Top 10 blog posts of 2022

From edge computing and causal reasoning to differential privacy and visual-field mapping, the top blog posts of the year display the range of scientific research at Amazon.

Here, in chronological order, are ten of the year’s top blog posts — among the most popular and the most interesting.

  1. On-device speech processing has multiple benefits: a reduction in latency, lowered bandwidth consumption, and increased availability in in-car units and other applications where Internet connectivity is intermittent. Learn how innovative training methods and model compression techniques combine with clever engineering to keep Alexa’s speech processing local.

  2. The Prime Video app runs on 8,000 device types, such as gaming consoles, TVs, set-top boxes, and USB-powered streaming sticks, and updating the app poses a difficult trade-off between updatability and performance. Moving to WebAssembly (Wasm), a framework that allows code written in high-level languages to run efficiently on any device, helps resolve that trade-off, reducing the average frame times on a mid-range TV from 28 milliseconds to 18.

  3. Quantile function animation.gif
    The quantile function is simply the inverse of the cumulative distribution function (if it exists). Its graph can be produced by flipping the cumulative distribution function's graph over.

    The quantile function, which takes a quantile (a percentage of a distribution) as input and outputs the value of a variable, can answer questions like “If I want to guarantee that 95% of my customers receive their orders within 24 hours, how much inventory do I need to keep on hand?” Statisticians usually use regression analysis to approximate it, but a new approximation method lets the user query the function at different points, to optimize the trade-offs between performance criteria.

  4. In April, Amazon released a new dataset called MASSIVE, composed of one million labeled utterances spanning 51 (now, 52) languages, along with open-source code that provides examples of how to perform massively multilingual natural-language-understanding (NLU) modeling. By learning a shared data representation that spans languages, an NLU model can transfer knowledge from languages with abundant training data to those in which training data is scarce.

  5. image_to_bev.gif
    Every column of pixels in a digital image corresponds to a ray extending across a 2-D map of the field of view. The ray’s origin is the location of the camera on the map.

    Constructing a top-down “bird’s-eye” view of a scene on the basis of standard sideways-on photographs is an important problem for autonomous vehicles, which need to build maps of their immediate environments. At the International Conference on Robotics and Automation (ICRA), Amazon researchers and colleagues at the University of Surrey won the overall outstanding-paper award for a new approach to the problem that strongly improves on all existing methods on three different datasets.

  6. Neural networks learn by memorizing particular input-output relations, which helps them map out a space of possibilities, and then forgetting the memorized details as training examples accumulate. Enforcing differential-privacy constraints usually results in some performance drop, but letting a network memorize a little bit of public data, then training it on private data, enables it to meet differential-privacy criteria while cutting the resulting error increase by 60%-70%.

  7. Amazon researchers introduce a definition of “root cause” that uses graphical causal models to formalize the quantitative causal contributions of root causes to observed outliers. To attribute an outlier to a variable, they ask the counterfactual question “Would the event not have been an outlier had the causal mechanism of that variable been normal?” They treat each unobserved noise variable as a random switch that selects a deterministic function from a set of functions, enabling them to replace a deterministic mechanism in the graph with normal mechanisms.

    Causal circuits.png
    On the left, for the observed pair (xj, paj) of variable Xj and its parents PAj, the deterministic mechanism fj(1) of variable Xj is identified by the noise value (Nj = 1) corresponding to the pair (xj, paj). In the middle, a different value of noise (N= n) identifies a counterfactual deterministic mechanism fj(n). On the right, by drawing random samples of the noise term Nj according to some distribution, we assign “normal” deterministic mechanisms to Xj.

  8. Most large language models use decoder-only architectures, which work well for language modeling but are less effective for machine translation and text summarization. The 20-billion-parameter Alexa Teacher Model, which has an encoder-decoder architecture, outperforms language models more than 20 times its size on tasks such as few-shot article summarization and translation to and from low-resource languages.

