1. Artificial Intelligence – Deep Learning and its Applications
人工智能 – 深度學習及其應用
Ir Dr F C Chan
Past President
The Hong Kong Institution of Engineers
10 May 2018

2. Artificial Intelligence – Deep Learning and its applications
AlphaGo
Artificial Intelligence Development
Deep Learning
Features Extraction
Applications
Conclusion

3. Vs Lee Sedol AlphaGo Lee AlphaGo won by 4-1 9 and 15 March 2016 李世石
黃士傑
AlphaGo won by 4-1
AlphaGo
A combination of neural network and algorithm
9 and 15 March 2016

4. Vs Ke Jie AlphaGo Master Google hosted in Wuzhen in China
23-27 May 2017 Vs
Ke Jie
中國棋王柯潔
AlphaGo won by 3-0
Google has announced that the research team behind AlphaGo would now focus their energy into tackling more complex issues and develop highly advanced algorithms to tackle some of the most complex problems in the world.

5. AlphaGo Zero
Elo ratings - a measure of the relative skill levels of players in competitive games such as Go - show how AlphaGo has become progressively stronger during its development

6. AlphaGo Zero Vs Deep Blue
Deep Blue(IBM) beat Word Chess Champion
Garry Kasparov in 1997
Won 3.5 vs 2.5 in a match of 6-game
Go Vs Chess
Bigger board, 19x19 vs 8x8
Simpler in rules, more move possibilities,
361 vs 28
Longer game, 150 vs 80 moves
Alpha Go vs Deep Blue
Deep Blue can only play chess
Alpha Go is general purpose, can win 49 different arcade games
AlphaGo Zero
No Built-in expert knowledge
Creativity and Intuitive insights
Deep Learning can revolutionize everything

7. AI Development
Artificial Intelligence (1950s) – Giving intelligence to machine
Machine Learning (1980s) – realizing artificial intelligence (speech recognition, image recognition, playing go, dialogue)
Deep Learning (2006) – for machine learning for higher prediction accuracy
A powerful class of machine learning model
Modern reincarnation of artificial neural network
Collection of simple, trainable mathematical functions

8. AI Development 1958: Rosenblatt’s Perceptron algorithm
1969: Minksky showed Perceptron could not solve the XOR problem, connectedness, parity.
1986: Rumelhart developed Backpropagtion algorithm to train neural network
Mid 90’s: Cortes and Vapnik published paper on Support Vector Machines
2006: Hinton and Salakhutdinov proposed using Restricted Blotzmann Machine for pre-train Deep Neural Network

9. AI Development
2007: Fei-Fei Li’s ImageNet assembling a databse of 14 million labled images (Data drives learning)
2011: Micorsoft explored Speech recognition and IBM’s Watson
2014: Google acquired DeepMind, combing deep learning and reinforcement learning
2016: DeepMind’s AlphaGo defeated world champion Lee Sedol

10. Deep Learning applications
Google: 2011 launched deep-learning focused project bought DeepMind with AlphaGo.
Microsoft: strong in speech-recognition and translation
Facebook: translate user posts in more than 40 languages
Baidu: for speech recognition, translation, photo search and self-driving car project

11. Perceptron
Human mindset: Axons send signals to other cells while Dendrites receive them.
Perceptron approach

12. Perceptron Activation Function Network Linear Binary Sigmoid
Stochastic Binary

13. Perceptron
Logistic Regression

14. Handling XOR

15. Neutral Network
Weights are the important elements in Neural Network formulation

16. Example: 4- layer network with 2 output units
Neutral Network
Example: 4- layer network with 2 output units

17. Additional Layers of more Abstract
Deep Learning
Rules Based System
Hand
designed
Program
Input
Output
Classic Machine Learning
Hand
designed
Features
Mapping
from
Features
Input
Output
Deep Learning
Additional Layers of more Abstract
Features
Mapping
from
Features
Sample
Features
Input
Output

18. Deep Learning

19. Deep Learning

20. Neutral Network Deep Learning The drive behind Deep Learning
Nonlinear activation function
Neuron could connect to every input
Multi-levels with millions neurons
Any function can be computed
The drive behind Deep Learning
Faster machines and core (CPU/GPU)
Big data (with large dataset)
New models and algorithms
Neural Network: pattern recognition or data classification

21. Deep Learning

22. Deep Learning
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques
Adaptive learning
Self Organization
Fault Tolerance

23. Deep Learning Implementation
Cloud with Big Data
Google, Facebook, Amazon, Baidu, Tencent
PC Implementation
Linux cluster with GPU servers
Python
TensorFlow, Theano, Keras

24. Deep Learning Software

25. Trend Prediction Recognition New Knowledge Making Sense
Artificial Intelligence – Deep Learning and its applications
Trend Prediction
Recognition
New Knowledge
Making Sense
Replacing Human

26. Artificial Intelligence – Deep Learning and its applications
Information retrieval (search engines)
Pattern recognition
Audience targeting
Sentiment analysis (based on written text)
Personalization
Automation
Natural Language Processing
Social media mining
Organic search and content performance
Brand and product differentiation

27. Artificial Intelligence – Deep Learning and its applications
Language Translation
Speech Recognition
Generating Handwriting
Face Recognition
Autonomous Driving
Generating Arts
Imitating Famous Painters
Generating Music
Generating Photos

28. Deep Learning Applications
Power Systems
Peak Load Forecast (e.g. Maximum Demand)
Failure Prediction (e.g. Battery)
Condition Monitoring (e.g. Partial Discharge)
…..

29. Deep Learning Applications
Data Centres
Increase its energy efficiency by 15%
120 different variables (sensors, temperatue gauges, fans, windows…)

30. Conclusion
Artificial Intelligence – Deep Learning will have major impact to our living
Specialized applications in various domains

31. Thank you Artificial Intelligence – Deep Learning and its Applications
Ir Dr F C Chan
Past President
The Hong Kong Institution of Engineers
Thank you