Uploaded byAdrian Hornsby
Innovations and the Cloud
The document discusses innovations in artificial intelligence and cloud computing. It describes how AWS services like Amazon Rekognition, Amazon Polly, and AWS Deep Learning AMIs can be used to build applications involving computer vision, natural language processing, and deep learning. Examples are provided of using these services for image recognition, text-to-speech, and deploying pre-trained machine learning models for inference at scale. The document advocates that these AWS AI services allow for the democratization of AI and deep learning.
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Innovations and the Cloud
- 1. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Adrian Hornsby, Technical Evangelist @adhorn Innovations and the cloud
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- 3. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. 4 September 1882 The Pearl Street Generating Station
- 4. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. 4 September 1882 The Pearl Street Generating Station
- 5. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. The lightbulb was a product of networked innovations, all linked together to create the magic of electric light.
- 6. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. How would you reinvent the lightbulb today?
- 7. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. http://bit.ly/adhornlightbulb Cloud Powered Lightbulb
- 8. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. AWS IoT IoT shadow Amazon Cognito MQTT over WebSockets AWS LambdaAlexa Amazon S3
- 9. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. What is the real power of the cloud?
- 10. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Abstract complex problems into easy to use services.
- 11. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. 90+ of them …
- 12. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Artificial Intelligence
- 13. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. The Advent of AI & Deep Learning Algorithms
- 14. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. The Advent of AI & Deep Learning Data Algorithms
- 15. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. The Advent of AI & Deep Learning Data GPUs & Acceleration Algorithms
- 16. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. The Advent of AI & Deep Learning Data GPUs & Acceleration Cloud Computing Algorithms
- 17. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. The Advent of AI & Deep Learning Data GPUs & Acceleration Cloud Computing Algorithms AWS
- 18. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.
- 19. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.
- 20. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved.
- 21. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Amazon AI
- 22. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. AI and Deep Learning In The Hands Of Every Developer
- 23. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Amazon Rekognition Object and scene detection Facial analysis Face comparison Celebrity recognition Image moderation Text in Pictures Image Analysis with Amazon Rekognition
- 24. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Object & Scene Detection
- 25. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Facial Analysis
- 26. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Facial Search
- 27. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Text in Picture
- 28. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. <API> Amazon Rekognition </API> aws rekognition detect-faces --image '{"S3Object":{"Bucket":"adhorn-reko","Name":"horse.jpg"}}' --attributes "ALL" aws rekognition detect-labels --image '{"S3Object":{"Bucket":"adhorn-reko","Name":"horse.jpg"}}'
- 30. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. <demo> Image Recognition and Processing Backend Step Functions </demo> https://github.com/awslabs/lambda-refarch-imagerecognition
- 31. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Text-to-Speech with Amazon Polly Amazon Polly “Today in Seattle, WA it’s 11°F” “Today in Seattle Washington it’s 11 degrees Fahrenheit”
- 32. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. https://www.w3.org/TR/speech-synthesis/ <speak> The spelling of my name is <prosody rate='x-slow'> <say-as interpret-as="characters">Adrian</say-as> </prosody> </speak> A Focus On Voice Quality & Pronunciation
- 33. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. <API> Amazon Polly </API> aws polly synthesize-speech --text "It was nice to live such a wonderful live show" --output-format mp3 --voice-id Joanna --text-type text johanna.mp3
- 34. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. “With Amazon Polly our users benefit from the most lifelike Text-to-Speech voices available on the market.” Severin Hacker CTO, Duolingo
- 35. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. One-Click Deep Learning AWS Deep Learning AMIs Amazon Linux & Ubuntu Up to~40k CUDA cores Apache MXNet TensorFlow Theano Keras Caffe CNTK Torch Pre-configured CUDA drivers Anaconda, Python3 Out-of-the-box Tutorials + CloudFormation template + Container Image Available in the AWS Marketplace AI Frameworks on AWS
- 36. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Autonomous Driving Systems
- 37. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Pre-trained MXNet models http://data.mxnet.io/models/imagenet/
- 38. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Scale Predictions with AWS Lambda and MXNet AWS LambdaAmazon API Gateway Amazon S3 Training Inference https://aws.amazon.com/blogs/compute/seamlessly-scale-predictions-with-aws-lambda-and-mxnet/
- 39. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Predict with AWS Lambda
- 40. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Predict with AWS Lambda
- 41.
