Summary and application of recent technology, artificial intelligence, machine learning, and deep learning research and reports.

1. Technology and AI Sharing – From 2016
to Y2017 and Beyond
James CC Huang

2. 最強大腦 – Human vs. Machine
Source: http://bit.ly/2jqiagE

3. 最強大腦 – Human vs. Machine
Machine (小度) won!Source: http://bit.ly/2jqiagE

4. Share My 2016 Learning Journey
2016台灣資料科學年會
一天搞懂深度學習
Jul 15-17
給工程師的統計學及資料分析 123
Sep 4
資料科學面面觀
Jan 23
AWSome Day Express
Nov 22
AWS 基礎設施服務
實作工作坊
Dec 13
百度
世界大會
Sep 1
NVIDIA GTC
Sep 21-22
Deep Learning
School
Sep 25-26
NIPS
Dec 5-10
CCAI
Aug 26-27

5. Put Things Together

6. Before We Start…
• AL, machine learning, and deep learning are different, but in
the sharing we may not discuss about it.
• Abbreviation:
– AI: Artificial Intelligence
– ML: Machine Learning
– DL: Deep Learning
• A lot of reference URL in the slides. Enjoy!
– Articles / media reports / posts
– Video clips

7. AI > Machine Learning > Deep Learning
Source: http://bit.ly/2h4AfLl

8. Best Short Definition of AI
Source: http://bit.ly/2h4z52B
AI = Training Data + Machine
Learning + Human-in-the-loop

9. Technology Trend

10. Gartner's 2016 Hype Cycle for Emerging
Technologies
* No “Deep Learning”
Source: link

11. Gartner’s
Top 10 Strategic
Technology Trends
for 2017
Source: link, link

12. Top 10 Strategic Tech Trends - Intelligent
AI & Advanced Machine Learning
• AI, machine learning, deep learning, neural networks, natural language processing (NLP)
• Parallel processing power + advanced algorithms + massive datasets
• Real-time analytics
Intelligent Apps
• Virtual personal assistants (VPAs)
• Existing application with AI capabilities enabled.
• 3 focus areas: advanced analytics, AI-powered and increasingly autonomous business
processes and AI-powered immersive, conversational and continuous interfaces.
Intelligent Things
• Robots, drones, and autonomous vehicles.

13. Top 10 Strategic Tech Trends - Digital
Virtual & Augmented Reality
• Training scenarios and remote experiences.
• Enterprises should look for targeted applications of VR and AR through 2020.
Digital Twin
• Dynamic software model + sensors
• Users collaborate with data scientists and IT/BA professionals.
Blockchain
• Bitcoin
• FinTech

14. Top 10 Strategic Tech Trends - Mesh
Conversational Systems
• Communicate across the digital device mesh (e.g., sensors, appliances, IoT systems) using text / voice / sight / sound /
tactile.
Mesh App and Service Architecture (MASA)
• Flexible enough to allow rapid evolution of user needs and how they interact with technology.
• Apps connect and communicate and with other apps using agile architecture with, for example, HTTP/REST JSON.
Digital Technology Platforms
• Information systems, customer experience, analytics and intelligence, IoT and business ecosystems.
• New platforms and services for IoT, AI and conversational systems will be a key focus through 2020.
Adaptive Security Architecture
• Multilayered security and use of user and entity behavior analytics will become a requirement for virtually every
enterprise.
• Security in the IoT environment

15. With data, advanced AI, and computing
power, everything will be “more”
intelligent.

16. Programming Language and Tool
Ranking
FOCUSING ON DATA SCIENCE AND AI / MACHINE LEARNING / DEEP LEARNING

17. Top Programming Language - TIOBE
#30 T-SQL
Source: link

18. Top Programming Language - KDnuggets
Source: link

19. Top Data Science Tools - KDnuggets
Source: link

20. Top 20 Python ML Open Source Project
Top projects are ML, DL
Projects on GitHub. A lot
of them are new in top 20
in Y2016.
Source: link

