Presentation given at Raleigh DevDay on August 1, 2017 during the Workshop session

2. WIFI: awsDevDay | PASS: CodeHappy
U P N E X T :
Distributed Deep Learning on
AWS with Apache MXNet

3. T H A N K S T O O U R F R I E N D S A T :

4. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

5. Artificial Intelligence
At Amazon

6. An Overview of AI on AWS
Apache
Apache
MXNet
Deep learning framework

7. An Overview of AI on AWS
Apache
Amazon
Polly
Text-to-Speech
Apache
MXNet
Deep learning framework

8. An Overview of AI on AWS
Apache
Amazon
Polly
Text-to-Speech
Amazon
Rekognition
Computer Vision
Apache
MXNet
Deep learning framework

9. An Overview of AI on AWS
Apache
Amazon
Polly
Text-to-Speech
Amazon
Rekognition
Amazon
Lex
Computer Vision ASR & NLU
Apache
MXNet
Deep learning framework

10. An Overview of AI on AWS
Apache
MXNet
Apache
Deep learning framework

11. Apache MXNet
Programmable Portable High Performance
Near linear scaling
across hundreds of GPUs
Highly efficient
models for mobile
and IoT
Simple syntax,
multiple languages

12. Why Apache MXNet?
Most Open Best On AWS
Optimized for
deep learning on AWS
Accepted into the
Apache Incubator
(Integration with AWS)

13. Apache MXNet is the deep learning framework
of choice for AWS

14. P2 INSTANCES DL CLOUD FORMATION
TEMPLATE
DL AMIS

15. An Overview of AI on AWS
Amazon
Polly
Text-to-Speech
Apache

16. Amazon Polly: Life-like Text-to-Speech Service
Converts text
to life-like speech
47 voices 24 languages Low latency,
real time
Fully managed

17. Let’s take a listen…

18. “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

19. 2. Intelligible and Easy to Understand
1. Automatic, Accurate Text Processing
Amazon Polly:
A Focus On Voice Quality & Pronunciation

20. 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

21. 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

22. 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

23. An Overview of AI on AWS
Amazon
Rekognition
Computer Vision
Apache

24. Amazon Rekognition: Computer Vision Service
Object and Scene
Detection
Facial
Analysis
Facial
Comparison
Facial
Recognition

25. Amazon Rekognition: Computer Vision Service
State-of-the-art face recognition (bounding box and key features).

26. 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

27. Amazon Rekognition: Object & Scene Detection

28. Amazon Rekognition: Facial Search
Facial
verification
Face
Search
Visual Similarity
Search
(compare two faces) (compare many faces) (find similar faces)

29. 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

30. An Overview of AI on AWS
Amazon
Lex
ASR & NLU
Apache

31. The Advent Of Conversational Interactions
1st Gen: Machine-oriented
interactions

32. The Advent Of Conversational Interactions
1st Gen: Machine-oriented
interactions
2nd Gen: Control-oriented
& translated

33. The Advent Of Conversational Interactions
1st Gen: Machine-oriented
interactions
2nd Gen: Control-oriented
& translated
3rd Gen:
Intent-oriented

34. Amazon Lex .. for Conversational Interactions
Powered by the same deep learning technology as Alexa
Enterprise SaaS Connectors
Deployment to chat platforms, like Slack, FB
Messenger, Twilio SMS
Build Voice and Text Chatbots
Interactions on mobile, web, and devices

35. Informational Bot: Example

37. 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 ….

38. AI Solutions for Every Developer

39. https://aws.amazon.com/amazon-ai/
Amazon AI: Getting Started

40. Q&A

41. Thank you!
aws.amazon.com/amazon-ai

42. Thank You!

43. Don’t Forget Evaluations!