, and not perceived as creepy by the user.”

Not only did the team have to account for creating a motion experience that felt natural, they had to do it all on a relatively small device. "Building state-of-the-art computer vision algorithms that were processed locally on the device was the greatest challenge we faced," said Varsha Hedau, applied science manager.

The multi-faceted nature of the project also prompted the teams to test the device in a fairly new way. “When the project came along, we decided that that VR would be a great way to actually demonstrate Echo Show 10, particularly with motion,” Rowell noted. “How could it move with you? How does it frame you? How do we fine tune all the ways we want machine learning to move with the correct person?”

Behind each of those questions lay challenges for the design, science, and engineering teams. To identify and address those challenges, the far-flung teams collaborated regularly, even in the midst of a pandemic. “It was interesting because we’re spread over many different locations in the US,” Rowell said. “We had a lot of video calls and VR meant teams could very quickly iterate. There was a lot of sharing and VR was great for that.”

Clearing the hurdles

One of the first hurdles the teams had to clear was how to accurately and consistently locate a person.

“The way we initially thought about doing this was to use spatial cues from your voice to estimate where you are,” Nair said. “Using the direction given by Echo’s chosen beam, the idea was to move the device to face you, and then computer vision algorithms would kick in.”

The science behind Echo Show 10

A combination of audio and visual signals guide the device’s movement, so the screen is always in view. Learn more about the science that empowers that intelligent motion.

That approach presented dual challenges. Current Echo devices form beams in multiple directions and then choose the best beam for speech recognition. “One of the issues with beam selection is that the accuracy is plus or minus 30 degrees for our traditional Echo devices,” Nair observed. “Another is issues with interference noise and sound reflections, for example if you place the device in a corner or there is noise near the person.” The acoustic reflections were particularly vexing since they interfere with the direct sound from the person speaking, especially when the device is playing music. Traditional sound source localization algorithms are also susceptible to these problems.

The Audio Technology team addressed these challenges to determine the direction of sound by developing a new sound localization algorithm. “By breaking down sound waves into their fundamental components and training a model to detect the direct sound, we can accurately determine the direction that sound is coming from,” said Phil Hilmes, director of audio technology. That, along with other algorithm developments, led the team to deliver a sound direction algorithm that was more robust to reflections and interference from noise or music playback, even when it is louder than the person’s voice.

Rowell said, “When we originally conceived of the device, we envisioned it being placed in open space, like a kitchen island so you could use the device effectively from multiple rooms.” Customer feedback during beta testing showed this assumption ran into literal walls. “We found that people actually put the device closer to walls so the device had to work well in these positions.” In some of these more challenging positions, using only audio to find the direction is still insufficient for accurate localization and extra clues from other sensors are needed.

Echo Show 10, Charcoal, Living room.jpg
Echo Show 10 designers initially thought it would be placed in open space, like a kitchen island. Feedback during beta testing showed customers placed it closer to walls, so the teams adjusted.

The design team worked with the science teams so the device relied not just on sound, but also on computer vision. Computer vision algorithms allow the device to locate humans within its field of view, helping it improve accuracy and distinguish people from sounds reflecting off walls, or coming from other sources. The teams also developed fusion algorithms for combining computer vision and sound direction into a model that optimized the final movement.

That collaboration enabled the design team to work with the device engineers to limit the device’s rotation. “That approach prevented the device from turning and basically looking away from you or looking at the wall or never looking at you straight on,” Rowell said. “It really tuned in the algorithms and got better at working out where you were.”

The teams undertook a thorough review of every assumption made in the design phase and adapted based on actual customer interactions. That included the realization that the device’s tracking speed didn’t need to be slow so much as it needed to be intelligent.

“The biggest challenge with Echo Show 10 was to make motion work intelligently,” said Meeta Mishra, principal technical program manager for Echo Devices. “The science behind the device movement is based on fusion of various inputs like sound source, user presence, device placement, and lighting conditions, to name a few. The internal dog-fooding, coupled with the work from home situation, brought forward the real user environment for our testing and iterations. This gave us wider exposure of varied home conditions needed to formulate the right user experience that will work in typical households and also strengthened our science models to make this device a delight.”