21. DL Software w/ Default Support for AWS and Python
Software Platform Interface GPU
Support
Recurrent
nets
Convolution
al nets
RBM/DBNs
Parallel
execution
Caffe
Linux, Mac OS X, AWS,
Windows support by
Microsoft Research
C++, command
line, Python, MATLAB
Yes Yes Yes No Yes
Deeplearning4j
Linux, Mac OS
X, Windows, Android (Cross-
platform)
Java, Scala, Clojure Yes Yes Yes Yes Yes
Keras
Linux, Mac OS X, Windows
Python
Yes Yes Yes Yes Yes
Microsoft Cognitive
Toolkit - CNTK
Windows, Linux (OSX via
Docker on roadmap)
Python, C++, Command line,
BrainScript (.NET on roadmap)
Yes Yes Yes No Yes
MXNet
Linux, Mac OS X, Windows,
AWS, Android,
iOS, JavaScript
C++, Python, Julia, Matlab, JavaSc
ript, Go, R, Scala
Yes Yes Yes Yes Yes
PaddlePaddle Linux, Mac OS X Python, C++ Yes Yes Yes ? Yes
TensorFlow
Linux, Mac OS X, Windows
Python, (C/C++ public API only for
executing graphs)
Yes Yes Yes Yes Yes
Theano Cross-platform Python Yes Yes Yes Yes Yes
Torch
Linux, Mac OS X, Windows,
Android, iOS
Lua, LuaJIT, C, utility library
for C++/OpenCL
Yes Yes Yes Yes Yes
Source: link

22. Evaluate
Which is the best programming language to data / AI / ML /
DL?
How to select deep learning software?
On-premise or cloud / API platform?

23. Use Case:
Eva can get current product customer
account on Facebook Messenger chatbot
using natural language query and voice
command.

24. Microsoft Cognitive Services

25. [Video] Microsoft Cognitive Services:
Introducing the Seeing AI project
http://bit.ly/2i8JOgc

26. LUIS
https://www.luis.ai/

27. Artificial Intelligence, Machine
Learning, Deep Learning

28. 中國大陸人稱
“女神”
http://bit.ly/2gDyCG7
清潔工到斯坦福，人工智能科學家李飛飛的逆
襲之路

29. AI Talent Wars / Acquisition
• Giant corporations are soaking up AI talent.
• Top AI researchers -> industry with humongous data.
• “The cost of acquiring a top AI researcher is comparable to
the cost of acquiring an NFL quarterback.”
• AI talent shortage.
Source: link, link

31. Academic researcher -> Humongous data
and computing power

32. For AI talent, hire from outside, or train
and transit our developers for AI-powered
projects?

33. Gap for the Transition
• Academic background
• Differences between computer program and brain (AI tries
to simulate brain)
– Computer program: define the general to store specifics
– Brain: store the specific to identify the general
Source: link

34. Rise of Current AI (Not Long Time Ago)

35. AI > Machine Learning > Deep Learning
Source: http://bit.ly/2h4AfLl

36. One of the Biggest Crowdsourcing Project
– Started in Y2007
– On Amazon Mechanical Turk Marketplace
• 48,940 workers
• 167 countries
– Total number of images: 14,197,122 (as of 2010/4/30)

37. ImageNet Challenge
ILSVRC’16 winner:
Error rate 2.991%
* Human-level performance: 5.1%

38. 2016: The Year That Deep Learning
Took Over

39. The State of AI and Focus
Source: link

40. Notable AI Events in 2016 (by China)
Source: link

41. Google Trends - Deep Learning

42. Published AI Documents by Country
(Y2015, Top 10)
* Taiwan ranked #11.Source: link, link

43. Main Developments in 2016
(From Top AI Researchers)
Reinforcement
Learning
Inhuman
Encryption
GAN NLP
Machine
Translation
Lip Reading
Speech
Recognition
WaveNet
Computer
Vision
Hype
Source: link

44. AI Category Innovation Quadrant
Source: http://bit.ly/2h4FA5CSource: link

45. DL dominates now
(and is still growing fast).
* DL is not equal to AI / ML.

46. Neural Networks Zoo
Most talked: CNN (?)
Hot: RNN (?)
Uprising: GAN (?)
Source: link

47. Rule of Thumb (Mostly from Andrew Ng)
Why
• Add value to our business.
When
• “If a typical person can do a mental task with less than one second of thought, we can probably automate it
using AI either now or in the near future.”
What
• A large amount of data.
How
• Choose tool(s) and “customize to our business context and data.”
Evaluation
• If AI error rate surpasses human-level performance.
Source: link

48. Predictions for AI in 2017

49. 5 Big Predictions for AI in 2017 (MIT Press)
Positive reinforcement
•Reinforcement Learning
•AlphaGo -> Master -> ?
Dueling neural networks
•GAN (Generative Adversarial Networks)
•Learn from unlabeled data
China’s AI boom
Language learning
•NLP
•Image caption -> description
Backlash to the hype
Source: link

