https://docs.com/satonaoki/9410/connect-japan-2016-microsoft-ai-cognitive-services

1. Microsoft の AI 開発 最新アップデート
～ Cognitive Services から Azure Machine
Learning、Cognitive Toolkit まで ～
佐藤 直生 (@satonaoki)
エバンジェリスト
日本マイクロソフト株式会社

2. Microsoft AI Development Updates
- From Cognitive Services to Azure Machine
Learning and Cognitive Toolkit -
SATO Naoki (@satonaoki)
Evangelist
Microsoft Japan

4. Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Intelligence
Dashboards &
Visualizations
Cortana
Bot
Framework
Cognitive
Services
Power BI
Information
Management
Event Hubs
Data Catalog
Data Factory
Machine Learning
and Analytics
HDInsight
(R Server and
Spark)
Stream Analytics
Intelligence
Data Lake
Analytics
Machine
Learning
Big Data Stores
SQL Data
Warehouse
Data Lake Store
Data
Sources
Apps
Sensors
and
devices
Data

5. Machine Learning and
Azure Machine Learning

6. https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

7. I need our systems to think.
I need them to learn and
I need them to present issues
and problems and anomalies
to the employees, to the managers.
Adam Coffey
President and CEO
WASH Laundry Systems
What is
Machine Learning?
Computing systems
that become smarter
with experience
“Experience” =
past data + human input
“
”

9. Bing maps
launches
What’s the
best way
home?
Microsoft
Research
formed
Kinect
launches
What does
that motion
“mean”?
Azure Machine
Learning GA
What will
happen next?
Hotmail
launches
Which email is
junk?
Bing search
launches
Which
searches are
most relevant?
Skype
Translator
launches
What is that
person saying?
1991 201420091997 201520102008
Machine learning is pervasive throughout Microsoft products.

10. Social network
analysis
Weather
forecasting
Healthcare
outcomes
Predictive
maintenance
Targeted
advertising
Natural resource
exploration
Fraud
detection
Telemetry data
analysis
Buyer propensity
models
Churn analysis
Life sciences
research
Web app
optimization
Network
intrusion
detection
Smart meter
monitoring

19. Fully
managed
Integrated Best in Class
Algorithms + R
Deploy in
minutes
No software to install,
no hardware to manage,
and one portal to view
and update.
Simple drag, drop and
connect interface for
Data Science. No need
for programming for
common tasks.
Built-in collection of
best of breed
algorithms. Support for
R and popular CRAN
packages.
Operationalize models
with a single click.
Monetize in Machine
Learning Marketplace.

20. Blobs and Tables
Hadoop (HDInsight)
Relational DB (Azure SQL DB)
Data Clients
Model is now a web
service that is callable
API
Integrated development environment
for Machine Learning
ML STUDIO

23. https://azure.microsoft.com/en-us/documentation/learning-paths/data-science-process/

25. logistic regression, linear models,
basic statistics, hypothesis testing,
k-means, decision trees
page rank, collaborative filtering,
graph processing, SVD, PCA,
Bayesian models, …
deep learning over
various types of networks

26. product recommendations
intelligent search
routing
robotics
ad placement
predictive maintenance
image, video recognition
sentiment analysis
text comprehension
natural language processing
robotics
bots
augmented reality
predictive maintenance
Retail Financial services Healthcare Manufacturing
loyalty programs
customer acquisition
pricing strategy
supply chain mgnt
customer churn
fraud detection
risk & compliance
cross-sell & upsell
personalization
bill collection
operational efficiency
patient demographics
pay for performance
demand forecasting
pricing strategy
supply chain
optimization
predictive
maintenance
remote monitoring

27. Regression line
represented by an
equation of the
form Y = b0 + b1X
where Y is the
dependent variable
Error between
actual and
computed output
minimized using
least-squares or
gradient-descent
method

30. Azure Machine
Learning Demo

35. https://gallery.cortanaintelligence.com/

37. Deep Learning and
Microsoft Cognitive Toolkit
(CNTK)

38. 2012 – AlexNet
2014 – Image description
2016 – AlphaGo

39. Inception-v3

40. GPU-enabled
And distributed

41. Deep Learning library Language GPU Distributed mode
Theano Python Yes N/A
Torch Lua/C++ Yes N/A
Caffe Python/C++ Yes N/A
DeepLearning4J Java/Scala Yes Spark
TensorFlow Python/C++ Yes Native
CNTK Python/C++ Yes Native
MXNet Python/R/
C++ and
Julia
Yes Native

