AWS AI Blog

Listen to this post Voiced by Polly

Just as Dan Brown has captivated millions of readers through countless plot twists and turns, the launch of his new novel, Origin, will lead you along an inspired journey that guarantees to speak to you and draw you in. Literally.

The 2003 best-selling author of The Da Vinci Code invites you to participate in selecting the book cover design for a limited edition of his novel, to be released on October 3^rd 2017. In return for casting your vote, you will receive a personalized video in which you will be greeted by name, and witness Dan Brown signing a copy of his new book, just for you.

The magic behind the #DanBrownOrigin experience is produced by BeeLiked, a self-service social pollination platform that is rewriting the script for launching engaging marketing campaigns. The secret behind the voice that greets each fan by name, in their very own personalized video, is Amazon Polly. To get further behind the scenes, and to learn more about how BeeLiked pulled off this amazing opportunity for millions of loyal fans to connect with Dan Brown, read the full blog post about the #DanBrownOrigin campaign.

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As AWS continues to support the Artificial Intelligence (AI) community with contributions to Apache MXNet and the release of Amazon Lex, Amazon Polly, and Amazon Rekognition managed services, we are also expanding our team of AI experts, who have one primary mission: To lower the barrier to AI for all AWS developers, making AI more accessible and easy to use. As Swami Sivasubramanian, VP of Machine Learning at AWS, succinctly stated, “We want to democratize AI.”

In our Research Spotlight series, I spend some time with these AI team members for in-depth conversations about their experiences and get a peek into what they’re working on at AWS.

Mu Li is a principal scientist for machine learning at AWS. Before joining AWS, he was the CTO of Marianas Labs, an AI start-up. He also served as a principal research architect at the Institute of Deep Learning at Baidu. He obtained his PhD in computer science from Carnegie Mellon University, where one of his advisors was Alex Smola, now Director of Machine Learning at AWS. Mu’s research has focused on large-scale machine learning. In particular, he is interested in the co-design of distributed systems and machine learning algorithms. He has been the first-author for computer science conference and journal papers on subjects that span theory (FOCS), machine learning (NIPS, ICML), applications (CVPR, KDD), and operating systems (OSDI).

At AWS, Mu leads a team that works primarily on the Apache MXNet framework. Their focus is making it easier to use deep learning and to run deep learning applications on AWS. To accomplish this, Mu and his team are charting new territory in deep learning research, investigating and simplifying new algorithms that can run on large-scale datasets in distributed systems. “The speed of machine learning training depends on two things: how fast you can process images and how fast you can process the final model,” says Mu. The framework should support using multiple GPUs and multiple machines. “The latter is related to optimization–what we call the convergence rate. When we move from a single machine to multiple machines, we need to develop new distributed training algorithms. We need to change the algorithm itself–to change the neural network structure–so that it can be easily used to train very large datasets on a large number of machines.”

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For our customers in Europe, Julien Simon, Principal Technical Evangelist at Amazon Web Services, is leading workshops and speaking at several events throughout the month. Julien is a frequent speaker at workshops and conferences taking participants on a journey through Deep Learning with AWS, Amazon Lex, Amazon Polly, Amazon Rekognition, Apache MXNet and more. If you’re attending any of these events, please join us for a great conversation.

More information:

May 20, 2017 – AI on a Pi – Dev It, Thessaloniki (Greece) 

May 23, 2017 – Amazon AI – AWS Transformation Day, Utrecht (Netherlands)

May 30, 2017 – Amazon AI – Sharks in IT, Sofia (Bulgaria)

May 30, 2017 – Deep Learning with Apache MXNet –  AWS User Group Sofia (Bulgaria) 

Is your Amazon Lex chatbot ready to talk to the world? When it is, chances are that you’ll want it to be able to interact with as many users as possible. Amazon Lex offers built-in integration with Facebook, Slack and Twilio. But what if you want to connect to a messaging service that isn’t supported? Well, there’s an API for that–the Amazon Lex API. In this post, I show how to integrate an Amazon Lex bot with an external messaging service by using Twilio Programmable SMS as the example service.

You can integrate any messaging service that provides the right APIs with Amazon Lex using the design pattern described in this post. The solution includes a serverless middle tier or a preprocessing layer “in front of” Amazon Lex. This is useful if you want to incorporate Amazon Lex as another building block into your systems. For example, if you’re in an enterprise, you could use this solution to implement custom message routing to specialized bots developed by different business units.