Frame rates and bounding boxes

Responding to the user feedback about the preference for intelligent motion meant the science and design teams also had to navigate issues around detection. “Video calls often run at 24 frames a second,” Nair observed. “But a deep learning network that accurately detects where you are, those don't run as fast, they’re typically running at 10 frames per second on the device.”

That latency meant several teams had to find a way to bridge the difference between the frame rates. “We had to work with not just the design team, but also the team that worked on the framing software,” Nair said. “We had to figure out how we could give intermediate results between detections by tracking the person.”

By breaking down sound waves into their fundamental components and training a model ... we can accurately determine the direction that sound is coming from.
Phil Hilmes, director of audio technology

Hedau and her team helped deliver the answer in the form of bounding boxes and Kalman filtering, an algorithm that provides estimates of some unknown variables given the measurements observed over time. That approach allows the device to, essentially, make informed guesses about a user’s movement.

During testing, the teams also discovered that the device would need to account for the manner in which a person interacted with it. “We found that when people are on a call, there are two use cases,” Rowell observed. “They're either are very engaged with the call, where they’re close to the device and looking at the device and the other person on the other end, or they're multitasking.”

The solution was born, yet again, from collaboration. “We went through a lot of experiments to model which user experience really works the best,” Hedau said. Those experiments resulted in utilizing the device’s CV to determine the distance between a person and Echo Show 10.

“We have settings based on the distance that the customer is from the device, which is a way to roughly measure how engaged a customer is,” Rowell said. “When a person is really up close, we don't want the device to move too much because the screen just feels like it's fidgety. But if somebody is on a call and multitasking, they're moving a lot. In this instance, we want smoother transitions.”

Looking to the future

The teams behind the Echo Show 10 are, unsurprisingly, already pondering what’s next. Rowell suggested that, in the future, the Echo Show might show a bit of personality. "We can make the device more playful," Rowell said. "We could start to express a lot of personality with the hardware." [Editor’s note: Some of this is currently enabled via APIs; certain games can “take on new personality through the ability to make the device shake in concert with sound effects and on-screen animations.”]

Nair said his team will also focus on making the on-device processing even faster. “A significant portion of the overall on-device processing is CV and deep learning,” he noted. “Deep networks are always evolving, and we will keep pushing that frontier.”

“Our teams are working continuously to further push the performance of our deep learning models in corner cases such a multi-people, low lighting, fast motions, and more,” added Hedau.

Whatever route Echo Show goes next, the teams behind it already know one thing for certain: they can collaborate their way through just about anything. “With Echo Show 10, there were a lot of assumptions we had when we started, but we didn’t know which would prove true until we got there,” Jara said. “We were kind of building the plane as we were flying it.”

