AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address a wide range of research needs. For domain specialists looking to add managed Artificial Intelligence (AI) services to their research, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and supervised machine learning with Amazon Machine Learning. For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances. Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud. Learn More: https://aws.amazon.com/government-education/

1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Tools of Research
Guy Ernest, Business Development Manager, AI & Deep Learning, AWS
Machine Learning
June 14, 2017

2. Today’s Agenda
What is artificial intelligence?1
What is Amazon AI?
2 How to do research in the cloud?
3
How to get started?
Why Deep Learning?4
5

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

4. The Challenge For Artificial Intelligence: SCALE
Data

5. Data Training
The Challenge For Artificial Intelligence: SCALE

6. Data Training Prediction
The Challenge For Artificial Intelligence: SCALE

7. Aggressive migration
New data created on AWS
Data
Training Prediction
PBs of existing data
The Challenge For Artificial Intelligence: SCALE

8. Tons of GPUs
Elastic capacity
Training
Prediction
Pre-built images
The Challenge For Artificial Intelligence: SCALE
Aggressive migration
New data created on AWS
Data
PBs of existing data

9. Tons of GPUs and CPUs
Serverless
At the Edge, On IoT Devices
Prediction
The Challenge For Artificial Intelligence: SCALE
Tons of GPUs
Elastic capacity
Training
Pre-built images
Aggressive migration
New data created on AWS
Data
PBs of existing data

10. AWS is the Center of Gravity for
Artificial Intelligence

11. Open Data on AWS

12. Data Sets for Research
Landsat
Sentinel-2
SpaceNet
MODIS
NEXRAD
NASA NEX
Geospatial and
Environmental
Genomics and Life
Science
Machine Learning Regulatory and
Statistical Data
1000 Genomes Project
TCGA
ICGC
3000 Rice Genome
GIAB
Common Crawl
Amazon Bin Image Dataset
GDELT
Multimedia Commons
Google Books Ngrams
IRS 990 Filings
ACS PUMS
USAspending.gov

13. Research and Education Grants

14. Example in Life Sciences
Baylor CHARGE project:
• Genomics analysis on
14,000 participants
• 24 terabases of sequencer
content each month
• 1 PB of raw data storage
• 21,000 AWS compute cores
at peak
• Initial analysis completed in
10 days

15. Cluster HPC and Grid HPC on the Cloud
Cluster HPC
Tightly coupled, latency
sensitive applications
Use larger EC2
compute instances,
placement groups,
enhanced networking
Grid HPC
Loosely coupled,
pleasingly parallel
Use a variety of EC2
instances, multiple
AZs, Spot, Auto
Scaling, Amazon SQS
Grids of Clusters
Use a grid strategy on the cloud
to run a group of parallel,
individually clustered HPC jobs

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

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

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

19. Running AI In Production on AWS Today

20. Early Detection of
Diabetic Complications

21. FDA-Approved Medical Imaging

24. Autonomous Driving Systems

25. Real Time, Per Pixel
Object Segmentation

26. Centimeter-accurate
positioning

27. Computational
Knowledge Engine

28. Pinterest
Visual Search

29. Pinterest Lens

30. Recommendations & Ranking At Netflix
Personalized ranking,
page generation,
search, similarity, ratings
In 140 new countries,
simultaneously

31. Thousands of Amazon Engineers Focused on AI
Fulfilment &
Logistics

32. Thousands of Amazon Engineers Focused on AI
Fulfilment &
Logistics
Search &
Discovery

33. Thousands of Amazon Engineers Focused on AI
Fulfilment &
Logistics
Search &
Discovery
Existing
Products

37. Fulfilment &
Logistics
Search &
Discovery
Existing
Products
New
Products
Thousands of Amazon Engineers Focused on AI

42. “The future is here,
it’s just not evenly distributed yet”
William Gibson

43. Amazon AI
“Inside AWS, we’re excited to lower the costs and barriers to
machine learning and AI so organizations of all sizes can take
advantage of these advanced techniques”

44. AI Services
Amazon
Rekognition
Amazon AI: Democratized Artificial Intelligence

45. Rekognition: Object & Scene Detection

46. Rekognition: Facial Detection

47. Rekognition: Facial Comparison

48. Rekognition: Facial Search

49. • Digital Asset Management
• Media and Entertainment
• Travel and Hospitality
• Influencer Marketing
• Systems Integration
• Digital Advertising
• Consumer Storage
• Law Enforcement
• Public Safety
• eCommerce
• Education
Rekognition: Use Cases