  9. Regret bound.png
    An example of Bayesian optimization, in which γ is a set of hyperparameter configurations and f-hat is an empirical estimate of the resulting model error. The gap between the green and orange lines is the estimate of the upper bound on the optimizer's regret, or the distance between the ideal hyperparameter configuration and the best configuration found by the optimization algorithm.

    A machine learning model’s hyperparameter configuration can dramatically affect performance. Hyperparameter optimization (HPO) algorithms search the configuration space as efficiently as possible, but at some point, the search has to be called off. A new stopping criterion offers a better trade-off between the time consumption of HPO and the accuracy of the resulting models. The criterion acknowledges that the generalization error, which is the true but unknown optimization objective, does not coincide with the empirical estimate optimized by HPO. As a consequence, optimizing the empirical estimate too strenuously may in fact be counterproductive.

  10. From iterated SAT solvers to verifiable code, Amazon’s Byron Cook, Daniel Kröning, and Marijn Heule discuss the prospects for automated reasoning at Amazon.

Related content

US, CA, Palo Alto
The Amazon Search team creates powerful, customer-focused search and advertising solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, the Amazon Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide.
JP, Tokyo
The Amazon Logistics (AMZL) Team is responsible for the acquisition, design, construction, and management of all facilities in the Amazon Delivery Station Network. AMZL is looking for a talented and passionate Data Scientist to help shape its Last Mile business with technical strategies and solutions, by processing, analyzing and interpreting huge data sets. You should be comfortable with ambiguity, problem solving and enjoy working in a fast-paced, diverse and dynamic environment. Using analytical rigor and statistical methods, you mine through data to identify opportunities for Amazon and our delivery channels. And you collaborate with other scientists, engineers, Product and Program Managers to deploy new products and solutions. [More Information] Last Mile Department Data Analyst/BI Engineer Tokyo Office *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, visit https://www.amazon.jobs/disability/jp Key job responsibilities Creating a roadmap of the most challenging business questions and use data to articulate possible root cause analysis and solutions Managing and executing entire projects or components of large projects from start to finish including project management, data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights Partnering with Product, Program and Engineering teams to design and run models, research new algorithms, and prove incrementality and drive growth Understanding drivers, impacts, and key influences on seller growth dynamics Developing and scaling end-to-end ML Models and solutions Automating feedback loops for algorithms in production Utilizing Amazon systems and tools to effectively work with terabytes of data About the team Last Mile Execution Analytics (LMEA) team of JP works as an integral part of Amazon Logistics to ensure that its business intelligence, analytics, tools and planning needs are met. By providing information, insight, and decision support, we strive to enable success of all parts of AMZL. Our customer set includes senior management, station operations, external vendors, long-term planning, Ops technology (Voice of the Delivery Station, Voice of the Customer), network planning, and pretty much every BI and Ops teams. Voice of Employee [Work Life Harmony] We believe, it is important to spend private time such as spending time with your family or doing anything you like to spur innovation. Amazon promotes a fulfilling and flexible work style according to the work volume and lifestyle of each employee.
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
LU, Luxembourg
Are you a talented and inventive scientist with a strong passion about modern data technologies and interested to improve business processes, extracting value from the data? Would you like to be a part of an organization that is aiming to use self-learning technology to process data in order to support the management of the procurement function? The Global Procurement Technology, as a part of Global Procurement Operations, is seeking a skilled Data Scientist to help build its future data intelligence in business ecosystem, working with large distributed systems of data and providing Machine Learning (ML) and Predictive Modeling expertise. You will be a member of the Data Engineering and ML Team, joining a fast-growing global organization, with a great vision to transform the Procurement field, and become the role model in the market. This team plays a strategic role supporting the core Procurement business domains as well as it is the cornerstone of any transformation and innovation initiative. Our mission is to provide a high-quality data environment to facilitate process optimization and business digitalization, on a global scale. We are supporting business initiatives, including but not limited to, strategic supplier sourcing (e.g. contracting, negotiation, spend analysis, market research, etc.), order management, supplier