The document provides an overview of artificial intelligence services available on Amazon Web Services (AWS). It discusses Amazon Polly for text-to-speech, Amazon Rekognition for image and video analysis, and Amazon Lex for conversational interfaces. It also describes Amazon Machine Learning for building machine learning models and Amazon EMR for running Spark and Hadoop clusters. Customers in various industries are using these AI services to power applications like fraud detection, personalization, targeted marketing, and more.

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Artificial Intelligence on AWS
An Introduction to AI services
Adrian Hornsby, Technical Evangelist
@adhorn
adhorn@amazon.com

2. What to expect
• What is AI anyway?
• AI ecosystem.
• How customers use AI on AWS.
• Wrap up.

3. The difficulty comes in writing software that will make sense
of the data.

4. A system or service which can perform tasks
that usually require human intelligence
Artificial Intelligence

6. The (60 years) rise of Artificial Intelligence

7. Artificial neural network

8. The Challenge For Artificial Intelligence: SCALE
Training
Tons of GPUs

9. Data
The Challenge For Artificial Intelligence: SCALE
PBs of existing data
Training
Tons of GPUs

10. Prediction
The Challenge For Artificial Intelligence: SCALE
Tons of GPUs and CPUs
Data
PBs of existing data
Training
Tons of GPUs

11. New Features
for Existing Products
The Promise Of Artificial Intelligence: INNOVATION

12. New Experiences
and Product Categories
New Features
for Existing Products
The Promise Of Artificial Intelligence: INNOVATION

13. Breakthrough
Advances
New Experiences
and Product Categories
New Features
for Existing Products
The Promise Of Artificial Intelligence: INNOVATION

18. Amazon AI Ecosystem

19. Amazon AI Ecosystem

20. Polly
Text-to-Speech
Artificial Intelligence Services on AWS

21. Text In, Life-like Speech Out
Amazon Polly
“Today in Seattle, WA
it’s 11°F”
“Today in Seattle Washington
it’s 11 degrees Fahrenheit”

22. “Today in Seattle, WA, it’s 11°F”
‘"We live for the music" live from the Madison Square Garden.’
1. Automatic, Accurate Text Processing
A Focus On Voice Quality & Pronunciation

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

24. https://www.w3.org/TR/speech-synthesis/
<speak>
The spelling of my name is
<prosody rate='x-slow'>
<say-as interpret-as="characters">Adrian</say-as>
</prosody>
</speak>
A Focus On Voice Quality & Pronunciation

25. 2. Intelligible and Easy to Understand
3. Add Semantic Meaning to Text
“Richard’s number is 2122341237“
“Richard’s number is 2122341237“
Telephone Number
1. Automatic, Accurate Text Processing
A Focus On Voice Quality & Pronunciation

26. 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
A Focus On Voice Quality & Pronunciation

27. Duolingo voices its language learning service Using Polly
Duolingo is a free language learning service where
users help translate the web and rate translations.
With Amazon Polly our users
benefit from the most lifelike
Text-to-Speech voices
available on the market.
Severin Hacker
CTO, Duolingo
”
“
• Spoken language crucial for language
learning
• Accurate pronunciation matters
• Faster iteration thanks to TTS
• As good as natural human speech

28. Rekognition
Image Analysis
Artificial Intelligence Services on AWS

29. Amazon
Rekognition
Object and scene detection
Facial analysis
Face comparison
Celebrity recognition
Image moderation
Amazon Rekognition: Images In, Rich Metadata
Out

30. Object & Scene Detection

31. Celebrity Recognition

32. Facial Analysis

33. Facial Search

34. Image moderation
Explicit Nudity
Nudity
Graphic Male Nudity
Graphic Female Nudity
Sexual Activity
Partial Nudity
Suggestive
Female Swimwear or Underwear
Male Swimwear or Underwear
Revealing Clothes

35. 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

36. Lex
Conversation Bots
Artificial Intelligence Services on AWS

37. Speech Recognition & Natural
Language Understanding
Amazon Lex
Automatic Speech Recognition
Natural Language Understanding
“What’s the weather
forecast?”
Weather
Forecast

