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An Overview of the AI on the AWS Platform

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AWS 提供一系列的智能服務,包括雲原生的機器學習和深度學習技術,以滿足您不同的用例和需求。 對於希望將人工智慧 (AI) 服務加到其應用程式的開發人員,AWS 的 Amazon Lex 能夠進行自然語言處理 Natural Language Understanding (NLU) 和自動語音辨識 Automatic Speech Recognition(ASR)、Amazon Rekognition 能夠進行視像搜索和圖像識別,而 Amazon Polly 則備有文字轉語音 (TTS) 的功能。我們的 Amazon Machine Learning 是專為開發人員而設的機器學習配套。

對於更深入的深度學習應用程式,AWS Deep Learning AMI 可讓您在任何規模的雲端運行深度學習。 啟動預先安裝了開放源碼深度學習引擎(Apache MXNet,TensorFlow,Caffe,Theano,Torch和Keras)的 AMI 實例,以訓練自定義、更複雜的 AI 模型、對新的算法進行實驗,並學習新的深度學習技能 – 這些都由基於 GPU 的實例的自動縮放集群支援。

無論您是剛開始使用 AI 還是深度學習的專家,這場線上研討會將提供有關如何透過 AWS 提高規模和效率的概述。

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An Overview of the AI on the AWS Platform

