Amazon Lex, Amazon Polly, Amazon Rekognition, Apache MXNet and the AWS Deep Learning AMIs

2. WIFI: awsDevDay | PASS: CodeHappy
U P N E X T :
An Overview of AI at AWS

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.
Keith Steward, Ph.D. | Specialist Solution Architect | AWS
August 1, 2017
An Overview of AI at AWS:
Amazon Lex, Amazon Polly, Amazon Rekognition,
Apache MXNet and the AWS Deep Learning AMIs

5. Artificial Intelligence
at Amazon

6. An Introduction to the AI Services at AWS
Apache
Apache
MXNet
Deep learning framework

7. An Introduction to the AI Services at AWS
Apache
Amazon
Polly
Text-to-Speech
Apache
MXNet
Deep learning framework

8. An Introduction to the AI Services at AWS
Apache
Amazon
Polly
Text-to-Speech
Amazon
Rekognition
Computer Vision
Apache
MXNet
Deep learning framework

9. 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

10. An Introduction to the AI Services at 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 Introduction to the AI Services at 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 Introduction to the AI Services at 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 Introduction to the AI Services at 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, Facebook
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. Thank you!
aws.amazon.com/amazon-ai

41. • 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

42. Amazon & Intel

43. 43@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
+

44. 44@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

45. 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

46. 46@IntelAI
Libraries, frameworks & tools
Intel® Math Kernel Library
Intel® MLSL
Intel® Data
Analytics
Acceleration
Library
(DAAL)
Intel®
Distribution
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

47. Q & A

48. Thank You!

49. Don’t Forget Evaluations!