Have you always wanted to add predictive capabilities, image or voice recognition to your application, but haven’t been able to find the time or the right technology to get started? Everybody wants to build smart apps, but only a few are Data Scientists. This session will help you understand machine learning terminology & challenges, what deep learning is and the possible use cases, how to build a machine learning model that works, and how to use developer-ready APIs for high-quality, high-accuracy AI capabilities that are scalable and cost-effective.

1. Machines are Learning
Danilo Poccia, AWS Technical Evangelist
CODEMOTION MILAN - SPECIAL EDITION
10 – 11 NOVEMBER 2017

2. Machines are Learning
Bringing Powerful Artificial Intelligence to All Developers
Danilo Poccia
AWS Technical Evangelist
@danilop danilop@amazon.comdanilop

3. Credit: Gerry Cranham/Fox Photos/Getty Images
http://www.telegraph.co.uk/travel/destinations/europe/united-kingdom/england/london/galleries/The-history-of-the-Tube-in-pictures-150-years-of-London-Underground/1939-ticket-examin/

4. 1939 London Underground
Credit: Gerry Cranham/Fox Photos/Getty Images
http://www.telegraph.co.uk/travel/destinations/europe/united-kingdom/england/london/galleries/The-history-of-the-Tube-in-pictures-150-years-of-London-Underground/1939-ticket-examin/

5. Data Predictions

6. Data Model Predictions

7. Model

8. http://www.thehudsonvalley.com/articles/60-years-ago-today-local-technology-demonstrated-artificial-intelligence-for-the-first-time
1959 Arthur Samuel

9. Machine Learning

10. Machine Learning
Supervised
Learning
Inferring a model
from labeled
training data

11. Machine Learning
Supervised
Learning
Unsupervised
Learning
Inferring a model
from labeled
training data
Inferring a model
to describe hidden
structure from
unlabeled data

12. Reinforcement
Learning
Perform a certain
goal in a
dynamic
environment
Machine Learning
Supervised
Learning
Unsupervised
Learning

13. Driving a vehicle
Playing a game
against an opponent

14. Clustering

15. Clustering

16. Tip: Try topic modeling with your own emails ;-)
Topic Modeling
Discovering abstract “topics”
that occur in a collection of documents
For example, looking for “infrequent” words
that are used more often in a document

17. Regression
“How many bikes will
be rented tomorrow?”
Happy, Sad, Angry,
Confused, Disgusted,
Surprised, Calm,
Unknown
Binary
Classification
Multi-Class
Classification
“Is this email spam?”
“What is the
sentiment of this
tweet, or of this social
media comment?”
1, 0, 100K
Yes / No
True / False
%

18. Training the Model
Minimizing the Error
of using the Model on the Labeled Data

19. Validation
How well is this Model working on New Data?

20. Be Careful of Overfitting

21. Be Careful of Overfitting

22. Be Careful of Overfitting

23. Better Fitting

24. Better Fitting

25. Different Models ⇒ Different Predictions

26. Labeled Data

27. Labeled Data
70%
30%
Training
Validation

28. Neural
Networks

29. 1943 Warren McCulloch, Walter Pitts
Threshold
Logic
Units

30. 1962 Frank Rosenblatt
Perceptron

31. ∑
w1
w2
w3
wn
w0 = 𝜃
output
weights
(parameters)
activation
function
input

32. f(∑)
w1
w2
w3
wn
w0 = 𝜃
weights
(parameters)
activation
function
outputinput

33. f(∑)input output

34. 1969 Marvin Minsky, Seymour Papert
Perceptrons:
An Introduction
to Computational Geometry
A perceptron can only solve
linearly separable functions
(e.g. no XOR)

35. f(∑)
f(∑)
f(∑)
f(∑)
f(∑)
f(∑)
f(∑)
f(∑)
f(∑)
input
layer
hidden
layer
output
layer
input output
Multiple Layers
Lots of Parameters
Backpropagation

36. Microprocessor Transistor Counts 1971-2011
Intel Xeon CPU
28 cores
NVIDIA V100 GPU
5,120 CUDA Cores
640 Tensor Cores
https://en.wikipedia.org/wiki/Moore's_law

37. LeCun, Gradient-Based
Learning Applied to Document
Recognition,1998
Hinton, A Fast Learning
Algorithm for Deep Belief
Nets, 2006
Bengio, Learning Deep
Architectures for AI, 2009
Advances in Research 1998-2009