38. Speech Recognition & Natural
Language Understanding
Amazon Lex
Automatic Speech Recognition
Natural Language Understanding
“What’s the weather
forecast?”
“It will be sunny
and 25°C”
Weather
Forecast

39. Amazon Lex Customers

40. Amazon AI Ecosystem

41. Amazon
Machine Learning
Predictive Analytics
Artificial Intelligence Platforms on AWS

42. Amazon ML Supervised Learning Algorithms
Binary classification
(Logistic regression)
Multi-category classification
(Multinomial logistic regression)
Regression
(Linear regression)

45. And a few more examples…
Fraud detection Detecting fraudulent transactions, filtering spam emails,
flagging suspicious reviews, …
Personalization Recommending content, predictive content loading,
improving user experience, …
Targeted marketing Matching customers and offers, choosing marketing
campaigns, cross-selling and up-selling, …
Content classification Categorizing documents, matching hiring managers and
resumes, …
Churn prediction Finding customers who are likely to stop using the
service, free-tier upgrade targeting, …
Customer support Predictive routing of customer emails, social media
listening, …

46. Amazon Machine Learning helps us
reduce complexity and make sense of
emerging fraud patterns. We can see
correlations we wouldn’t have been able to
see otherwise and answer questions it
would have taken us way too long to
answer ourselves.
Oliver ClarkCTO
Fraud.net Case Study
Fraud.net is the world’s leading
crowdsourced fraud prevention platform,
aggregating and analyzing large amounts
of fraud data from thousands of online
merchants in real time. A collaborative
program, Fraud.net is currently the largest
merchant-led effort to combat online
payment fraud, which costs U.S.
merchants an estimated $20 billion
annually. The platform protects more than
2 percent of all U.S. e-commerce, and its
client base and data requirements are
growing at a pace of more than 1,000
percent per year.

48. Howard Hughes Corp
Creating an enterprise data lake on Amazon S3 by The
Howard Hughes Corporation and 47Lining. Their
business analytics built a lead-scoring model using
Amazon Machine Learning (Amazon ML) to predict
propensity to purchase high-end real estate. Some pretty
impressive numbers – 400% increase in the number of
identified qualified leads in their pipeline and more than
10x reduction is lead acquisition cost.
https://www.youtube.com/watch?v=o7atIw2ntgw

49. Howard Hughes Corp

51. Amazon EMR
Elastic MapReduce
Artificial Intelligence Platforms on AWS

52. ML Applications on Amazon EMR

53. Zillow Provides Near-Real-Time Home-Value
Estimates Using Amazon Kinesis
Zillow Group increases machine-learning calculation
performance and scalability and delivers near-real-time
home-valuation data to customers using AWS. The
company houses a portfolio of the largest online real-
estate and home-related brands. Zillow Group runs the
Zestimate, its machine learning–based home-valuation
tool, on Amazon Kinesis and Apache Spark on Amazon
EMR.
Zestimate

55. FINRA Anomaly detection, sequence matching, regression
analysis, network/tribe analysis
https://aws.amazon.com/blogs/big-data/low-latency-access-on-trillions-of-records-finras-
architecture-using-apache-hbase-on-amazon-emr-with-amazon-s3/
The Financial Industry Regulatory Authority (FINRA) is a
private sector regulator responsible for analyzing 99% of
the equities and 65% of the option activity in the US. In
order to look for fraud, market manipulation, insider
trading, and abuse, FINRA’s technology group has
developed a robust set of big data tools in the AWS
Cloud to support these activities.
Early in the 2 ½ year migration of FINRA’s Market
Regulation Portfolio to the AWS Cloud, FINRA developed
a system on AWS to replace an on-premises solution
that allowed analysts to query this trade activity. This
solution provided fast random access across trillions of
trade records, which would quickly grow to over 700 TB
of data.
Most critical system!