For simpler uses cases, the built-in integration for Twilio in the Amazon Lex console might be a better option.

Architecture and message flow

For this integration, I chose a serverless architecture that uses Amazon API Gateway and AWS Lambda to robustly and scalably integrate the Amazon Lex bot with the Twilio messaging service. Going serverless means that you don’t have to worry about managing individual instances, and that you incur costs only for the resources that your application uses. API Gateway provides the secure API endpoint for a Lambda function that implements your business logic.

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As AWS continues to support the Artificial Intelligence (AI) community with contributions to Apache MXNet and the release Amazon Lex, Amazon Polly, and Amazon Rekognition managed services, we are also expanding our team of AI experts, who have one primary mission: To lower the barrier to AI for all AWS developers, making AI more accessible and easy to use. As Swami Sivasubramanian, VP of Machine Learning at AWS, succinctly stated, “We want to democratize AI.”

In our Research Spotlight series, I spend some time with these AI team members for in-depth conversations about their experiences and get a peek into what they’re working on at AWS.

Anima Anandkumar joined AWS in November 2016, as Principal Scientist on Deep Learning. She is currently on leave from the EECS Department at UC Irvine, where she has been an associate professor since August 2010. Anima has earned several prestigious awards, including the Alfred P. Sloan Research Fellowship, the NSF CAREER award, and Young Investigator Research awards from the Army Research Office and the Air Force Office for Sponsored Research. Her research interests include large-scale machine learning, non-convex optimization, and high-dimensional statistics. In particular, she’s been spearheading the development and analysis of tensor algorithms.

“My mission is to make machine learning accessible to everyone on the planet, and AWS is an awesome place to achieve that.” She went on to explain that she wants to remove the guesswork for launching large-scale machine learning jobs, so that you don’t have to be an expert in machine learning, application domains, or programming, especially because it’s humanly impossible for one person to have all these skill sets. As Anima notes, there is a huge gap between formulating theories and going into production with a machine learning workload. Her goal is to shrink the gap from prototyping to deployment.

One of the tools that Anima plans to work with is Apache MXNet. She wants to add a lot more functionality to exploit Apache MXNet’s programmability and ease of use. “Our roadmap includes operations that surpass the existing deep learning framework. We want to develop multi-modal processing algorithms.” Multi-modal processing allows an algorithm to simultaneously process text, images, and other modalities at scale.

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Listen to this post Voiced by Polly

This is a guest post by André Kenji Horie, a software engineer on Duolingo’s Learning Team. In their own words, “Duolingo is the most popular language-learning platform and the most downloaded education app in the world, with more than 170 million users.”

When teaching a foreign language, accurate pronunciation matters. If exposed to incorrect pronunciation, learners develop their listening and speaking skills poorly, which compromises their ability to communicate effectively. Duolingo uses text-to-speech (TTS) to provide high-quality language education. To some, this approach might seem counterintuitive: shouldn’t people learn by listening to a native speaker?

In this post, we outline why we chose to use TTS instead of human recordings. We also describe our quantitative and qualitative framework for choosing voices to ensure high-quality material for our users. Using this framework, we show that Amazon Polly has provided a superior experience. Finally, we provide an overview of the infrastructure we built for reliably serving audio to millions of language learners every day.

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As AWS continues to support the Artificial Intelligence (AI) community with contributions to Apache MXNet and the release of Amazon Lex, Amazon Polly, and Amazon Rekognition managed services, we are also expanding our team of AI experts, who have one primary mission: To lower the barrier to AI for all AWS developers, making AI more accessible and easy to use. As Swami Sivasubramanian, VP of Machine Learning at AWS, succinctly stated, “We want to democratize AI.”

In our Research Spotlight series, I spend some time with these AI team members for in-depth conversations about their experiences and get a peek into what they’re working on at AWS.

This week, I sat down with Alex Smola. Alex joined AWS in August 2016 as the Director of Machine Learning and Deep Learning. Alex is recognized as one of the world’s top machine learning experts. He comes to AWS after helping grow the widely respected machine learning department at Carnegie Mellon, where he still holds a professorship. Alex is a prolific and widely cited author within the academic research community, writing or contributing to 462 titles with 75,000+ citations.