38. “Stacks of differentiable
non-linear functions
with lots of parameters
solve nearly any predictive
modeling problem”
—Jeremy Howard, fast.ai

39. Image
Processing

40. f(∑)
f(∑)
f(∑)
f(∑)
f(∑)
f(∑)
f(∑)
f(∑)
f(∑)
output
How to give images in input
to a Neural Network?
Photo by David Iliff. License: CC-BY-SA 3.0
https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg

41. Convolution Matrix
0 0 0
0 1 0
0 0 0
Identity
Photo by David Iliff. License: CC-BY-SA 3.0
https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg

42. Convolution Matrix
1 0 -1
2 0 -2
1 0 -1
Left Edges
Photo by David Iliff. License: CC-BY-SA 3.0
https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg

43. Convolution Matrix
-1 0 1
-2 0 2
-1 0 1
Right Edges
Photo by David Iliff. License: CC-BY-SA 3.0
https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg

44. Convolution Matrix
1 2 1
0 0 0
-1 -2 -1
Top Edges
Photo by David Iliff. License: CC-BY-SA 3.0
https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg

45. Convolution Matrix
-1 -2 -1
0 0 0
1 2 1
Bottom Edges
Photo by David Iliff. License: CC-BY-SA 3.0
https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg

46. Convolution Matrix
0.6 -0.6 1.2
-1.4 1.2 -1.6
0.8 -1.4 1.6
Random Values
Photo by David Iliff. License: CC-BY-SA 3.0
https://commons.wikimedia.org/wiki/File:Colosseum_in_Rome,_Italy_-_April_2007.jpg

47. Convolutional Neural Networks (CNNs)
https://en.wikipedia.org/wiki/Convolutional_neural_network

48. ImageNet Classification Error Over Time
0
5
10
15
20
25
30
2010 2011 2012 2013 2014 2015 2016 2017Classification Error
CNNs

49. 2012 ImageNet Classification with Deep Convolutional Neural Networks

50. SuperVision: 8 layers, 60M parameters
0

51. 2013 Visualizing and Understanding Convolutional Networks

54. How Do Neural Networks Learn?
?
More generic and can be reused
as feature extractor for other visual tasks
Specific
to task
Cat
Dog0

55. The Challenge For Machine Learning: Scale
Aggressive migration
New data created on AWS
PBs of existing data
Data

56. The Challenge For Machine Learning: Scale
Tons of GPUs
Elastic capacity
Pre-built images
Aggressive migration
New data created on AWS
PBs of existing data
Data Training

57. The Challenge For Machine Learning: Scale
Tons of GPUs and CPUs
Serverless
At the edge, on IoT Devices
Tons of GPUs
Elastic capacity
Pre-built images
Aggressive migration
New data created on AWS
PBs of existing data
Data Training Prediction

58. Natural Language Processing Experiment: Topic Modeling
EC2 Spot Instances
1.1 Million vCPUs

59. Fulfilment & logistics Search & discovery Existing products New products
Thousands Of Amazon Engineers Focused On Machine Learning

62. Machine Learning On AWS Today

63. Artificial Intelligence In The Hands Of Every Developer
S E R V I C E S
P L A T F O R M S
E N G I N E S
I N F R A S T R U C T U R E
GPU CPU IoT Mobile
Apache MXNet Caffe 2 Theano PyTorch CNTKTensorFlow

64. Early Detection
Of Diabetic
Complications

65. FDA-approved
Medical Imaging

66. Sports Analytics

67. Autonomous Driving Systems

68. Real Time, Per Pixel Object Segmentation

69. Centimeter-accurate Positioning

70. Computation Knowledge Engine

72. S E R V I C E S
P L A T F O R M S
E N G I N E S
I N F R A S T R U C T U R E
Amazon ML Spark & EMR Kinesis Batch ECS
GPU CPU IoT Mobile
Apache MXNet Caffe 2 Theano PyTorch CNTKTensorFlow
Artificial Intelligence In The Hands Of Every Developer

73. S E R V I C E S
P L A T F O R M S
Vision
Amazon Rekognition
E N G I N E S
I N F R A S T R U C T U R E
Amazon ML Spark & EMR Kinesis Batch ECS
GPU CPU IoT Mobile
Apache MXNet Caffe 2 Theano PyTorch CNTKTensorFlow
Artificial Intelligence In The Hands Of Every Developer