- 42. The Future isnow.
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- 44. Go Build A SmarterFuture. @adhorn
Editor's Notes
- #3 In fact, it is worth noting that Thomas Edison did not invent the incandescent light bulb. Twenty three different light bulbs were developed before Edison's. Though he didn’t come up with the whole concept, his light bulb was the first that proved practical and affordable for home illumination. The trick had been choosing a filament that would be durable but inexpensive, and they tested more than 6,000 possible materials before finding one that fit the bill: carbonized bamboo.
- #4 But the strike of Genius of Edison wasn’t the lightbulb itself using carbonized bamboo, but instead it came with the lighting of the Pearl Street 2 years later.
- #5 Building on the work of other inventors in some cases and drawing on his own vision and ingenuity, Edison and his associates had to invent a long lasting lightbulbs yes, but they also had to have a reliable source of electric current, a system for distributing that current around the street, a mechanism for connecting lightbulbs to the grid and a meter to measure how much electricity each household was using. The lightbulb in itself was more of a curiosity piece rather than a life changing technology.
- #6 In fact, the lightbulb was a product of networked innovations, all linked together to create the magic of electric light.
- #9 To build that, no genius was involved .. but like Edison, we stitched innovations together to create a Wow experience for our customers. (security, scalable, reliable..) And this is exactly what the cloud is about! Just few years ago, it would have taken months or even years to build a scalable, reliable and secure application like that from scratch.
- #11 Catalyst for innovations to flourish, because you can easy abstract complex problems into easy to use, secure and scalable units, that you can use to easily build complex applications
- #12 From storage, compute, databases, analytics tools and datawarehouses to Mobile applications, Devops tools, Containers, Serverless, IoT and AI just to name a few.
- #13 One of the most fascinating complex problem but also one that has the potential to shape the future of technology is AI… When did it all start? Well, that was approx 60 years ago!
- #14 By the 2000s, 4 things made large-scale neural networks possible. Algorithms: 1998 Convolutional Neural Networks, a new type of multi-layered networks ( “Deep Learning”) - dramatically reduces the computing cost of network training.
- #15 Large data sets became widely available. Text, pictures, movies, music: everything was suddenly digital and could be used to train neural networks.
- #16 Graphics Processing Units (GPUs) parallel processing power to train large neural networks.
- #17 Cloud computing brought elasticity and scalability to developers and researchers, allowing them to use as much infrastructure as needed for training… without having to build, run or pay for it long term.
- #18 AWS is an AI enabler .. For all the reason mentioned here –
- #19 At Amazon, we’ve been investing deeply in artificial intelligence for over 20 years, and many of the capabilities customers experience are driven by machine learning. We have thousands of engineers at Amazon committed to machine learning and deep learning, and it’s a big part of our heritage.
- #20 Amazon Robotics was founded in 2003 on the notion that in order to meet consumer demands in eCommerce, a better approach to order fulfillment solutions was necessary. Amazon Robotics empowers a smarter, faster, more consistent customer experience through automation automates fulfilment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands.
- #21 Amazon Prime Air is a service that will deliver packages up to 2.5 kg in 30 minutes or less using small drones and relies extensively on visual object recognition. We have Prime Air development centers in the United States, the United Kingdom, Austria, France and Israel.
- #22 So the 20 years and thousands of engineers we’ve had working on Amazon have helped drive operation efficiencies and better experiences for our customers.. And that experience and the tools we have developed, we offer them for you to build and innovate with.
