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AI and machine learning

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This presentation discusses matters of AI and machine learning. This presentation was given during the ITU-T workshop on Machine Learning for 5G and beyond, held at ITU HQ in Geneva, Switzerland on 29 Jan 18. More information on the workshop can be found here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/20180129/Pages/default.aspx

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AI and machine learning

  1. 1. Artificial Intelligence & Machine Learning Elena Ehrlich, PhD eeehrlic@amazon.com
  2. 2. What is AI? https://www.geospatialworld.net/blogs/difference-between-ai%EF%BB%BF-machine-learning-and-deep-learning/
  3. 3. Agenda • Image & Video Recognition Rekognition • Deep-Learning Enabled Video Cameras DeepLens • Natural Language Understanding Comprehend • Voice & Convseration Bots Polly, Lex, & Alexa • Fully-Managed Machine Learning Sagemaker
  4. 4. Image Analysis AWS Rekognition
  5. 5. Bay Beach Coast Outdoors Sea Water Palm_tree Plant Tree Summer Landscape Nature Hotel 99.18% 99.18% 99.18% 99.18% 99.18% 99.18% 99.21% 99.21% 99.21% 58.3% 51.84% 51.84% 51.24% Category Confidence Rekognition: Object & Scene Detection
  6. 6. Rekognition: Facial Analysis "FaceDetails": [{ "BoundingBox": { "Height": 0.22111110389232635 , "Left": 0.29600000381469727, "Top": 0.08888889104127884, "Width": 0.4000000059604645 }, "Confidence": 99.9970474243164, "Emotions": [{ "Confidence": 98.48326110839844, "Type": "HAPPY" }, { "Confidence": 15.214723587036133, "Type": "CALM" }, { "Confidence": 1.2157082557678223, "Type": "CONFUSED" }], "AgeRange": { "High": 47, "Low": 30 }, "Beard": { "Confidence": 95.77610778808594, "Value": false }, "Eyeglasses": { "Confidence": 99.68527221679688, "Value": true }, "EyesOpen": { "Confidence": 99.99991607666016, "Value": true }, "Gender": { "Confidence": 99.92896270751953, "Value": ”Female" }, "MouthOpen": { "Confidence": 99.90928649902344, "Value": true }, "Mustache": { smart cropping & ad overlays sentiment capture demographic analysis face editing & pixelation DetectFaces { "contentString": { "Attributes": [ "ALL" ], "Image": { "Bytes": "..." } } }
  7. 7. Rekognition: Compare Faces Face Comparision
  8. 8. 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": "Covered Nudity", "ParentName": "Nudity and Sexuality" }, { "Confidence": 50.11056137084961, "Name": "Nudity and Sexuality", "ParentName": "" }, ] Rekognition: Image Moderation Suggestive 82.7% Female Swimwear or Underwear 82.7% Nudity and Sexuality 50.1% Covered Nudity 50.1%
  9. 9. https://console.aws.amazon.com/rekognition/home?region=us-east-1#/label-detection https://d3qtbfbtl5c95j.cloudfront.net/Main.html http://iad-front.deepvideoanalysis.cloud/results.html#!?identifier=introducingamazongo.mp4 https://aws.amazon.com/rekognition/customers/ Interesting Demos…Time permitting
  10. 10. Deep-Learning Enabled Video Cameras AWS DeepLens
  11. 11. DeepLens: Deep-Learning Enabled Video Camera A DL video camera uses deep convolutional neural networks (CNNs) to analyze visual imagery. The device itself is a development environment to build computer vision applications. AWS DeepLens communicates with the following ML endpoints: • Amazon SageMaker, for model training and validation • AWS Lambda, event-driven triggers run inference against CNN models • AWS Greengrass, for deploying updates and functions to your device and other IoT devices April 2018
  12. 12. Natural Language Understanding AWS Comprehend
  13. 13. https://www.ip-watch.org/2018/01/24/itu-4-5-people-ldcs-can-access-mobile-networks-not-using-internet/ Comprehend: Keyword, Sentiment, & Topic Modeling
  14. 14. Comprehend: Keyword, Sentiment, & Topic Modeling
  15. 15. Comprehend: Keyword, Sentiment, & Topic Modeling
  16. 16. Comprehend: Keyword, Sentiment, & Topic Modeling
  17. 17. Life-like Speech AWS Polly
  18. 18. Polly: Life-like Speech Service Plain Text SSML Lexicons Plain Text SSML Lexicons Speech Synthesis Markup Language <speak> - Start Tag <break> - Pause in Speech <lang> - Specifies the language <mark> - Tag Name for specific word <p> - Indicates Paragraph <phoneme>- phonetic pronunciation <prosody> - Controls the volume <s> - Indicates a sentence <say-as>- Interpretation <sub> - Alias words <w> - Customize pronunciation <amazon:effect name="whispered"> <lexeme> <grapheme>espresso</grapheme > <alias>ess-press-oh</alias> </lexeme>
  19. 19. Conversational Engines AWS Lex
  20. 20. Lex: The Advent Of Conversational Interactions 1st Gen: Machine-oriented interactions 2nd Gen: Control-oriented & translated 3rd Gen: Intent-oriented Speech Recognition Language UnderstandingBusiness Logic Disparate Systems Authentication Messaging platforms Scale Testing Security Availability Mobile
  21. 21. Lex – Converstaional Engines 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 …. Operational Bots Chatbots for IT automation • Reset my Password • TCO analysis • Productivity….