43. 28.2
25.8
16.4
11.7
7.3 6.7
3.5
ILSVRC
2010 NEC
America
ILSVRC
2011 Xerox
ILSVRC
2012
AlexNet
ILSVRC
2013 Clarifi
ILSVRC
2014 VGG
ILSVRC
2014
GoogleNet
ILSVRC
2015 ResNet
ImageNet Classification top-5 error (%)
Microsoft had all 5 entries being the 1-st places this year: ImageNet classification,
ImageNet localization, ImageNet detection, COCO detection, and COCO
segmentation

48. https://blogs.msdn.microsoft.com/translation/2016/11/15/microsoft-translator-launching-neural-network-based-translations-for-all-its-speech-languages/

52. “CNTK expresses (nearly) arbitrary neural networks by composing simple building
blocks into complex computational networks, supporting relevant network types
and applications.”
example: 2-hidden layer feed-forward NN
h1 = s(W1 x + b1) h1 = sigmoid (x @ W1 + b1)
h2 = s(W2 h1 + b2) h2 = sigmoid (h1 @ W2 + b2)
P = softmax(Wout h2 + bout) P = softmax (h2 @ Wout + bout)
with input x  RM
and one-hot label y  RJ
and cross-entropy training criterion
ce = yT
log P ce = cross_entropy (P, y)
Scorpusce = max

53. “CNTK expresses (nearly) arbitrary neural networks by composing simple building
blocks into complex computational networks, supporting relevant network types
and applications.”
h1 = sigmoid (x @ W1 + b1)
h2 = sigmoid (h1 @ W2 + b2)
P = softmax (h2 @ Wout + bout)
ce = cross_entropy (P, y)
•
+
s
•
+
s
•
+
softmax
W1
b1
W2
b2
Wout
bout
cross_entropy
h1
h2
P
x y
ce

54. Theano only supports 1 GPU
Achieved with 1-bit gradient quantization
algorithm
0
10000
20000
30000
40000
50000
60000
70000
80000
CNTK Theano TensorFlow Torch 7 Caffe
speed comparison (samples/second), higher = better
[note: December 2015]
1 GPU 1 x 4 GPUs 2 x 4 GPUs (8 GPUs)

55. http://nvidianews.nvidia.com/news/nvidia-and-microsoft-accelerate-ai-together
“Every industry has awoken to the potential of
AI”
Jen-Hsun Huang
founder and CEO
NVIDIA
“By working closely with NVIDIA and
harnessing the power of GPU-accelerated
systems, we've made Cognitive Toolkit and
Microsoft Azure the fastest, most versatile
AI platform.”
Harry Shum
EVP of AI and Research
Microsoft

58. Microsoft Cognitive
Toolkit Demo
Jupyter Notebook on Azure Notebooks
https://notebooks.azure.com/library/cntkbeta2

61. https://github.com/Microsoft/CNTK
https://github.com/Microsoft/CNTK/wiki
https://github.com/Microsoft/CNTK/issues

62. https://github.com/Microsoft/CNTK
https://notebooks.azure.com/library/cntkbeta2

63. Microsoft Cognitive Services

64. Microsoft
Cognitive
Services
Give your apps
a human side

65. Roll your own with REST APIs
Simple to add: just a few lines of
code required
Integrate into the language and
platform of your choice
Breadth of offerings helps you find the
right API for your app
Built by experts in their field from
Microsoft Research, Bing, and Azure
Machine Learning
Quality documentation, sample
code, and community support
Easy Flexible Tested
GET A
KEY

68. How do I use them?
{
"tags": [
{ "name": "outdoor",
"score": 0.976 },
{ "name": "bird",
"score": 0.95 } ],
"description":
{ "tags":
[ "outdoor", "bird" ],
"captions": [
{ "text”: “partridge
in a pear tree”,
“confidence”: 0.96 }
]
}
}

71. Speech

74. Language

75. Knowledge

76. Azure Cognitive Services
Language
Speech
Vision
Search
Knowledge

77. Azure Bot Service

79. Thank you!