AWS Machine Learning Blog

Category: Artificial Intelligence*

Create a Question and Answer Bot with Amazon Lex and Amazon Alexa

by Bob Strahan and John Calhoun | on | in Amazon Lex* | Permalink | Comments |  Share

Your users have questions and you have answers, but you need a better way for your users to ask their questions and get the right answers. They often call your help desk, or post to your support forum, but over time this adds stress and cost to your organization. Could a chat bot add value […]

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AWS Deep Learning AMI Now Includes Apache MXNet 0.11 and TensorFlow 1.3

The AWS Deep Learning Amazon Machine Image (AMI) is designed to help you build stable, secure, and scalable deep learning applications on AWS. The AMI comes pre-installed with popular deep learning frameworks. It has GPU drivers and libraries that let you train sophisticated AI models and scale them in the cloud. The latest release of […]

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Get Started with Deep Learning Using the AWS Deep Learning AMI

Whether you’re new to deep learning or want to build advanced deep learning projects in the cloud, it’s easy to get started by using AWS. The AWS Deep Learning AMIs, available in both Ubuntu and Amazon Linux versions, let you run deep learning applications in the cloud at any scale. The Amazon Machine Images (AMIs) […]

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Enhancements to the Amazon Lex Console Let You Test Your Bot for Better Troubleshooting

Building your chatbot in the Amazon Lex console takes just a few steps, and testing your bot is just as easy. We’ve made enhancements to the Test window of the Amazon Lex console which now provides you more details during testing and enables easier bot troubleshooting. Once you’ve built a bot to test, the Test […]

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Export your Amazon Lex bot schema to the Alexa Skills Kit

You can now export your Amazon Lex chatbot schema into the Alexa Skills Kit to simplify the process of creating an Alexa skill. Amazon Lex now provides the ability to export your Amazon Lex chatbot definition as a JSON file that can be added to the Alexa Skills Kit (ASK). Once you add the bot schema file […]

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Bring Machine Learning to iOS apps using Apache MXNet and Apple Core ML

With the release of Core ML by Apple at WWDC 2017, iOS, macOS, watchOS and tvOS developers can now easily integrate a machine learning model into their app. This enables developers to bring intelligent new features to users with just a few lines of code. Core ML makes machine learning more accessible to mobile developers. […]

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Apple Core ML and Keras Support Now Available for Apache MXNet

We’re excited about the availability of Apache MXNet version 0.11. With this release, MXNet hit major milestones, both in terms of community development and as an incubating Apache project. Contributors—including developers from Apple, Samsung and Microsoft—committed code to this release. There are over 400 contributors on the project so far. The project has now fully […]

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Use Synonyms and Slot Value Validation in your Amazon Lex Chatbots

You can now provide synonyms for slot values in Amazon Lex. With the synonym functionality, you can specify multiple synonyms for a slot value in your chatbot. The synonyms specified are resolved to the corresponding slot values. For example, if the slot value is “comedy”, with “funny” and “humorous” specified as synonyms, then user input […]

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How Amazon Polly Breathed Life into Dan Brown’s Digital Assistant

This is a guest post by Damian Dutton, CEO and Founder of Beeliked. Beeliked is, in their own words, “a digital marketing platform offering a wide range of campaigns to help brands engage with their existing audiences and reach new customers through the viral and social nature of the contests and games.” To support the […]

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AWS CloudTrail Integration is Now Available in Amazon Lex

Amazon Lex is now integrated with AWS CloudTrail, a service that enables you to log, continuously monitor, and retain events related to API calls across your AWS infrastructure, to provide a history of API calls for your account. Amazon Lex API calls are captured from the Amazon Lex console or from your API operations using […]

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