  1. 1. ©2015, Amazon Web Services, Inc. Yian Han Senior Business Development Manager, AWS 2017 09 14 Amazon Web Services AI
  2. 2. What to Expect from the Session Why AI? A Flying Wheel for Data AI Example in Amazon and AWS The Challenges How We can Help You
  3. 3. 43,252,003,274,489,856,000 43 QUINTILLION UNIQUE COMBINATIONS
  4. 4. SOLVED IN 0.9 SECONDS
  5. 5. F2 U' R' L F2 R L' U' Learning function
  6. 6. F2 U' R' L F2 R L' U' Learning function 1% accuracy R U r U R U2 r U2% accuracy
  7. 7. Learning function 20% accuracy 40% accuracy 60% accuracy 80% accuracy 95% accuracy 2% accuracy
  8. 8. Learning function 95% accuracy ? F2 R F R′ B′ D F D′ B D F
  9. 9. Behind The Scene
  10. 10. A Flywheel For Data Machine Learning Deep Learning AI More Users Better Products More Data Better Analytics Object Storage Databases Data warehouse Streaming analytics BI Hadoop Spark/Presto Elasticsearch Click stream User activity Generated content Purchases Clicks Likes Sensor data
  11. 11. Machine Learning & Artificial Intelligence Big Data More Users Better Products More Data Better Analytics A Flywheel For Data
  12. 12. Algorithms Data Programming Models GPUs & Acceleration The Advent of Deep Learning image understanding natural language processing speech recognition autonomy
  13. 13. AI Applications on AWS Zillow • Zestimate (using Apache Spark) Howard Hughes Corp • Lead scoring for luxury real estate purchase predictions FINRA • Anomaly detection, sequence matching, regression analysis, network/tribe analysis Netflix • Recommendation engine Pinterest • Image recognition search Fraud.net • Detect online payment fraud DataXu • Leverage automated & unattended ML at large scale (Amazon EMR + Spark) Mapillary • Computer vision for crowd sourced maps Hudl • Predictive analytics on sports plays Upserve • Restaurant table mgmt & POS for forecasting customer traffic TuSimple • Computer Vision for Autonomous Driving Clarifai • Computer Vision APIs
  14. 14. AI on AWS and Amazon
  15. 15. AI Applications on AWS Pinterest Lens Netflix Recommendation Engine
  16. 16. Thousands Of Employees Across The Company Focused on AI Discovery & Search Fulfilment & Logistics Enhance Existing Products Define New Product Categories Bring Machine Learning To All Artificial Intelligence At Amazon
  17. 17. Thousands Of Employees Across The Company Focused on AI Discovery & Search Artificial Intelligence At Amazon
  18. 18. Artificial Intelligence At Amazon (1995)
  19. 19. Thousands Of Employees Across The Company Focused on AI Discovery & Search Fulfilment & Logistics Artificial Intelligence At Amazon
  20. 20. Thousands Of Employees Across The Company Focused on AI Discovery & Search Fulfilment & Logistics Enhance Existing Products Artificial Intelligence At Amazon
  21. 21. Thousands Of Employees Across The Company Focused on AI Discovery & Search Fulfilment & Logistics Enhance Existing Products Define New Product Categories Artificial Intelligence At Amazon
  22. 22. Thousands Of Employees Across The Company Focused on AI Discovery & Search Fulfilment & Logistics Enhance Existing Products Define New Product Categories Bring Machine Learning To All Artificial Intelligence At Amazon
  23. 23. Sounds interesting! Is there any challenges?
  24. 24. Data Training Prediction The Challenge For Artificial Intelligence: SCALE
  25. 25. Tons of GPUs and CPUs Serverless At the Edge, On IoT Devices Prediction The Challenge For Artificial Intelligence: SCALE Tons of GPUs Elastic capacity Training Pre-built images Aggressive migration New data created on AWS Data PBs of existing data
  26. 26. Can We Help Customers Put Intelligence At The Heart Of Every Application & Business?
  27. 27. Machine Learning & Artificial Intelligence Big Data More Users Better Products More Data Better Analytics A Flywheel For Data
  28. 28. Amazon AI Intelligent Services Powered By Deep Learning
  29. 29. Amazon AI: New Deep Learning Services Life-like Speech Polly Lex Conversational Engine Rekognition Image Analysis Deep Learning Frameworks MXNet, TensorFlow, Theano, Caffe, Torch
  30. 30. DIY Deep Learning for Custom Models AI Enabled Managed API Services Amazon AI: New Deep Learning Services Polly LexRekognition Deep Learning Frameworks MXNet, TensorFlow, Theano, Caffe, Torch CONTROL USABILITY& SIMPLICITY
  31. 31. AWS offers a range of tools to make AI/ML more accessible PollyLex Rekognition Deep Learning FrameworksMachine Learning PlatformsAmazon AI/ML Services Usability/simplicity: leverages AWS AI/ML expertise Greater control: customer-specific models These solutions are underpinned by proven, scalable AWS products and services AWS Greengrass AWS IoT AWS Lambda Amazon EC2 (P2 and G2 GPUs) Amazon S3 Amazon DynamoDB Amazon Redshift Amazon EC2 (CPUs) Amazon EC2 (ENA) Amazon ML Spark & EMR Kinesis Batch ECS MXNet, TensorFlow, Theano, Caffe, Torch
  32. 32. One-Click GPU Deep Learning AWS Deep Learning AMI Up to~40k CUDA cores MXNet TensorFlow Theano Caffe Torch Pre-configured CUDA drivers Anaconda, Python3 + CloudFormation template + Container Image
  33. 33. MXNet: Scalable Deep Learning Framework
  34. 34. Amazon Polly
  35. 35. Converts text to life-like speech 47 voices 24 languages Low latency, real time Fully managed Polly: Life-like Speech Service Voice Quality & Pronunciation 1. Automatic, Accurate Text Processing 2. Intelligible and Easy to Understand 3. Add Semantic Meaning to Text 4. Customized Pronunciation Articles and Blogs Training Material Chatbots (Lex) Public Announcements
  36. 36. Today in Las Vegas, NV it's 54°F
  37. 37. "We live for the music", live from the Madison Square Garden
  38. 38. St. Mary's Church is at 226 St. Mary's St.
  39. 39. Richard’s number is 2025551212
  40. 40. My name is Hermione. It is spelled Hermione
  41. 41. Jesus Manuel Corona is a professional footballer
  42. 42. Still lost in the forest, Marry started to whisper: "Don't make any noise, they will find us"
  43. 43. Duolingo voices its language learning service Using Polly Duolingo is a free language learning service where users help translate the web and rate translations. With Amazon Polly our users benefit from the most lifelike Text-to-Speech voices available on the market. Severin Hacker CTO, Duolingo ” “ • Spoken language crucial for language learning • Accurate pronunciation matters • Faster iteration thanks to TTS • As good as natural human speech
  44. 44. Polly (Salli) Duolingo Old voice Winner! ”The new voice is a huge improvement ! I really like it, the old one was terrible at times.”
  45. 45. Amazon Lex
  46. 46. 1st Gen: Machine-oriented interactions 2nd Gen: Control-oriented & translated 3rd Gen: Intent-oriented The Advent Of Conversational Interactions
