This is an image with six separate photos, one of an Amazon fulfillment center, another of female scientist Belinda Zend, a third of a Pi Day billboard in Times Square with an image of Marie Curie, a fourth with an image of Josh Miele at a computer, a fifth of an Alexa Echo device, and a sixth of a Formula 1 race car.
Images from some of the stories that captivated our readers in the first half of 2022, including Belinda Zeng (top row, middle), head of applied science and engineering, Amazon Search Science and AI, who earlier this year shared her thoughts on what it takes to succeed as a scientist at Amazon, and MacArthur Fellow Josh Miele (lower left), who has merged a lifelong passion for science with a mission to make the world more accessible for people with disabilities.

Ten stories from the first half of 2022 that captivated readers

From Josh Miele's passion for making the world more accessible to improving forecasting by learning quantile functions, these stories resonated with our audience.

  1. Josh Miele, in a purple dress shirt, sits at a desk in an office, he is typing and looking at a computer screen, there are chairs and desks in the background
    Josh Miele, an Amazon principal accessibility researcher, was selected a 2021 MacArthur Foundation Fellow. He has spent his career developing tools to make the world more accessible for people who are blind and visually impaired.
    Meg Coyle / Amazon

    In September 2021, when Josh Miele, an Amazon principal accessibility researcher, got a text from someone at the MacArthur Foundation requesting a phone call, his heart leapt. For anyone in the arts and sciences, a MacArthur Fellowship, known as the “genius” grant, is akin to winning the lottery.

    For Miele, who is blind and has spent his career developing tools to make the world more accessible for people who are blind and visually impaired, a MacArthur grant had long been a fantastical dream. Learn how he has merged a lifelong passion for science with a mission to make the world more accessible for people with disabilities.

  2. Belinda Zeng, the head of applied science and engineering at Amazon Search Science and AI, is seen standing outside in Costa Rica on a sunny day, a wire fence is just behind her in the foreground, and a valley and mountains are seen in the background
    Belinda Zeng is the head of applied science and engineering at Amazon Search Science and AI.
    Courtesy of Belinda Zeng

    Belinda Zeng, head of applied science and engineering at Amazon Search Science and AI, has participated in hundreds of interviews for science roles across the company.

    Earlier this year, she shared her thoughts on what it takes to succeed as a scientist at Amazon — including the lessons she learned as a Bar Raiser: experienced interviewers who help to raise the Amazon recruiting standard.

    Learn what the hiring team looks for and which three Leadership Principles stand out for scientists.

  3. Multilingual Alexa.png
    The MASSIVE dataset is a step toward the creation of multilingual natural-language-understanding models that can generalize easily to new languages.

    Amazon researchers released a new dataset called MASSIVE, which is composed of one million labeled utterances spanning 51 languages, along with open-source code.

    The release provides examples of how to perform massively multilingual NLU modeling and allows practitioners to re-create baseline results for intent classification and slot filling.

  4. Image shows the 2022 F1 car sitting in profile on a racetrack with viewing stands in the background
    The F1 engineering team collaborated with AWS to explore the science of how cars interact when racing in close proximity.
    F1

    When the 2022 FORMULA 1 (F1) racing season started in March, teams will took to the track with newly designed cars engineered to give fans — and drivers — more of the wheel-to-wheel action they’ve been seeking.

    Learn how the F1 engineering team collaborated with AWS to develop new design specifications to help make races more competitive.

  5. Protein graphs.png
    Examples of graph representations of proteins.

    At Amazon Web Services, the use of machine learning to make the information encoded in graphs more useful to customers has been a major research focus.

    In this post, AWS researchers showcased a variety of graph ML applications that customers have developed in collaboration with AWS scientists, from malicious-account detection and automated document processing to knowledge-graph-assisted drug discovery and protein property prediction.

  6. 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 is a mathematical function that takes a quantile (a percentage of a distribution, from 0 to 1) as input and outputs the value of a variable. It 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?” As such, the quantile function is commonly used in the context of forecasting questions.

    In practical cases, however, we rarely have a tidy formula for computing the quantile function. Instead, statisticians usually use regression analysis to approximate it for a single quantile level at a time. That means that if you decide you want to compute it for a different quantile, you have to build a new regression model — which, today, often means retraining a neural network.

    In a pair of papers presented at this year’s International Conference on Artificial Intelligence and Statistics (AISTATS), Amazon researchers describe an approach to learning an approximation of the entire quantile function at once, rather than simply approximating it for each quantile level.

  7. An overhead shot inside an Amazon fulfillment center shows hundreds of boxes on conveyor belts along with people monitoring the flow of those packages
    Amazon's scale makes picking the right package for each product a challenge. Fortunately, machine learning approaches — particularly deep learning — thrive on big data and massive scale. These tools have helped Amazon reduce per-shipment packaging weight by 36% and eliminate more than a million tons of packaging.

    Finding the right amount of packaging to ship an item can be challenging — and at Amazon, an ever-changing catalog of hundreds of millions of products makes it an ongoing challenge.

    Fortunately, machine learning approaches — particularly deep learning — thrive on big data and massive scale, and a pioneering combination of natural language processing and computer vision is enabling Amazon to hone in on using the right amount of packaging. Learn how these tools have helped Amazon drive change over the past six years, reducing per-shipment packaging weight by 36% and eliminating more than a million tons of packaging, equivalent to more than 2 billion shipping boxes.