performance, etc. We are seeking an individual who can thrive in a fast-paced work environment, be collaborative and share knowledge and experience with his colleagues. You are expected to deliver results, but at the same time have fun with your teammates and enjoy working in the company. In Amazon, you will find all the resources required to learn new skills, grow your career, and become a better professional. You will connect with world leaders in your field and you will be tackling Data Science challenges to ensure business continuity, by taking the right decisions for your customers. As a Data Scientist in the team, you will: -be the subject matter expert to support team strategies that will take Global Procurement Operations towards world-class predictive maintenance practices and processes, driving more effective procurement functions, e.g. supplier segmentation, negotiations, shipping supplies volume forecast, spend management, etc. -have strong analytical skills and excel in the design, creation, management, and enterprise use of large data sets, combining raw data from different sources -provide technical expertise to support the development of ML models to facilitate intelligent digital services, such as Contract Lifecycle Management (CLM) and Negotiations platform -cooperate closely with different groups of stakeholders, e.g. data/software engineers, product/program managers, analysts, senior leadership, etc. to evaluate business needs and objectives to set up the best data management environment -create and share with audiences of varying levels technical papers and presentations -deal with ambiguity, prioritizing needs, and delivering results in a dynamic environment Basic qualifications -Master’s Degree in Computer Science/Engineering, Informatics, Mathematics, or a related technical discipline -3+ years of industry experience in data engineering/science, business intelligence or related field -3+ years experience in algorithm design, engineering and implementation for very-large scale applications to solve real problems -Very good knowledge of data modeling and evaluation -Very good understanding of regression modeling, forecasting techniques, time series analysis, machine-learning concepts such as supervised and unsupervised learning, classification, random forest, etc. -SQL and query performance tuning skills Preferred qualifications -2+ years of proficiency in using R, Python, Scala, Java or any modern language for data processing and statistical analysis -Experience with various RDBMS, such as PostgreSQL, MS SQL Server, MySQL, etc. -Experience architecting Big Data and ML solutions with AWS products (Redshift, DynamoDB, Lambda, S3, EMR, SageMaker, Lex, Kendra, Forecast etc.) -Experience articulating business questions and using quantitative techniques to arrive at a solution using available data -Experience with agile/scrum methodologies and its benefits of managing projects efficiently and delivering results iteratively -Excellent written and verbal communication skills including data visualization, especially in regards to quantitative topics discussed with non-technical colleagues
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, CA, San Francisco
About Twitch Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate, learn, and grow their personal interests and passions. We’re always live at Twitch. Stay up to date on all things Twitch on Linkedin, Twitter and on our Blog. About the role: Twitch builds data-driven machine learning solutions across several rich problem spaces: Natural Language Processing (NLP), Recommendations, Semantic Search, Classification/Categorization, Anomaly Detection, Forecasting, Safety, and HCI/Social Computing/Computational Social Science. As an Intern, you will work with a dedicated Mentor and Manager on a project in one of these problem areas. You will also be supported by an Advisor and participate in cohort activities such as research teach backs and leadership talks. This position can also be located in San Francisco, CA or virtual. You Will: Solve large-scale data problems. Design solutions for Twitch's problem spaces Explore ML and data research
US, WA, Seattle
We are a team of doers working passionately to apply cutting-edge advances in deep learning in the life sciences to solve real-world problems. As a Senior Applied Science Manager you will participate in developing exciting products for customers. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the leading edge of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with others teams. Location is in Seattle, US Embrace Diversity Here at Amazon, 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. 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 Balance Work and Life Our 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 Mentor & Grow Careers Our 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. Key job responsibilities • Manage high performing engineering and science teams • Hire and develop top-performing engineers, scientists, and other managers • Develop and execute on project plans and delivery commitments • Work with business, data science, software engineer, biological, and product leaders to help define product requirements and with managers, scientists, and engineers to execute on them • Build and maintain world-class customer experience and operational excellence for your deliverables