56. FINRA
FINRA, the—the Financial Industry Regulatory Authority—is all in on AWS and has gained massive
performance improvements in its stock-market surveillance system, including 400 percent faster response
times, by using the AWS cloud. FINRA, one of the largest independent securities regulators in the United
States, was established to monitor and regulate financial trading practices. The organization is moving all of its
IT infrastructure to AWS and closing data centers in the process. FINRA is moving its databases that ingest and
store billions of financial transaction records daily from Oracle to Amazon RDS and Amazon Aurora,
leveraging AWS database technologies for capturing and storing a daily influx of more than 75 billion financial
records. Steve Randich, EVP and Chief Information Officer, spoke onstage at re:Invent 2016.

58. Amazon AI Ecosystem

59. Deep Learning AMI
Popular Frameworks
Artificial Intelligence Frameworks on AWS

60. One-Click
Deep Learning
AWS Deep Learning AMIs
Amazon Linux & Ubuntu
Up to~40k CUDA cores
Apache MXNet
TensorFlow
Theano
Keras
Caffe
CNTK
Torch
Pre-configured CUDA drivers
Anaconda, Python3
Out-of-the-box Tutorials
+ CloudFormation template
+ Container Image
Available in the AWS Marketplace

61. 0.2
-0.1
...
0.7
Input Output
1 1 1
1 0 1
0 0 0
3
mx.sym.Pooling(data, pool_type="max", kernel=(2,2), stride=(2,2)
lstm.lstm_unroll(num_lstm_layer, seq_len, len, num_hidden, num_embed)
4 2
2 0
4=Max
1
3
...
4
0.2
-0.1
...
0.7
mx.sym.FullyConnected(data, num_hidden=128)
2
mx.symbol.Embedding(data, input_dim, output_dim = k)
Queen
4 2
2 0
2=Avg
Input Weights
cos(w, queen) = cos(w, king) - cos(w, man) + cos(w, woman)
mx.sym.Activation(data, act_type="xxxx")
"relu"
"tanh"
"sigmoid"
"softrelu"
Neural Art
Face Search
Image Segmentation
Image Caption
“People Riding Bikes”
Bicycle, People,
Road, Sport
Image Labels
Image
Video
Speech
Text
“People Riding Bikes”
Machine Translation
“Οι άνθρωποι
ιππασίας ποδήλατα”
Events
mx.model.FeedForward model.fit
mx.sym.SoftmaxOutput
Anatomy of a Deep Learning Model
mx.sym.Convolution(data, kernel=(5,5), num_filter=20)
Deep Learning Models

62. Autonomous Driving Systems

63. Real Time, Per Pixel Object Segmentation

64. Centimeter-accurate positioning

65. Neural Style Transfer

66. Create your own Basquiat with Deep Learning
https://becominghuman.ai/create-your-own-basquiat-with-deep-learning-for-much-less-than-110-million-314aa07c9ba8

67. Early Detection of Diabetic Complications

68. Skin Cancer Detection At Physician-Levels

69. Lung Cancer Detection With Deep Learning & Medical
Imaging

70. Lung Cancer Detection With Deep Learning & Medical
Imaging

71. Amazon AI Ecosystem

72. Up to
40 thousand parallel processing cores
70 teraflops (single precision)
over 23 teraflops (double precision)
Instance Size GPUs GPU Peer
to Peer
vCPUs Memory
(GiB)
Network
Bandwidth*
p2.xlarge 1 - 4 61 1.25Gbps
p2.8xlarge 8 Y 32 488 10Gbps
p2.16xlarge 16 Y 64 732 20Gbps
*In a placement group
Amazon EC2 P2 Instances

79. NVIDIA TESLA V100
The Most Advanced Data Center GPU Ever Built.
640 Tensor Cores
120 teraflops
Pre-optimized for Apache MXNet

80. FPGA Images Available In AWS Marketplace
F 1 I n s t a n c e
W i t h y o u r c u s t o m l o g i c
r u n n i n g o n a n F P G A
D e v e l o p , s i m u l a t e , d e b u g
& c o m p i l e y o u r c o d e
P a c k a g e a s F P G A
I m a g e s
F1 Instances:
Bringing Hardware Acceleration To All

81. “FPGAs may become the platform of
choice for accelerating next-
generation DNNs.”
E Nurvitadhi - 2017

82. Wrap up.

83. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Thank you!