This document discusses Amazon's artificial intelligence capabilities and services. It provides an overview of Amazon's focus on AI across the company in areas like search, logistics, and new product development. It then describes Amazon's machine learning and deep learning technologies and services including Alexa, Lex, Polly, Rekognition and how customers are using them. The document concludes with a case study of how the Washington County Sheriff's Office uses Amazon Rekognition to identify suspects from images.

1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Ben Snively, Sr. Solutions Architect, AWS
Chris Adzima, Washington County Sheriff's Department
Wednesday, Jun 14, 1:00 PM - 1:50 PM – East Salon A
Dynamic Business Logic with Machine
Learning and Deep Learning
Letting Your Data Build Your System

2. Artificial Intelligence
At Amazon (1995)

3. 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

5. Algorithms
Data
Programming
Models
GPUs &
Acceleration
The Advent of Deep Learning
image understanding
natural language
processing
speech recognition
autonomy

6. Amazon AI
Intelligent Services Powered By Deep Learning

7. 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)

8. 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
Atlanta

9. 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
Atlanta
Prompt
“When would you like to fly?”
“When would you
like to fly?”
Polly

10. Origin
Destination
Departure Date
Flight Booking
“Next Friday”
Automatic
Speech Recognition
Next Friday
Utterances
Natural Language
Understanding
Flight booking
05 / 12 / 2017
Intent /
Slot model
London Heathrow
Atlanta
05/12/2017
Confirmation
“Your flight is booked for next Friday”
“Your flight is booked
for next Friday”
Polly

11. 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

12. Rich Metadata Index
objects, scenes, facial attributes, persons
Amazon Rekognition
Deep Learning-Based Image Recognition Service

13. Deer 98.8%
Wildlife 95.1%
Conifer 95.1%
Spruce 95.1%
Wood 78.3%
Tree 63.5%
Forest 63.5%
Vegetation 61.9%
Pine 60.6%
Outdoors 54.0%
Flower 53.9%
Plant 52.9%
Nature 50.7%
Field 50.7%
Grass 50.7%

14. DetectLabels
"Labels": [
{
"Confidence": 98.9294204711914,
"Name": "Moss"
},
{
"Confidence": 98.9294204711914,
"Name": "Plant"
},
{
"Confidence": 97.35887908935547,
"Name": "Creek"
},
{
"Confidence": 97.35887908935547,
"Name": "Outdoors"
},
{
"Confidence": 97.35887908935547,
"Name": "Stream"
},
{
"Confidence": 97.35887908935547,
"Name": "Water"
},

15. 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: False 86.2%
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%

16. "BoundingBox": {
"Height": 0.3449999988079071,
"Left": 0.09666666388511658,
"Top": 0.27166667580604553,
"Width": 0.23000000417232513
},
"Confidence": 100,
"Emotions": [
{"Confidence": 99.1335220336914,
"Type": "HAPPY" },
{"Confidence": 3.3275485038757324,
"Type": "CALM"},
{"Confidence": 0.31517744064331055,
"Type": "SAD"}
],
"Eyeglasses": {"Confidence": 99.8050537109375,
"Value": false},
"EyesOpen": {Confidence": 99.99979400634766,
"Value": true},
"Gender": {"Confidence": 100,
"Value": "Female”}
DetectFaces
smart cropping
& ad overlays
sentiment
capture
demographic
analysis
face editing
& pixelation

17. Amazon Rekognition
Customers
• Digital Asset Management
• Media and Entertainment
• Travel and Hospitality
• Influencer Marketing
• Systems Integration
• Digital Advertising
• Consumer Storage
• Law Enforcement
• Public Safety
• eCommerce
• Education

18. Washington County Sheriff’s Office –
Rekognition Case Study

19. The Problem
• Bulletins sent to multiple
agencies
• PDF containing images
viewed by officers
• If they recognize the person
in the images, they reply to
emails
• Schedule lag, memory, and
attrition make identifying
subjects difficult

20. The Solution

21. Indexing in 3 Steps
$sdk = new AwsSdk($sharedConfig);
$client = $sdk->createS3();
$rekog = new AwsRekognitionRekognitionClient($sharedConfig);
$result = $rekog->indexFaces([
'CollectionId' => 'wcso-let-faces',
'ExternalImageId' => $s3Obj['Key'],
'Image' => [
'S3Object' => [
'Bucket' => 'wcso-let-faces',
'Name' => $s3Obj['Key'],
],
],
]);
$result = $client->listObjects([
'Bucket' => 'wcso-let-faces',
"MaxKeys" => 500,
"Marker" => $line,
]);
1)
2)
3)

22. Searching in 3 Steps
$sdk = new AwsSdk($sharedConfig);
$client = $sdk->createS3();
$rekog = new AwsRekognitionRekognitionClient($sharedConfig);
$result = $rekog->searchFacesByImage([
'CollectionId' => 'wcso-let-faces',
'Image' =>
[
'Bytes' => $imageBytes,
],
"MaxFaces" => 5
]);
$fileData = file_get_contents($_FILES["photos"]["tmp_name"][0]);
1)
2)
3)

23. Real World Example

24. Real World Example

25. Demo

26. Benefits
• Identification has gone from days to minutes
• Learned AWS platform
• Researched, designed and implemented solution within 30
days, with no previous AWS experience
• Assisted in identifying multiple cases where no other
information was available to identify suspects

27. Next Steps
• Expand to all internal Deputies
• Expand collection with help of other agencies
• Create separate collection for finding and helping at risk
seniors/children

28. Thank you!