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
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
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.
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
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
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)
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
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
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
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
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
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
中國人工智能大會 CCAI (China Conference on Artificial Intelligence): http://ccai.caai.cn/
百度世界大會: http://baiduworld.baidu.com/
NIPS (Conference on Neural Information Processing Systems): https://nips.cc/
GTC Taiwan (GPU Technology Conference): https://www.gputechconf.com.tw/
Bay Area Deep Learning School: http://www.bayareadlschool.org/
What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
The 7 Myths of AI
http://www.datasciencecentral.com/profiles/blogs/the-7-myths-of-ai-by-robin-bordoli
http://www.gartner.com/newsroom/id/3412017
Gartner’s Top 10 Strategic Technology Trends for 2017
Artificial intelligence, machine learning, and smart things promise an intelligent future. (October 18, 2016)
http://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017/
Gartner:2017 年十大策略科技趨勢預測
https://buzzorange.com/techorange/2016/10/25/gartner-2017-tech/
TIOBE Index for December 2016
http://www.tiobe.com/tiobe-index/
R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results
http://www.kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html
R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results
http://www.kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html
Top 20 Python Machine Learning Open Source Projects
http://www.kdnuggets.com/2016/11/top-20-python-machine-learning-open-source-updated.html
Comparison of deep learning software
https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software
Microsoft Cognitive Services
https://www.microsoft.com/cognitive-services/en-us/
Microsoft Cognitive Services: Introducing the Seeing AI project
http://bit.ly/2i8JOgc
https://www.luis.ai/
清潔工到斯坦福,人工智能科學家李飛飛的逆襲之路
http://bit.ly/2gDyCG7
Giant Corporations Are Hoarding the World’s AI Talent (2016/11/17)
https://www.wired.com/2016/11/giant-corporations-hoarding-worlds-ai-talent/
如何評價李飛飛和李佳加盟谷歌?看看AI 達人怎麼說
http://bangqu.com/gpu/blog/5058
A.I. is too hard for programmers
http://www.computerworld.com/article/2928992/emerging-technology/a-i-is-too-hard-for-programmers.html
What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
How we teach computers to understand pictures | Fei Fei Li
https://youtu.be/40riCqvRoMs
ImageNet
http://image-net.org/index
Source: https://www.52ml.net/wp-content/uploads/2016/08/imagenethistory.png
Large Scale Visual Recognition Challenge (ILSVRC)
Microsoft Researchers’ Algorithm Sets ImageNet Challenge Milestone (2015/2/10)
https://www.microsoft.com/en-us/research/blog/microsoft-researchers-algorithm-sets-imagenet-challenge-milestone/
The State of Artificial Intelligence in 15 Visuals (2016/6/16)
http://www.appcessories.co.uk/artificial-intelligence/
Machine Learning
NLP
Computer Vision
VPA
Speech Recognition
Smart Robots
Recommendation Engine
Gesture Control
Content Aware Computing
Speech to Speech Translation
Video Content Recognition
Emerging from Y2012
Hot in China
“Deep Learning” Google Trends: https://www.google.com/trends/explore?date=all&geo=US&q=deep%20learning
Scimago Journal & Country Rank
http://www.scimagojr.com/countryrank.php?category=1702
在人工智慧研究領域 美國與中國領先各國
http://iknow.stpi.narl.org.tw/Post/Read.aspx?PostID=13039
US, China most active in AI research, report finds (2016/12/9)
http://asia.nikkei.com/Tech-Science/Science/US-China-most-active-in-AI-research-report-finds
人工智慧經濟席捲全球
http://udn.com/news/story/6860/2100235
Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017
http://bit.ly/2i2hrBj
AI Has Beaten Humans at Lip-Reading
http://bit.ly/2fMLeMw
Intel Core i7 6700HQ CPU (8 cores)
NVIDIA GeForce GTX-1060 video card (6GB RAM)
CUDA 8.0
TensorFlow
FPGA: Field Programmable Gate Array
TPU: Tensor Processing Unit
Does the future lie with CPU+GPU or CPU+FPGA?
https://www.scientific-computing.com/news/analysis-opinion/does-future-lie-cpugpu-or-cpufpga
Comparison of deep learning software
https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software
AI Winter Isn’t Coming (2016/12/7)
http://bit.ly/2hepl70
罗辑思维"时间的朋友2016"跨年演讲 04 智能革命
http://bit.ly/2iBtyD0
強人工智慧 (Strong AI / Artificial General Intelligence)
弱人工智慧 (Weak AI / Applied AI)
Google uses DeepMind AI to cut data center energy bills
http://www.theverge.com/2016/7/21/12246258/google-deepmind-ai-data-center-cooling
百度世界大會2016
http://baiduworld.baidu.com/
How a Japanese cucumber farmer is using deep learning and TensorFlow
http://bit.ly/2i8d06S
AI for Hobbyists: DIYers Use Deep Learning to Shoo Cats, Harass Ants
http://bit.ly/2hCM9O4
Chasing Cats
http://myplace.frontier.com/~r.bond/cats/cats.htm
Google’s AI assistant has 5 New Year’s resolutions for you
http://bit.ly/2iBzaNM
罗辑思维"时间的朋友2016"跨年演讲 04 智能革命
http://bit.ly/2iBtyD0
Where machines could replace humans—and where they can’t (yet) (2016/7)
http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet
Japanese white-collar workers are already being replaced by artificial intelligence
http://bit.ly/2hNHZ8d
Cybersecurity trends 2017: malicious machine learning, state-sponsored attacks, ransomware and malware
http://www.cso.com.au/article/612128/cybersecurity-trends-2017-malicious-machine-learning-state-sponsored-attacks-ransomware-malware/
2017 Predictions for AI, Big Data, IoT, Cybersecurity, and Jobs from Senior Tech Executives
http://blog.level3.com/transformation/2017-predictions-ai-big-data-iot-cybersecurity-jobs-senior-tech-executives/
防火墙做不到的事,人工智能可以吗?
http://app.fortunechina.com/mobile/article/276577.htm