  8. Block Corruption Detection.gif
    The initial version of Amazon Prime Video's block corruption detector uses a residual neural network to produce a map indicating the probability of corruption at particular image locations, binarizes that map, and computes the ratio between the corrupted area and the total image area.

    Streaming video can suffer from defects introduced during recording, encoding, packaging, or transmission, so most subscription video services — such as Amazon Prime Video — continually assess the quality of the content they stream.

    Manual content review — known as eyes-on-glass testing — doesn’t scale well, and it presents its own challenges, such as variance in reviewers’ perceptions of quality. More common in the industry is the use of digital signal processing to detect anomalies in the video signal that frequently correlate with defects.

    Three years ago, the Video Quality Analysis (VQA) group in Prime Video started using machine learning to identify defects in captured content from devices, such as gaming consoles, TVs, and set-top boxes, to validate new application releases or offline changes to encoding profiles. Learn how they've been applying the same techniques to problems such as real-time quality monitoring of thousands of channels and live events and to analyzing new catalogue content at scale.

  9. A screen grab of the Amazon Music website
    Since 2018, Amazon Music customers in the US have been able to converse with the Alexa voice assistant. Progress in machine learning has recently made the Alexa music recommender experience even more successful and satisfying for customers.

    Since 2018, Amazon Music customers in the US who aren’t sure what to choose have been able to converse with the Alexa voice assistant. The idea is that Alexa gathers the crucial missing information to help the customer arrive at the right recommendation for that moment. The technical complexity of this challenge is hard to overstate, but progress in machine learning (ML) at Amazon has recently made the Alexa music recommender experience even more successful and satisfying for customers.

    Learn how the Amazon Music Conversations team is using pioneering machine learning to make Alexa's discernment better than ever.

  10. Amazon Science celebrates Pi Day

    To mark Pi Day this year, Amazon Science utilized a Times Square billboard to honor scientists, engineers, and mathematicians past, present, and future.

    The billboard display ran from midnight to 8 a.m. and again — for 3 hours and 14 minutes — from 3:14 p.m. to 6:28 p.m. The display began by honoring Marie Curie, the first woman to be awarded a Nobel Prize in 1903 for her contributions to physics. It was Curie who once famously said, “Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.”

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US, WA, Seattle
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US, CA, Sunnyvale
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US, WA, Seattle
If you are excited about applying your science and engineering skills in business problems in the space of risk measurement, quantification, and mitigation, we invite you to consider this Applied Scientist opportunity within Amazon B2B Payment and Lending (ABPL). ABPL is seeking an Applied Scientist who combines their scientific and technical expertise with business intuition to build flexible, performant, and global solutions for complex financial and risk problems. You will develop and deploy production models to enhance our product features & processes that will delight our customers. Key job responsibilities - Apply advanced machine learning, deep learning and other analytical/scientific techniques to enable and improve Credit Management decisions - Source and assess various structured and unstructured data and leverage automated modeling framework to streamline data evaluation and integration - Spearhead leader to research and adopt State-of-the-Art AI/ML techniques and define the roadmap to revolutionize underwriting models leveraging adaptive modelling methods, Large Language Models(LLM), etc. - Bar-raising the design and implementation of production model pipelines(real time and batch) , lead design and code reviews to insist on high bar of engineering excellence and ensure high performance of the models - Collaborate effectively with Credit Strategy, Operations, Product, data and engineering teams. You will be advising and educating the leadership and stakeholders of the models and strategic decision making. - Understand business and product strategies, goals and objectives. Make recommendations for new techniques/strategies to improve customer outcomes. A day in the life As an Applied Scientist, you will design and build systems that support financial products. You will work closely with business partners, software and data engineers to build and deploy scalable solutions that deliver exceptional value for our customers. You will utilize intellectual and technical capabilities, problem solving and analytical skills, and excellent communication to deliver customer value. You will partner with product and operations management to launch new, or improve existing, financial products within Amazon.
US, WA, Seattle
Do you want to join an innovative team of scientists who use deep learning, natural language processing, large language models to help Amazon provide the best seller experience across the entire Seller life cycle, including recruitment, growth, support and provide the best customer and seller experience by automatically mitigating risk? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer interactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Are you excited by the opportunity to leverage GenAI and innovate on top of the state-of-the-art large language models to improve customer and seller experience? Do you like to build end-to-end business solutions and directly impact the profitability of the company? Do you like to innovate and simplify processes? If yes, then you may be a great fit to join the Machine Learning Accelerator team in the Amazon Selling Partner Services (SPS) group. Key job responsibilities The scope of an Applied Scientist II in the SPS Machine Learning Accelerator (MLA) team is to research and prototype Machine Learning applications that solve strategic business problems across SPS domains. Additionally, the scientist collaborates with project leaders, engineers and business partners to design and implement solutions at scale. The scientist focuses on components of large-scale projects, systems and products and can work independently and with the team to deliver successful solutions with medium to large business impact. The scientist helps our team evolve by actively participating in discussions, team planning, and by staying current on the latest techniques arising from both the scientist community in SPS, the larger Amazon-wide community, and beyond. The scientist develops and introduces tools and practices that streamline the work of the team, and he mentors junior team members and participates in hiring.