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BDA310 An Introduction to the AI services at AWS

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Artificial Intelligence (AI) services on the AWS cloud bring deep learning (DL) technologies like natural language understanding (NLU), automatic speech recognition (ASR), image recognition and computer vision (CV), text-to-speech (TTS), and machine learning (ML) within reach of every developer. In this session, you will be introduced to several new AI services: Amazon Lex, to build sophisticated text and voice chatbots; Amazon Rekognition, for deep learning-based image recognition; and Amazon Polly, for turning text into lifelike speech. The opportunities to apply one or more of these DL services are nearly boundless and this session will provide a number of examples and use cases to help you get started.

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BDA310 An Introduction to the AI services at AWS

  1. 1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Keith Steward, Ph.D. Specialist Solution Architect, AWS July 26, 2017 An Introduction to the AI Services at AWS
  2. 2. Artificial Intelligence at Amazon
  3. 3. An Introduction to the AI Services at AWS Apache Apache MXNet Deep learning framework
  4. 4. An Introduction to the AI Services at AWS Apache Amazon Polly Text-to-Speech Apache MXNet Deep learning framework
  5. 5. An Introduction to the AI Services at AWS Apache Amazon Polly Text-to-Speech Amazon Rekognition Computer Vision Apache MXNet Deep learning framework
  6. 6. An Introduction to the AI Services at AWS Apache Amazon Polly Text-to-Speech Amazon Rekognition Amazon Lex Computer Vision ASR & NLU Apache MXNet Deep learning framework
  7. 7. An Introduction to the AI Services at AWS Apache MXNet Apache Deep learning framework
  8. 8. Apache MXNet Programmable Portable High Performance Near linear scaling across hundreds of GPUs Highly efficient models for mobile and IoT Simple syntax, multiple languages
  9. 9. Why Apache MXNet? Most Open Best On AWS Optimized for deep learning on AWS Accepted into the Apache Incubator (Integration with AWS)
  10. 10. Apache MXNet is the deep learning framework of choice for AWS
  11. 11. P2 INSTANCES DL CLOUD FORMATION TEMPLATE DL AMIS
  12. 12. An Introduction to the AI Services at AWS Amazon Polly Text-to-Speech Apache
  13. 13. Amazon Polly: Life-like Text-to-Speech Service Converts text to life-like speech 47 voices 24 languages Low latency, real time Fully managed
  14. 14. Let’s take a listen…
  15. 15. “Today in Seattle, WA, it’s 11°F” ‘"We live for the music" live from the Madison Square Garden.’ 1. Automatic, Accurate Text Processing Amazon Polly: A Focus On Voice Quality & Pronunciation
  16. 16. 2. Intelligible and Easy to Understand 1. Automatic, Accurate Text Processing Amazon Polly: A Focus On Voice Quality & Pronunciation
  17. 17. 2. Intelligible and Easy to Understand 3. Add Semantic Meaning to Text “Richard’s number is 2122341237“ “Richard’s number is 2122341237“ Telephone Number Amazon Polly: A Focus On Voice Quality & Pronunciation 1. Automatic, Accurate Text Processing
  18. 18. 2. Intelligible and Easy to Understand 3. Add Semantic Meaning to Text 4. Customized Pronunciation “My daughter’s name is Kaja.” “My daughter’s name is Kaja.” 1. Automatic, Accurate Text Processing Amazon Polly: A Focus On Voice Quality & Pronunciation
  19. 19. Amazon Polly: Common Use Cases • Internet of Things (smart home, connected devices) • Education (language learning, training videos) • Voiced Media (news, blogs, email) • Voiced Chat Bots (Amazon Lex, Alexa skills) • Gaming (avatars, Amazon Lumberyard) #VoiceFirst Movement
  20. 20. An Introduction to the AI Services at AWS Amazon Rekognition Computer Vision Apache
  21. 21. Amazon Rekognition: Computer Vision Service Object and Scene Detection Facial Analysis Facial Comparison Facial Recognition
  22. 22. Amazon Rekognition: Computer Vision Service State-of-the-art face recognition (bounding box and key features).
  23. 23. Face Attribute Extraction (emotion, gender, race, age, etc.) Emotion: confused: 4%, calm: 73% Sunglasses: false (value: 0) Gender: female (value: 0) Mouth open wide: 0% (value: 0) Eye closed: open (value: 0) Glasses: no glass (value: 0) Mustache: false (value: 0) Beard: (value: 0) Amazon Rekognition: Computer Vision Service demo
  24. 24. Amazon Rekognition: Object & Scene Detection
  25. 25. Amazon Rekognition: Facial Search Facial verification Face Search Visual Similarity Search (compare two faces) (compare many faces) (find similar faces)
  26. 26. Amazon Rekognition: A few use cases Best photo: use the attributes smile and eyesOpen to determine the best photos to post Demographic detection: collect the age and gender of customers in your store Sentiment capture: detect the emotions of your customers as they try your product A/B tuning: identify visually similar alternatives to high-scoring images for A/B testing Smart filtering: identify images with high visual similarity to ensure only one is displayed Verify face: compare two faces, receive a confidence score that they are the same person Protected images: identify visually similar images that are protected by trademarks
  27. 27. An Introduction to the AI Services at AWS Amazon Lex ASR & NLU Apache
  28. 28. The Advent of Conversational Interactions 1st gen: Machine-oriented interactions
  29. 29. The Advent of Conversational Interactions 1st gen: Machine-oriented interactions 2nd gen: Control-oriented & translated
  30. 30. The Advent of Conversational Interactions 1st gen: Machine-oriented interactions 2nd gen: Control-oriented & translated 3rd gen: Intent-oriented
