Research spans the five core areas of Alexa functionality, as well as more-general questions in machine learning.

The new focus areas for the Amazon Research Awards, which provide up to $80,000 in funding and up to $20,000 in Amazon Web Services Promotional Credits to academic researchers investigating topics related to machine learning, were announced this month. The application period opens on September 10.

Student system demonstrates how "early exit" strategies could improve Alexa's efficiency, by letting neural networks break off computations when they have high confidence in their solutions.

At this year's Conference on Empirical Methods in Natural Language Processing (EMNLP), Amazon researchers are cohosting what they hope will be the first in a series of annual workshops that will both catalyze and publicize research on automatic fact verification.

Amazon papers at Interspeech 2018 examine ways that interaction histories can improve customers' Alexa experiences, from "collaborative filtering" to infer customers' tastes to methods that use the immediate "context" of a customer's requests to increase the accuracy of speech understanding systems.

3 questions with Björn Hoffmeister, the senior manager for machine learning in the Alexa Automatic Speech Recognition group, about this year's Interspeech conference and research trends in the speech technology field.

Two months after Amazon released a dataset containing assertions of fact and Wikipedia data that either substantiate or refute them, preliminary results of a competition to use the dataset to train fact verification systems indicate a 134% improvement over baseline.

The deadline for the 2018 Amazon Research Awards, a grant program that provides up to $80,000 in funding and $20,000 in Amazon Web Services Promotional Credits to academic researchers investigating topics related to machine learning, is September 15.