Alexa Science

Delivering Tomorrow’s Vision for Conversational AI Today

What Is the Role of Science in Alexa?

Science is critical to how Alexa is revolutionizing daily conveniences, from playing music and controlling your smart home, to getting information and much more just by using your voice. Our scientists and engineers develop foundational AI technologies for anyone to build intelligent conversational interfaces for any device, application, language or environment. We build machine learning algorithms, services and data-driven models for key components, such as wake word detection, automatic speech recognition, natural language understanding, contextual reasoning, dialog management, question answering, and text-to-speech, all of which contribute to the magic that is Alexa.

What Is Our Unique Approach to Science?

We believe in hiring and developing world-class talent in science and engineering, and building teams with multi-disciplinary skills with clear charters and goals. These multi-disciplinary teams employ our working backwards method to identify key long-term problems to solve on behalf of our customers and a staged approach to ensuring we make rapid progress towards our goals. The combination of world-class elastic computing resources available via AWS, large-scale heterogeneous data resources, and the team’s years of experience in building and deploying machine learning algorithms is key to innovation at scale.

What Is the Impact?

Our research is focused on delivering magical experiences for our customers through the ground-breaking Echo family of devices and third-party devices available everywhere. As a result, our conversational AI inventions are having a direct impact on the lives of millions of people. We also contribute to the advancement of conversational AI through engagements with the academic community via funded research and Grand Challenges such as the Alexa Prize. Moreover, we encourage the publication of research that will contribute to the future of AI.

Research published recently by Alexa conversational AI scientists includes:

2019

2018

2017 

Alexa at Five: Looking Back, Looking Forward

Nov. 6, 2019 - Alexa AI's chief scientist on the past and future of the voice service.
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Improving Cross-Lingual Transfer Learning by Filtering Training Data

Oct. 28, 2019 - Cross-lingual transfer learning, which uses machine learning models trained in one language to bootstrap models in another, benefits from algorithms that select high-value training data in the source language.
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The FEVER Data Set: What Doesn’t Kill It Will Make It Stronger

Oct. 17, 2019 - The open challenge for the Fact Extraction and Verification (FEVER) workshop at EMNLP involved devising adversarial examples that would stump fact verification systems trained on the FEVER data set.
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Tools for Generating Synthetic Data Helped Bootstrap Alexa’s New-Language Releases

Oct. 11, 2019 - Synthetic-data generators provided initial training data for natural-language-understanding models in Hindi, U.S. Spanish, and Brazilian Portuguese.
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Amazon Releases New Public Data Set to Help Address “Cocktail Party” Problem

Oct. 1, 2019 - Recorded in the lab during simulated dinner parties, a new data set should aid the development of systems for separating speech signals in reverberant rooms with multiple speakers.
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How to Construct the Optimal Neural Architecture for Your Machine Learning Task

Sep. 23, 2019 - Theoretical analysis shows how to efficiently search a large space of possible neural architectures, to identify the one that offers optimal performance.
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Amazon Releases Data Set of Annotated Conversations to Aid Development of Socialbots

Sep. 17, 2019 - Data set includes more than 230,000 dialogue turns, most of which are annotated to indicate the sources of their factual assertions.
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Turning Dialogue Tracking into a Reading Comprehension Problem

Sep. 16, 2019 - Treating a conversation as a text, and dialogue state tracking as answering questions about the text, enables an 11.75% improvement in accuracy over the best-performing prior system.
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The 16 Alexa-Related Papers at This Year’s Interspeech

Sep. 10, 2019 - Research spans the five core areas of Alexa functionality, as well as more-general questions in machine learning.
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Accelerating Parallel Training of Neural Nets

Sep. 5, 2019 - By combining two state-of-the-art techniques for parallelizing machine learning — one that prioritizes accuracy, one that prioritizes efficiency — Alexa researchers improve on both.
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Why Work for Alexa Science?

It’s about the opportunity to have impact at scale. There are many roles to explore across science, engineering and data-driven modeling that span every facet of machine learning. Below are examples of peers who are delivering tomorrow’s conversational AI experiences today. You also can review our global job opportunities across the many teams that deliver Alexa experiences, or check out the job opportunities in each city listed below.

Where We Do Alexa Science

Alexa Science in the News

The Year Alexa Grew Up

WIRED | December 19, 2018