  31. 31. Amazon Lex ... for Conversational Interactions Powered by the same deep learning technology as Alexa Enterprise SaaS Connectors Deployment to chat platforms, like Slack, Facebook Messenger, Twilio SMS Build Voice and Text Chatbots Interactions on mobile, web, and devices
  32. 32. Informational Bot: Example
  33. 33. Amazon Lex Use Cases Informational Bots Chatbots for everyday consumer requests Application Bots Build powerful interfaces to mobile applications • News updates • Weather information • Game scores …. • Book tickets • Order food • Manage bank accounts …. Enterprise Productivity Bots Streamline enterprise work activities and improve efficiencies • Check sales numbers • Marketing performance • Inventory status …. Internet of Things (IoT) Bots Enable conversational interfaces for device interactions • Wearables • Appliances • Auto ….
  34. 34. AI Solutions for Every Developer
  35. 35. https://aws.amazon.com/amazon-ai/ Amazon AI: Getting Started
  36. 36. Thank you! aws.amazon.com/amazon-ai
  37. 37. • 10+ year partnership • Joint development • Shared customer passion • High performance + low costs • World class supply chain CLOUD & DATA CENTER THINGS & DEVICES AWS IOT Alexa Voice Services Amazon EC2 Amazon S3 Amazon & Intel
  38. 38. Amazon & Intel
  39. 39. 40@IntelAI Hardware for DL Workloads  Up to 2X better peak performance on compute-intensive analytics  100x improvement in inference performance on EC2 C5 instance*  NEW C5 more computational power, lower costs – customers do more with less Blazingly Fast Data Access  New microarchitecture, hardware acceleration, Intel® AVX-512  50% more memory than previous generation  Novartis conducted 39 years of computational chemistry in 9 hours* High Speed Scalability  Up to 1.73x faster completion of massively parallel research simulations than the previous generation  Seamless data transfer via interconnects Training AI: Intel® xeon® scalable processor Best-in-Class Deep Learning Training Performance Accelerator for training compute density in deep learning centric environments +
  40. 40. 41@IntelAI Inference in the cloud: amazon & Intel® Math Kernel Library for Deep Neural Networks For developers of deep learning frameworks featuring optimized performance on Intel hardware 6.1 2.4 1.2 0.8 679.4 262.5 79.7 73.9 0 200 400 600 800 AlexNet GoogLeNet v1 ResNet-50 Inception v3 Images/Sec c4.8xlarge MXNet Inference No MKL MKL  Up to 2X better peak performance on compute-intensive analytics  100x improvement in inference performance on EC2 C5 instance*  Intel-optimized Caffe, Intel® MKL for high performance distributed training and inference  CloudFormation template with AWS services and EC2, CfnCluster, DynamoDB, EBS and Spot Instance support  Classify text, train a Convolutional neural network, visualize the training using Tensorboard using BigDL on AWS
  41. 41. Intel Confidential INTEL® IOT GATEWAY REAL TIME ANALYTICSAWS IOT PLATFORM Amazon EC2 X1 Inference at the edge: AWS & Intel® cost savings with scalability End-to-end interoperability to scale applications and services streamlined manageability and analytics Seamless data management and analytics from thing to network to cloud multilayered, end-to-end security A chain of trust rooted in the hardware and linked throughout the software
  42. 42. 43@IntelAI Libraries, frameworks & tools Intel® Math Kernel Library Intel® MLSL Intel® Data Analytics Acceleration Library (DAAL) Intel® Distributio n Open Source Frameworks Intel Deep Learning SDK Intel® Computer Vision SDKIntel® MKL MKL-DNN High Level Overview Computation primitives; high performance math primitives granting low level of control Computation primitives; free open source DNN functions for high- velocity integration with deep learning frameworks Communication primitives; building blocks to scale deep learning framework performance over a cluster Broad data analytics acceleration object oriented library supporting distributed ML at the algorithm level Most popular and fastest growing language for machine learning Toolkits driven by academia and industry for training machine learning algorithms Accelerate deep learning model design, training and deployment Toolkit to develop & deploying vision- oriented solutions that harness the full performance of Intel CPUs and SOC accelerators Primary Audience Consumed by developers of higher level libraries and Applications Consumed by developers of the next generation of deep learning frameworks Deep learning framework developers and optimizers Wider Data Analytics and ML audience, Algorithm level development for all stages of data analytics Application Developers and Data Scientists Machine Learning App Developers, Researchers and Data Scientists. Application Developers and Data Scientists Developers who create vision-oriented solutions Example Usage Framework developers call matrix multiplication, convolution functions New framework with functions developers call for max CPU performance Framework developer calls functions to distribute Caffe training compute across an Intel® Xeon Phi™ cluster Call distributed alternating least squares algorithm for a recommendation system Call scikit-learn k-means function for credit card fraud detection Script and train a convolution neural network for image recognition Deep Learning training and model creation, with optimization for deployment on constrained end device Use deep learning to do pedestrian detection … Find out more at software.intel.com/ai
  43. 43. Q & A
  44. 44. Don’t Forget Your Evaluations!

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