50. AI Services
Amazon
Rekognition
Amazon
Polly
Amazon AI: Democratized Artificial Intelligence

51. Let’s take a listen…

54. AI Services
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
Amazon AI: Democratized Artificial Intelligence

55. Amazon Lex: Conversational Chatbots
I’d like to book a hotel.
Sure! What city would you like to book it in?
New York City
When would you like to check in?
Next Friday
Destination:
New York City
Check In:
5/26/2017

56. Amazon Lex: Conversational Chatbots
Lex Bots
Web
Devices
Apps
Facebook Messenger,
Slack,
Amazon
Connect
Mobile
Mobile Hub
integration

57. Amazon Lex: Conversational Chatbots
Lex Bots
Salesforce
Microsoft Dynamics
Marketo
Zendesk
Web
Devices
Apps
Facebook Messenger,
Slack,
Amazon
Connect
Mobile
Mobile Hub
integration
Quickbooks

58. 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 ….

59. AI Services
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
More to come
in 2017
Amazon AI: Democratized Artificial Intelligence

60. AI Services
AI Platform
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
More to come
in 2017
More to come
in 2017
Amazon AI: Democratized Artificial Intelligence
Amazon
Machine Learning
EMR & Spark AWS Batch

61. AI Services
AI Platform
AI Engines
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
More to come
in 2017
More to come
in 2017
Apache
MXNet
TensorFlow Caffe Theano KerasTorch CNTK
Amazon AI: Democratized Artificial Intelligence
Amazon
Machine Learning
EMR & Spark AWS Batch

62. Model
Training
Amazon AI: Building Intelligent Systems, End To End

63. Training Artificial Intelligence With GPUs
Core of the next AWS GPU instance family
Custom built for artificial intelligence
Train in hours, not days
NVIDIA Volta

64. Training Artificial Intelligence With GPUs
Core of the next AWS GPU instance family
Custom built for artificial intelligence
Train in hours, not days
NVIDIA Volta
MXNet is already optimized for Volta

65. Model
Training
Inference
in the Cloud
Amazon AI: Building Intelligent Systems, End To End

66. Model
Training
Inference
in the Cloud
Inference
at the Edge
Amazon AI: Building Intelligent Systems, End To End

67. Significantly improve many applications on multiple domains
“deep learning” trend in the past 10 years
image understanding speech recognition natural language
processing
…
Deep Learning
autonomy

68. 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)
Anatomy of a Deep Learning Model

69. TX1 on Flying Drone
TX1 with customized board
Drone
Realtime detection and tracking on TX1
~10 frame/sec with 640x480 resolution

70. Deploy Everywhere
Beyond
BlindTool by Joseph Paul Cohen, demo on Nexus 4
✦ Fit the core library with all
dependencies into a single C++
source file
✦ Easy to compile on …
Amalgamation
Runs in browser
with Javascript
The first image for
search “dog” at
images.google.com
Outputs “beagle”
with prob = 73%
within 1 sec

71. AI Services
AI Platform
AI Engines
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
More to come
in 2017
Amazon
Machine Learning
Amazon EMR
Spark &
SparkML
More to come
in 2017
Apache
MXNet
Caffe Theano KerasTorch CNTK
Amazon AI: Democratized Artificial Intelligence
TensorFlow

72. “How can we get started?”

73. AI Services Are A No-Brainer (Pun Intended)
High
quality
Deep
functionality
Built for
production
Easy to use
& thoughtfully
integrated
Low
cost

74. Custom Models: A Data-Driven Pursuit
Collect
& Organize

75. Collect
& Organize
Augment
& Enrich
Custom Models: A Data-Driven Pursuit

76. Custom Models: A Data-Driven Pursuit
Collect
& Organize
Augment
& Enrich
Experiment
& Learn

77. Building Intuition For Deep Learning
“Fizz Buzz!”

78. AWS Deep Learning AMI: One-Click Deep Learning
Up To 40,000
CUDA Cores
Apache
MXNet
Python 3 Notebooks
& Examples
(and others)

79. Thank you!