  47. 47. Voice & Text “Chatbots” Powers Alexa Voice interactions on mobile, web & devices Text interaction with Slack & Messenger Enterprise Connectors (with more coming) Salesforce Microsoft Dynamics Marketo Zendesk Quickbooks Hubspot Lex: Build Natural, Conversational Interactions In Voice & Text Improving human interactions… • Contact, service, and support center interfaces (text + voice) • Employee productivity and collaboration (minutes into seconds)
  48. 48. Origin Destination Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Natural Language Understanding Book Flight London Utterances Flight booking London Heathrow Intent / Slot model London Heathrow
  49. 49. Origin Destination Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Natural Language Understanding Book Flight London Utterances Flight booking London Heathrow Intent / Slot model London Heathrow LocationLocation Seattle
  50. 50. Origin Destination Departure Date Flight Booking “Book a flight to London” Automatic Speech Recognition Natural Language Understanding Book Flight London Utterances Flight booking London Heathrow Intent / Slot model London Heathrow LocationLocation Seattle Prompt “When would you like to fly?” “When would you like to fly?” Polly
  51. 51. Origin Destination Departure Date Flight Booking London Heathrow Seattle Prompt “When would you like to fly?” “When would you like to fly?” Polly “Next Friday”
  52. 52. Origin Destination Departure Date Flight Booking “Next Friday” Automatic Speech Recognition Next Friday Utterances Natural Language Understanding Flight booking 02 / 24 / 2017 Intent / Slot model London Heathrow Seattle 02/24/2017
  53. 53. Origin Destination Departure Date Flight Booking “Next Friday” Automatic Speech Recognition Next Friday Utterances Natural Language Understanding Flight booking 02 / 24 / 2017 Intent / Slot model London Heathrow Seattle 02/24/2017 Confirmation “Your flight is booked for next Friday” “Your flight is booked for next Friday” Polly
  54. 54. Origin Destination Departure Date Flight Booking “Next Friday” Automatic Speech Recognition Next Friday Utterances Natural Language Understanding Flight booking 02 / 24 / 2017 Intent / Slot model London Heathrow Seattle 02/24/2017 Hotel Booking
  55. 55. Amazon Rekognition
  56. 56. Rekognition: Search & Understand Visual Content Real-time & batch image analysis Object & Scene Detection Facial Detection Face SearchFacial Analysis
  57. 57. Amazon Rekognition Deep learning-based image recognition service Search, verify, and organize millions of images Object and Scene Detection Facial Analysis Face Comparison Facial Recognition Integrated with S3, Lambda, Polly, Lex
  58. 58. Rekognition: Object & Scene Detection
  59. 59. Demo
  60. 60. Let’s try it out
  61. 61. Object and Scene Detection Generate labels for thousands of objects, scenes, and concepts, each with a confidence score • Search, filter, and curate image libraries • Smart searches for user generated content • Photo, travel, real estate, vacation rental applications Maple Plant Villa Garden Water Swimming Pool Tree Potted Plant Backyard
  62. 62. Facial Analysis Locate faces within images and analyze face attributes to detect emotion, pose, facial landmarks, and features • Avoid faces when cropping images and overlaying ads • Capture user demographics and sentiment • Recommend the best photos • Improve online dating match recommendations • Dynamic, personalized ads
  63. 63. Age Range 38-59 Beard: False 84.3% Emotion: Happy 86.5% Eyeglasses: False 99.6% Eyes Open: True 99.9% Gender: Male 99.9% Mouth Open: False86.2% Mustache: False 98.4% Smile: True 95.9% Sunglasses: False 99.8% Bounding Box Height: 0.36716.. Left: 0.40222.. Top: 0.23582.. Width: 0.27222.. Landmarks EyeLeft EyeRight Nose MouthLeft MouthRight LeftPupil RightPupil LeftEyeBrowLeft LeftEyeBrowRight LeftEyeBrowUp : Quality Brightness 52.5% Sharpness 99.9%
  64. 64. Rekognition: Facial Search Facial comparison Face Search Visual Similarity Search (compare two faces) (compare many faces) (find similar faces)
  65. 65. Face Comparison
  66. 66. Face Comparison Measure the likelihood that faces in two images are of the same person • Add face verification to applications and devices • Extend physical security controls • Provide guest access to VIP-only facilities • Verify users for online exams and polls
  67. 67. Facial Search Identify people in images by finding the closest match for an input face image against a collection of stored face vectors • Add friend tagging to social and messaging apps • Assist public safety officers find missing persons • Identify employees as they access sensitive locations • Identify celebrities in historical media archives
  68. 68. Image Moderation Hierarchical taxonomy Confidence score "ModerationLabels": [ { "Confidence": 82.7555923461914, "Name": "Suggestive", "ParentName": "" }, { "Confidence": 82.7555923461914, "Name": "Female Swimwear or Underwear", "ParentName": "Suggestive" }, { "Confidence": 50.11056137084961, "Name": "Nudity and Sexuality", "ParentName": ""
  69. 69. Travel and Hospitality Anticipatory guest experiences for hotels using Amazon Rekognition for facial recognition and sentiment capture Kaliber is using Amazon Rekognition to help front desk agents enhance relationships with guests: • Recognize guests early for instant and personalized service • Receive rich, contextualized guest information in real time • Track guest sentiment throughout their stay • Drive an 80% increase in guest satisfaction scores
  70. 70. Media Case Study Identify who is on camera at what time for each of 8 networks so that recorded video streams can be indexed and searched Video frame-sampling facial recognition solution using Amazon Rekognition: • Indexed 97,000 people into a face collection in 1 day • Sample frames every 6 secs and test for image variance • Upload images to S3 and call Rekognition to find best facial match • Store time stamp and faceID metadata
  71. 71. Celebrity Recognition
  72. 72. Influencer Marketing Case Study Associate influencers with objects and scenes in social media images in order to create high impact campaigns for clients Using Rekognition for metadata extraction: • Create rich media indexes of images from social media feeds, which the application associates with influencers • Enable analytics to profile environments where influence is strongest • Connect client brands with the influencers most likely to have impact
  73. 73. Rekognition Customers Media and Entertainment Public Safety Law Enforcement Digital Asset Management Influencer Marketing Digital Advertising Education Consumer Storage
  74. 74. Amazon AI Services • Leveraging Amazon internal experiences with AI / ML • Managed API services with embedded AI for maximum accessibility and simplicity • Full stack of platforms and engines for specialized deep learning applications
  75. 75. Q&A
  76. 76. Remember to complete your evaluations!
  77. 77. Thank You Yi-an Han yianhan@amazon.com

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