50. Key Trends in 2017 (From Top AI Researchers)
NLP
Unsupervised
Learning
Deep Learning in
Healthcare
Chatbot
Self-driving Car Computer Vision
Hybrid deep
learning with other
ML/AI techniques
AutoML
Commodify Deep
Learning
Source: link

51. High Performance Computing (HPC)
BOOST AI / ML / DL

52. In the race to build the best AI, there’s already
one clear winner
中國大陸人稱
“皮衣教主”
Source: link

53. GTC 2016 (GPU Technology Conference)
AI Revolution
GPU Supercomputer & Acceleration for Data Center
Computer Vision, VR
AI City by Y2020 (1B+ Cameras)
Self-Driving Car
AI Computing Ecosystem
Source: link

54. NVIDIA BB8 AI Car
Source: link

55. NVIDIA DGX-1 vs. Supercomputers
Unit: teraflops
0.03325
170
93,000
130000
0 20000 40000 60000 80000 100000 120000 140000
Intel Core i7-6700HQ
DGX-1
China (神威·太湖之光)
Japan (Future)
* DGX-1 list price: US$ 130,000

56. [Video] GPU vs. CPU on training MNIST dataset

57. HPC Competition (On-going)
CPU + GPU?
CPU + FPGA?
Tailored Processor?

58. HPC Competition (On-going)
• GPU is current leader.
• Major cloud computing platforms support both GPU and
FPGA, e.g.

59. Major DL Software Supports GPU Acceleration
Software Platform Interface GPU
Support
Recurrent
nets
Convolution
al nets
RBM/DBNs
Parallel
execution
Caffe
Linux, Mac OS X, AWS,
Windows support by
Microsoft Research
C++, command
line, Python, MATLAB
Yes Yes Yes No Yes
Deeplearning4j
Linux, Mac OS
X, Windows, Android (Cross-
platform)
Java, Scala, Clojure Yes Yes Yes Yes Yes
Keras
Linux, Mac OS X, Windows
Python
Yes Yes Yes Yes Yes
Microsoft Cognitive
Toolkit - CNTK
Windows, Linux (OSX via
Docker on roadmap)
Python, C++, Command line,
BrainScript (.NET on roadmap)
Yes Yes Yes No Yes
MXNet
Linux, Mac OS X, Windows,
AWS, Android,
iOS, JavaScript
C++, Python, Julia, Matlab, JavaSc
ript, Go, R, Scala
Yes Yes Yes Yes Yes
PaddlePaddle Linux, Mac OS X Python, C++ Yes Yes Yes ? Yes
TensorFlow
Linux, Mac OS X, Windows
Python, (C/C++ public API only for
executing graphs)
Yes Yes Yes Yes Yes
Theano Cross-platform Python Yes Yes Yes Yes Yes
Torch
Linux, Mac OS X, Windows,
Android, iOS
Lua, LuaJIT, C, utility library
for C++/OpenCL
Yes Yes Yes Yes Yes
Source: link

60. Future Challenges and
Opportunities

61. 2016: Rise of AI
2017: AI Enabled Things

62. AI Winter? Bubble?
AI is fueled by
• Humongous data (or Big Data)
• Algorithms
• Hardware advances
• Entry barrier lowers (*)
Source: link

63. Hype? Fake AI?

64. Source: link

65. Media
Hype Academic
and Research

66. Ultimate AI?

67. Value-added Business with AI/ML/DL

68. Source: link

69. Use Case: Real-time Answer for TS / Sales
Source: link

70. How to Add Value to OUR Business with AI

71. DIY AI

72. Source: link

73. DIY AI
“Cat Shooting” system
• Cat triggers camera.
• Pop up sprinkler.
• “Water”!
NVIDIA Jetson TX1 list price: US$599
Source: link

74. DIY AI
Source: link

75. Is “Current” AI Smart?
1. Ask Allo “What should be my New Year’s
resolution be?” Ask several times to get
more resolutions.
2. See what you get!
3. Did you get the same answers in the
article?
4. Is this the AI we look forward to?
Source: link

76. Source: link

77. Job Displacement
Source: link

78. Source: link

79. Cybersecurity
• Malicious tool applying AI
• Defense leveraging AI
• Fake data to influence result and output
Source: link, link

80. “Anatomy of the Blockbuster Novel”
• #NLP #MachineLearning
#TextMining
• Topic Modeling
• Sentiment Analysis
• Writing styles
– Frequently used words
– Punctuation
Source: link

81. What’s Next