We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are committed to furthering our culture of inclusion. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, we embrace our differences. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. 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.
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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. More common in the industry is the use of digital signal processing to detect anomalies in the video signal that frequently correlate with defects.
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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. 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. 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. 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. 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. Instead, statisticians usually use regression analysis to approximate it for a single quantile level at a time. In practical cases, however, we rarely have a tidy formula for computing the quantile function. 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. 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.
Its graph can be produced by flipping the cumulative distribution function's graph over. The quantile function is simply the inverse of the cumulative distribution function (if it exists).
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