AWS re:Invent 2017

Latest Episodes:

ALX319: It's All in the Data: The Machine Learning Behind Alexa's AI Systems Dec 01, 2017

Show notes

Garbage in, garbage out. The quality of all machine learning solutions depends on the data used in training. Alexa developers are able to use advanced natural language understanding capabilities like built-in slot and intent training, entity resolution, and dialog management. This utterance data behind your skills is the most important contributor to the voice input experience. This session discusses how utterance data is processed by our systems, and what you can do as a developer to improve accuracy.

ALX320: The Science behind the Alexa Prize: Meeting the AI Challenges Dec 01, 2017

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In this session, scientists from the Alexa team explore and discuss some of the AI challenges behind the Alexa Prize. Learn about the challenges of Automatic Speech Recognition (ASR), Natural Language Understanding (NLU) and conversational interaction through stories from the founding members of the team that also built Amazon Echo and Alexa. We'll address the early difficulties of designing the algorithms for noise reduction for close-talk, near field, and far-field Alexa devices, and methods and frameworks they use for ASR, NLU and conversational interaction.

ALX321: Alexa State of the Science Dec 01, 2017

Show notes

Join us for the Golden Age of AI. The way that humans interact with machines is at an inflection point and conversational artificial intelligence (AI) is at the center of the transformation. Learn how Amazon is using machine learning and cloud computing to help fuel innovation in AI, making Alexa smarter every day. Alexa VP and Head Scientist Rohit Prasad presents the state of the science behind Amazon Alexa. He addresses advances in spoken language understanding and machine learning in Alexa, and shares how Amazon thinks about building the next generation of user experiences. He will announce the inaugural winner of the Alexa Prize and award the winning student team a check for $500,000.

ALX322: Natural Language Processing Plus Natural Language Generation: The Cutting Edge of Voice Design Dec 01, 2017

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Your Alexa skill could become the voice of your company to customers. How do you make sure that it conveys rich information, delivered with your brand's personality? In this session, Adam Long, VP of Product Management at Automated Insights, discusses natural language generation (NLG) techniques and how to make your Alexa response more insightful and engaging. Rob McCauley, Solutions Architect with Amazon Alexa, shows you how to put those techniques into action.

ALX324: Alexa State of the Union: Amazon's Vision for Alexa and Voice Dec 01, 2017

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Join Alexa SVP Tom Taylor as we cover the state of the Alexa business, describe some early challenges, and share how we are approaching emerging trends. Voice experiences have transformed the way that customers interact with the world around them. We will introduce new capabilities to help developers better address opportunities in devices, the smart home, and voice. You will leave with an understanding of the vision behind Alexa that ties together the deep dives going on throughout re:Invent.

ALX326: Applying Alexa's Natural Language To Your Challenges Dec 01, 2017

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In this session, we will give you a complete picture of all the tools and techniques required to build complex, production-quality Alexa skills. You will leave this session knowing how to use Alexa's dialog management, entity resolution, and slot elicitation capabilities as well as how to process the results through a microservice with AWS Lambda.

AMF301: Big Data & Analytics for Manufacturing Operations Dec 01, 2017

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Manufacturing companies collect vast troves of process data for tracking purposes. Using this data with advanced analytics can optimize operations, saving time and money. In this session, we explore the latest analytics capabilities to support your goals for optimizing the manufacturing plant floor. Learn how to build dashboards that connect to prediction models driven by sensors across manufacturing processes. Learn how to build a data lake on AWS, using services and techniques such as AWS CloudFormation, Amazon EC2, Amazon S3, AWS Identity and Access Management, and AWS Lambda. We also review a reference architecture that supports data ingestion, event rules, analytics, and the use of machine learning for manufacturing analytics.

AMF302: Alexa, Where's My Car? A Test Drive of the AWS Connected Car Reference Dec 01, 2017

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Today's trends in auto technology are all about connecting cars and their occupants to the outside world in a seamless and safe manner. In this session, we discuss how automotive companies are leveraging AWS for a variety of connected vehicle use cases. You'll leave this session with source code, architecture diagrams, and an understanding of how to apply the AWS Connected Vehicle Reference Architecture to build your own prototypes. You'll also learn how car companies can leverage Amazon services such as Alexa and AWS services such as AWS IOT, AWS Greengrass, AWS Lambda and Amazon API Gateway to rapidly develop and deploy innovative connected vehicle services.

AMF304: Optimizing Design and Engineering Performance in the Cloud for Manufacturing Dec 01, 2017

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Manufacturing companies in all sectors—including automotive, aerospace, semiconductor, and industrial manufacturing—rely on design and engineering software in their product development processes. These computationally-intensive applications—such as computer-aided design and engineering (CAD and CAE), electronic design automation (EDA), other performance-critical applications—require immense scale and orchestration to meet the demands of today's manufacturing requirements. In this session, you learn how to achieve the maximum possible performance and throughput from design and engineering workloads running on Amazon EC2, elastic GPUs, and managed services such as AWS Batch and Amazon AppStream 2.0. We demonstrate specific optimization techniques and share samples on how to accelerate batch and interactive workloads on AWS. We also demonstrate how to extend and migrate on-premises, high performance compute workloads with AWS, and use a combination of On-Demand Instances, Reserved Instances, and Spot Instances to minimize costs.

AMF305: Autonomous Driving Algorithm Development on Amazon AI Dec 01, 2017

Show notes

Over the next decade, accelerating autonomous driving technology—including advances in artificial intelligence, sensors, cameras, radar and data analytics—are set to transform how we commute. In this session, you learn how to use Amazon AI for a highly productive, on demand, and scalable autonomous driving development environment. We compare the most popular AI frameworks including TensorFlow and MXNet for use in autonomous driving workloads. You learn about the AWS optimizations on MXNet that yield near linear scalability for training deep neural networks and convolutional neural networks. We demonstrate the ease of getting started on AWS AI by using a sample training dataset for building an object detection model on AWS. This session is intended for audiences who have some exposure to the underlying concepts for AI-based autonomous driving development. After attending the session, you can get started with AI development on AWS by using a sample dataset for building an object detection model.

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