  22. 22. Most Common Algorithms Provided •Linear Learner •Factorization Machines •XGBoost Algorithm •Image Classification Algorithm •Amazon SageMaker Sequence2Sequence •K-Means Algorithm •Principal Component Analysis (PCA) •Latent Dirichlet Allocation (LDA) •Neural Topic Model (NTM) •DeepAR Forecasting •BlazingText import sagemaker Sagemaker: Fully-Managed Machine Learning 10x Performance Single-Click Training Train Models at Petabyte Scale Deploy in Production Auto-Scaling Cluster of AWS EC2 Instances OpenSource tools TensorFlow Apache MXNet A/B Testing Built-in
  23. 23. AI/ML Adoption Benefits CONVERTING THE POWER OF MACHINE LEARNING INTO BUSINESS VALUE MAKING THE BEST USE OF A DATA SCIENTISTS TIME EMBEDDING MACHINE LEARNING INTO THE FABRIC OF YOUR BUSINESS While the power of ML is unrivaled, “data scientists spend around 80% of their time on preparing and managing data for analysis” … hence only 20% of their time is used to derive insights The value of data science relies upon operationalizing models within business applications and processes, yet “50% of the predictive models [built] don’t get implemented” While “60% of companies agree that big data will help improve their decision making and competitiveness … only 28% indicate that they are currently generating strategic value from their data” 1 2 3
  24. 24. Thank you Elena Ehrlich, PhD eeehrlic@amazon.com
  25. 25. Appendix
  26. 26. Services Platforms Frameworks AWS AI/ML: The Stack Apache MXNet KerasGluonPyTorch Cognitive Toolkit Caffe2 & Caffe Tensor- Flow AWS Deep Learning AMI SageMaker Mechanical Turk AWS DeepLens Amazon ML Spark & EMR Speech: Polly & Transcribe Vision: Rekognition Image & Rekognition Video Language: Lex, Translate & Comprehend
  27. 27. AWS AI/ML: Notable Successes Services Platforms Frameworks
  28. 28. AWS AI/ML: Solutions for Every Skill Level • Designed for Developers & Data Scientists • Solution-oriented Prebuilt Models Available via APIs • Image Analysis, NLU, NLP, Translation, Text-to-Speech & Speech-to-Text • Designed for Data Scientists to Address Common Needs • Fully Managed Platform for Model Building • Reduces the Heavy Lifting in Model Building & Deployment • Designed for Data Scientists to Address Advanced / Emerging Needs • Provides Maximum Flexibility to develop on the leading AI Frameworks • Enables Expert AI Systems to be Developed & Deployed Services Platforms Frameworks
  29. 29. Real-time & batch image analysis Object & Scene Detection Facial Detection Face SearchFacial Analysis Rekognition: Search & Understand Visual Content Image Moderation Celebrity Recognition
  30. 30. Rekognition: Image Moderation Manual Review Approved Rejected Picture posted to end users No inappropriate content detected Inappropriate content detected Object CreationUpload picture Users S3 Bucket Rekognition Lambda User Notification
  31. 31. Rekognition: Video - Case Study Architecture
  32. 32. Polly: Life-like Speech Service Converts text to life-like speech 47 voices 27 languages Low latency, real time Fully managed
  33. 33. Lex: Build Natural, Conversational Interactions In Voice & Text 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
  34. 34. BOT Intent Slot & Slot type BOT Intent Slot & Slot type An intent represents an action that the user wants to perform Intent name– A descriptive name for the intent. Sample utterances – How a user might convey the intent. How to fulfill the intent – How you want to fulfill the intent after the user provides the necessary information Slot - An intent can require zero or more slots or parameters Slot type – Each slot has a type. You can create your custom slot types or use built-in slot types Lex: Build Natural, Conversational Interactions In Voice & Text An Amazon Lex bot is powered by Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) capabilities
  35. 35. Response Cards • Simplify interactions for your users • Increase bot's accuracy • Can be used with Facebook Messenger, Slack, and Twilio as well as your own client applications.
  36. 36. DeepLens Architecture
  37. 37. IoT Anomaly Detection AWS Kinesis Analytics
  38. 38. Kinesis Analytics: real-time insights from streaming data
  39. 39. Kinesis Analytics: real-time insights from streaming data
  40. 40. AI Inquisitors AI Adopters AI Experts Interested in AI but have limited expertise and/or resources Limited expertise and/or use of AI for one-off projects Advanced expertise and/or use of embedded AI in apps AI/ML Assessment
  41. 41. Business Value Ability to Execute Data Availability Assessing POC Targets: Criteria
  42. 42. Prep Question Sample Answer What Business or Operational benefits are you trying to drive? • Improve content personalization How will you consume the outputs and put them into action? • Content will be distributed at a targeted level What types of data is available today? Where does the data reside? • Content and subscription data What types of analytics and/or machine learning are being employed today? • Business Intelligence • Predictive Analytics What staff and/or consultants currently support these activities? • Data Engineers • Data Scientists What software currently supports these activities? • R / Python What is your ideal scenario in tackling these business objectives? • One-to-one content for individuals What challenges have you experienced when deploying AI? • Prioritization of Targets • Operationalization AI/ML Assessment

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