- #24 Amazon Rekognition currently supports the JPEG and PNG image formats. You can submit images either as an S3 object or as a byte array. Amazon Rekognition supports image file sizes up to 15MB when passed as an S3 object, and up to 5MB when submitted as an image byte array. Amazon Rekognition is currently available in US East (Northern Virginia), US West (Oregon) and EU (Ireland) regions. Mxnet convolutional deep neural networks (CNNs),
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- #32 Polly’s basics are pretty simple, but the service has deep functionality. You can send the service a simple string of text, and it will generate the life like voice in your choice of 47 different voices. But it’s not naive of the context of the text. For example, the text here - ‘WA’ and ‘degree F’, that would sound strange if it were spoken out loud. Instead, Polly will automatically expand the text strings ‘WA’ and ‘degree F’, to ‘Washington’ and ‘degrees fahrenheit’, to create more life like speech. The developer doesn’t have to do anything - just send the text, and get life like voice back.
- #33 Polly also support Speech Synthesis Markup Language (SSML) Version 1.0 The Voice Browser Working Group has sought to develop standards to enable access to the Web using spoken interaction.
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- #35 Spoken language crucial for language learning Accurate pronunciation matters Faster iteration thanks to TTS As good as natural human speech 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? Find a company that records audio in the language: The company must find a voice actor who not only speaks the language, but also who speaks with good pronunciation and clarity. Find someone to evaluate the quality of pronunciation: We need an independent party from the recording company to create a small sample of sentences, which this party uses to evaluate pronunciation quality of the recordings. Record and evaluate the quality of the sample sentences. Set up a contract with the recording company. Record all sentences. Evaluate recordings, providing a data quality assurance check. For example, we need to check if all files are in the proper format and correctly separated. This step is necessary because the industry standard is to record all sentences in a single session and separate them later.
- #36 On click – Marketplace AMI supported and maintained by Amazon Web Services for use on EC2. Designed to provide a stable, secure, and high performance execution environment for deep learning applications running on Amazon EC2. Popular deep learning frameworks, including MXNet, Caffe, Tensorflow, Theano, CNTK and Torch as Packages that enable easy integration with AWS, including launch configuration tools and many popular AWS libraries and tools. It also includes the Anaconda Data Science Platform for Python2 and Python3. “ The AWS Deep Learning AMIs equip data scientists, machine learning practitioners, and research scientists with the infrastructure and tools to accelerate work in deep learning, in the cloud, at any scale. You can quickly launch Amazon EC2 instances on Amazon Linux or Ubuntu, pre-installed with popular deep learning frameworks to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques. The Deep Learning AMIs let you create managed, auto-scaling clusters of GPUs for large scale training, or run inference on trained models with compute-optimized or general purpose CPU instances, using Apache MXNet, TensorFlow, the Microsoft Cognitive Toolkit (CNTK), Caffe, Caffe2, Theano, Torch and Keras. “
- #37 Call out mxnet and AI at the edge.
- #39 We compiled and built the MXNet libraries to demonstrate how Lambda scales the prediction pipeline to provide this ease and flexibility for machine learning or deep learning model prediction. We built a sample application that predicts image labels using an 18-layer deep residual network. The model architecture is based on the winning model in the ImageNet competition called ResidualNet. The application produces state-of-the-art results for problems like image classification.
- #42 I often get asked about the future..
- #43 Democratization of technology – When we started AWS – our goal was to put technology that fortune 500 companies have in the hands of every developers
- #44 Remember the lightbulb was a product of networked innovations, all linked together to create the magic of electric light. Well, When we started AWS – our goal was to put technology that fortune 500 companies have in the hands of every developers – today you have access to technology that was still science fiction few years ago – technology that you can stitch together to create that woo experience for your customers - and we are not going to stop here.
- #45 The truth is, the future is always in the making. It’s engineers and wild thinkers that are helping to create the world of tomorrow. It’s customers like tusimple, revolutionizing the commerical trucking industry, arterys saving lives through 4d medical imaging, duolingo teaching language around the world.. It’s customers like you that are helping to build this. Together, we can tackle some of the worlds most challenging problems, and together we can continue to build a smater future.