Publication

Cynical Selection of Language Model Training Data

By Amittai Axelrod
2017
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Abstract
The Moore-Lewis method of “intelligent selection of language model training data” is very effective, cheap, efficient... and also has structural problems. (1) The method defines relevance by playing language models trained on the in-domain and the out-of-domain (or data pool) corpora against each other. This powerful idea – which we set out to preserve – treats the two corpora as the opposing ends of a single spectrum. This lack of nuance does not allow for the two corpora to be very similar. In the extreme case where the come from the same distribution, all of the sentences have a Moore-Lewis score of zero, so there is no resulting ranking. (2) The selected sentences are not guaranteed to be able to model the in-domain data, nor to even cover the in-domain data. They are simply well-liked by the in-domain model; this is necessary, but not sufficient. (3) There is no way to tell what is the optimal number of sentences to select, short of picking various thresholds and building the systems. We present “cynical selection of training data”: a greedy, lazy, approximate, and generally efficient method of accomplishing the same goal. It has the following properties:
1. Is responsive to the extent to which two corpora differ.
2. Quickly reaches near-optimal vocabulary coverage.
3. Takes into account what has already been selected.
4. Does not involve defining any kind of domain, nor any kind of classifier.
5. Has real units.
6. Knows approximately when to stop.
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Sr Mgr, Science Product
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.
Applied Scientist
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.
Applied Scientist - Personalization
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.
View from space of a connected network around planet Earth representing the Internet of Things.
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