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Job summaryAmazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products are strategically important to our businesses driving long term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!The Machine Learning Optimization (MLO) team develops algorithms and systems that improve the performance and delivery of Amazon’s Display Advertising campaigns and automates campaign management using machine learning techniques. The team develops and deploys machine learning solutions that drive ad selection, bidding, user response prediction, and automated campaign management. Customers are advertisers and publishers who do business with Amazon.We own the system for batch training of user response prediction models, while the ad serving engineering team owns the real-time model scoring component. This teams owns the system for automated management of advertising campaigns, which can dynamically adjust parameters such as budget, bid prices, and targeting to optimize for campaign performance.As an Applied Scientist on this team, you will: Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity.Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience.Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.Run A/B experiments, gather data, and perform statistical analysis.Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.Research new and innovative machine learning approaches.Recruit Applied Scientists to the team and provide mentorship.Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video https://youtu.be/zD_6Lzw8raE Advanced degree in Computer Science, Mathematics, Statistics, Economics, or related quantitative field.Published research work in academic conferences or industry circles.Experience in building large-scale machine-learning models and infra for online recommendation, ads ranking, personalization, or search, etc.Effective verbal and written communication skills with non-technical and technical audiences.Experience working with large real-world data sets and building scalable models from big data.Thinks strategically, but stays on top of tactical execution.Exhibits excellent business judgment; balances business, product, and technology very well.Experience in computational advertising.Key job responsibilitiesYou will work on the next generation of our real-time pricing systems. These systems are optimizing the price of every individual opportunity on behalf of Amazon Advertising advertisers. A day in the lifeConduct offline analysis of data to guide design decisions with the product teamConduct A/B test setup and analyze results to guide rollout, go to market or development priority decisionsSuggest and implement models to sophisticate the advertising products we offer to our customersAbout the teamThe Ranking team is responsible for real-time pricing decisions on the Amazon RTB (Real-Time Bidding) system
US, WA, Seattle
Job summaryAmazon Worldwide Advertising is one of Amazon's fastest growing and most profitable businesses. The Advertising Console Product and Technology team is a group of creative individuals whose vision is to make the Amazon Advertising Console (AAC) the most loved and used tool for all advertisers to market and grow their businesses, brands, and products to a global customer base. The AAC is a collection of federated applications that combine to form the face and brand of Amazon Advertising. ACPT owns the delivery of the software, processes, and tools that allow teams across Amazon to build, support and enhance applications and features that deliver a cohesive advertising experience to all advertisers worldwide. By using our development kit and reusable components, developers can rapidly build features that integrate seamlessly within the suite of advertiser products. We use survey research, data science, machine learning, experimentation, and predictive modeling to understand advertiser dynamics, drive platform optimization, support evidence-based decision making, and help to develop predictive, intelligent features. As the Applied Science Manager on this team, you will: Lead of team of scientists, business intelligence engineers, etc., on solving science problems with a high degree of complexity and ambiguity.Develop science roadmaps, run annual 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 scientists and engineers in the organization.Analyze historical data to identify trends and support optimal decision making.Apply statistical and machine learning knowledge to specific business problems and data.Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed.Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.Build decision-making models and propose effective solutions for the business problems you define.Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication.Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video ~ https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Job summaryAre you excited about joining a team of scientists building lasting solutions for Amazon customers from the ground up? Our team is using machine learning, and statistical methods to take Amazon’s unique customer obsession culture to another level by designing solutions that change customers behavior when it comes to product search, discovery, and purchase. In order to achieve this, we need scientists who will help us build advanced algorithms that deliver first-rate user experience during customers’ shopping journeys on Amazon, and subsequently make Amazon their default starting point for future shopping journeys. These algorithms will utilize advances in Natural Language Understanding, and Computer Vision to source and understand contents that customers trust, and furnish customers with these contents in a way that is precisely tailored to their individual needs at any stage of their shopping journey. Key job responsibilitiesWe are looking for an Applied Scientist to join our rapidly growing Seattle team. As an Applied Scientist, you are able to use a range of science methodologies in NLP/CV to solve challenging business problems when the solution is unclear. For example, you may lead the development of reinforcement learning models such as MAB to rank content to be shown to customers based on their queries. You have a combination of business acumen, broad knowledge of statistics, deep understanding of ML algorithms, and an analytical mindset. You thrive in a collaborative environment, and are passionate about learning. Our team utilizes a variety of AWS tools such as SageMaker, S3, and EC2 with a variety of skillsets in shallow and deep learning ML models, particularly in NLP and CV. You will bring knowledge in many of these domains along with your own specialties and skilset.Major responsibilities:Use statistical and machine learning techniques to create scalable and lasting systems.Analyze and understand large amounts of Amazon’s historical business data for Recommender/Matching algorithmsDesign, develop and evaluate highly innovative models for NLP.Work closely with teams of scientists and software engineers to drive real-time model implementations and new feature creations.Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and implementation.Research and implement novel machine learning and statistical approaches, including NLP and Computer VisionA day in the lifeIn this role, you’ll be utilizing your NLP or CV skills, and creative and critical problem-solving skills to drive new projects from ideation to implementation. Your science expertise will be leveraged to research and deliver often novel solutions to existing problems, explore emerging problems spaces, and create or organize knowledge around them. About the teamOur team puts a high value on your work and personal life happiness. 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 you. 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 establish your own harmony between your work and personal life.