I asked Alex about the opportunities he sees when he looks at the current state of AI. Without hesitating, he said, “Lowering the barrier of entry to anyone who wants to do machine learning is one of the biggest opportunities out there. One of my main goals is to make machine learning accessible to a million+ developers. You can accomplish this either by training a million developers or by making your tools easier to use.”

For Alex, this means finding the right tools for different levels of customers. He cited Amazon Rekognition, a managed service that addresses computer vision challenges, as an example of this. He continued his thoughts about increasing accessibility: “Lowering the barrier to machine learning includes building tools for data scientists to enable easier experimentation, and providing documentation that is easy for developers to understand. It’s critical we make it easy to bring the power of high-performance computing to developers who want to use the latest deep learning tools.”

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This year at NVIDIA’s GPU Technology Conference, AWS and NVIDIA partnered on multiple initiatives. The first is an exciting new Volta-based GPU instance that we think will completely change the face of the AI developer world through a 3x speedup on LSTM training. Second, we are announcing plans to train 100,000+ developers through the Deep Learning Institute (DLI) running on AWS. The third is the joint development of tools that enable large-scale deep learning for the broader developer community.

AWS is also delivering sessions at GTC including using Apache MXNet training at scale on Amazon EC2 P2 instances and at the edge through the support of NVIDIA’s Jetson TX2 platform. Here’s to a great partnership and some amazing innovation!

Volta—coming to an instance near you

The Tesla V100, based on the Volta architecture and equipped with 640 Tensor Cores, provides breakthrough performance of 120 teraflops of mixed precision deep learning performance. AWS is very excited to support V100 on Amazon EC2 instances. This support means that the growing deep learning community can take advantage of supercomputing class capabilities and train even deeper models, pushing the limits of AI. Also, in collaboration with NVIDIA, AWS engineers and researchers have pre-optimized neural machine translation (NMT) algorithms on Apache MXNet. This approach allows developers to train the fastest way now possible on Volta-based platforms. Overall, we expect the Volta-based instance to be very popular with developers!

Bringing deep learning to 100,000+ developers worldwide

We are excited to partner with NVIDIA to deliver coursework for their Deep Learning Institute on top of AWS. The DLI is broadening its curriculum to include the applied use of deep learning for self-driving cars, healthcare, web services, robotics, video analytics, and financial services. This curriculum includes instructor-led seminars, workshops, and classes to reach developers across Asia, Europe, and the Americas. With AWS’s global infrastructure spanning 42 Availability Zones (with 8 more planned) and 16 regions (with 3 more coming), AWS is the perfect infrastructure platform to reach a broad set of developers.

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Another month of AI solutions on the AWS AI Blog. Take a look at the summaries below and learn, comment and share. Thank you for reading!

NEW POSTS

AI Tech Talk: An Overview of AI on the AWS Platform
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. Whether you’re just getting started with AI or you’re a deep learning expert, this session provides a meaningful overview of the managed AI services, the AI Platform offerings, and the AI Frameworks you can run on the AWS Cloud. 

Deep Learning AMI for Ubuntu v1.3_Apr2017 Now Supports Caffe2
In this post, we announce that the AWS Deep Learning AMI for Ubuntu now supports the newly launched Caffe2 project led by Facebook. AWS is the best and most open place for developers to run deep learning, and the addition of Caffe2 adds yet another choice.  

Running BigDL, Deep Learning for Apache Spark, on AWS
BigDL is a distributed deep learning framework for Apache Spark that was developed by Intel and contributed to the open source community for the purposes of uniting big data processing and deep learning. In this post, learn how you can use familiar tools, such as Spark, and BigDL v0.1.0, to easily build deep learning applications in a distributed fashion on AWS.

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Join Julien Simon, Principal Technical Evangelist at Amazon Web Services, on May 9 for an evening of AI and Deep Learning hosted by the AWS User Group Dublin. The event will feature Amazon Lex, Amazon Polly, and Amazon Rekognition. Julien will take participants on a journey through Deep Learning with AWS covering AI theory to the latest offerings from AWS.  Additional speakers will dive deep on Amazon Lex and the new Alexa Skills Kit. If you’re in Dublin on May 9, we hope that you can join us!

For more information, see the AWS User Group Dublin’s Meetup invitation.