74. Mona Lisa
(Leonardo da Vinci)

78. Mona Lisa
(Prado's version)

79. Portrait of
Maddalena Doni
(Raphael)

80. Bynder allows you to easily create, find and use content
for branding automation and marketing solutions.
With our new AI capabilities,
Bynder’s software… now allows
users to save hours of admin
labor when uploading and
organizing their files, adding
exponentially more value.
Chris Hall
CEO, Bynder
”
“
With Rekognition, Bynder revolutionizes marketing admin tasks with AI capabilities

81. S E R V I C E S
P L A T F O R M S
Speech
Amazon Polly
Vision
Amazon Rekognition
E N G I N E S
I N F R A S T R U C T U R E
Amazon ML Spark & EMR Kinesis Batch ECS
GPU CPU IoT Mobile
Apache MXNet Caffe 2 Theano PyTorch CNTKTensorFlow
Artificial Intelligence In The Hands Of Every Developer

82. Generate Lifelike Speech With Amazon Polly
24 languages
“The temperature in
Milanis 16 degrees
Celsius”
“The temperature
in Milan is 16˚C”
Amazon
Polly
50 voices

83. aws polly synthesize-speech
--text "It was nice to live such a wonderful live show."
--output-format mp3
--voice-id Joanna
--text-type text
output.mp3

84. “Nel mezzo del cammin di nostra vita
mi ritrovai per una selva oscura
ché la diritta via era smarrita.”
https://commons.wikimedia.org/wiki/File:Portrait_de_Dante.jpg

85. 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

86. ”
“
Royal National Institute of Blind People creates and
distributes accessible information in the form of
synthesized content
Amazon Polly delivers
incredibly lifelike voices which
captivate and engage our
readers.
John Worsfold
Solutions Implementation Manager, RNIB
• RNIB delivers largest library of
audiobooks in the UK for nearly 2 million
people with sight loss
• Naturalness of generated speech is
critical to captivate and engage readers
• No restrictions on speech redistributions
enables RNIB to create and distribute
accessible information in a form of
synthesized content
RNIB provides the largest library in the UK for people with sight loss

87. S E R V I C E S
P L A T F O R M S
Chat
Amazon Lex
Speech
Amazon Polly
Vision
Amazon Rekognition
E N G I N E S
I N F R A S T R U C T U R E
Amazon ML Spark & EMR Kinesis Batch ECS
GPU CPU IoT Mobile
Apache MXNet Caffe 2 Theano PyTorch CNTKTensorFlow
Artificial Intelligence In The Hands Of Every Developer

88. Amazon Lex
Speech recognition and natural language understanding
Automatic speech recognition
Natural language understanding
“What’s the weather
forecast?”
Weather
forecast
Amazon Lex

89. Amazon Lex
Speech recognition and natural language understanding
“It will be
sunny
and 16C”
Automatic speech recognition
Natural language understanding
“What’s the weather
forecast?”
Weather
forecast
Amazon Lex

90. “It will be sunny
and 16 degrees
Celsius”
Amazon Polly
Amazon Lex
“It will be
sunny
and 16C”
Automatic speech recognition
Natural language understanding
“What’s the weather
forecast?”
Weather
forecast
Speech recognition and natural language understanding
Amazon Lex

91. ”
“ Finding missing persons:
~100,000 active missing
persons cases in the U.S.
at any given time
~60% are adults,
~40% are children
• Motorola Solutions applies Amazon
Rekognition, Amazon Polly and Amazon
Lex
• Image analytics and facial recognition
can continually monitor for missing
persons
• Tools that understand natural language
can enable officers to keep eyes up and
hands free
Motorola Solutions is using AI to help finding missing persons
Motorola Solutions keeps utility workers connected and
visible to each other with real-time voice and data
communication across the smart grid.

92. S E R V I C E S
P L A T F O R M S
Chat
Amazon Lex
Speech
Amazon Polly
Vision
Amazon Rekognition
E N G I N E S
I N F R A S T R U C T U R E
Amazon ML Spark & EMR Kinesis Batch ECS
GPU CPU IoT Mobile
Apache MXNet Caffe 2 Theano PyTorch CNTKTensorFlow

93. There’s Never Been A Better Time To Build Smart Apps

94. https://github.com/danilop/security-camera

95. Machines are Learning
Bringing Powerful Artificial Intelligence to All Developers
Danilo Poccia
AWS Technical Evangelist
@danilop danilop@amazon.comdanilop