Module 1
AI Disruptions
& Introduction to AI
AI and Society
Prof Yu Chien Siang
yucs@outlook.com
This module exposes the student on background knowhow to understand how AI will
impact Society, both positively and negatively. Its practical value is to teach leadership
and confidence, motivating and enhancing the student’s value when he enters the job
market; e.g. arm him with the skills to lead or contribute towards AI automation
projects, which are fundamentally different from standard IT projects, as AI systems tend
to be both highly diverse and complex.
Skills for
Future Jobs
Most AI Deep Learning and data science projects fail, despite promising test results and
clear problem statements at the start. This is because many users may start off with
unrealistic expectations due to an inadequate understanding of AI; what it can and
cannot do, and business leaders may fail to understand the privacy, ethics, cyber security
and governance issues related to this new disruptive science. Additionally, there may not
be enough high quality data available, and users do not understand the importance of
garbage in, garbage out.
Therefore, a key aim of this module is to provide a business deployment and societal
perspective of foundational Deep Learning AI capabilities, ranging from Natural
Language Processing to Machine Vision; in a manner that requires little or no coding.
Neural networks and how Deep Learning works, the data input requirements, project
lifecycle and the development framework would be introduced.
What problems that each specific AI algorithm can solve would be illustrated via virtual lab
workshop sessions and the enterprise benefits would be highlighted via case studies; e.g.
enhanced productivity for corporate decision making, process automation or state-of-art
unattended (autonomous) systems.
The focus is on rapid problem understanding and to explore AI project viability for real
world problem solving. Importantly, the student will also gain insights to discuss AI
regulatory compliance, privacy and cyber security requirements at the design phase.
Skills for
Future Jobs
Finally, modern AI governance challenges such as ensuring man-in-the-loop AI oversight,
cyber security hardening against adversarial AI attacks and privacy obligations would be
covered as new requirements for the digital economy.
The future of AI continues to shine brightly, and we aim to highlight major high value
digitalisation and enterprise automation possibilities as AI and Big Data systems converge
pervasively, and 5G and IoT AI at the edge become commodity.
With these insights, the student will master multidisciplinary innovation and thinking skills
that will be the main drivers to deliver AI strategic benefits to the corporation.
Applied Learning Objectives
Very up-to-date, inspirational, valuable, broad and deep coverage of AI
Confidence boosting, get used to learning complicated things, be street smart, non-stop
•
Big Picture Section (minus coding and the mathematical details)
• Architecture and History
• Appreciate the value and power of the knowledge gained
• Usage examples
• Innovation and opportunities
• Learn also the negatives really well
•
Engineering Section (modular, key components, problem solving)
• Learn how AI works, how to get problems solved and projects done via coding ideas.
• Rapid Prototyping, Agile Mindset
• Toolset empowerment – learn an idea, correlate it to the Big Picture and be able to exploit
what has been learnt to solve problems immediately by exploiting codeless/code lite
production and code simulation using Colab/Juypter Notebook
“AI has the potential to change my life
for the better, agree 71%.” - Gemalto
Applied Learning Pedagogy
Multi-modal Learning, self driven learning
- Extensive use of animation
- Video sequences and multimedia
- Instant gratification via virtual lab exercises, facilitated by experimental tuning
Team-based Learning
- Modified for remote learning (to cope with Covid)
Reinforcement Techniques
- 7 repeats (Learning the same thing 7 times but with different points of view)
- Try and then see what happens (open lab experimentation)
Life Long Learning and Skills for Thinking
• The cognitive development objective is to guide the students to
develop their own "compass" for learning and achievements, be
critical about existing systems and norms as these may be disrupted
quickly.
• Knowing important AI issues in depth helps to nurture our students to
be deep thinkers.
• Think innovation all the time!
Lessons and Test Plan
Lectures 13 of about 3 hrs each
• Week 1 – Introduction to AI and Smart Nation Society
• Week 2 – Usage and Models (Lab 1)
• Week 3 – Natural Language Processing (NLP) Use Cases (Lab 2)
• Week 4 – Natural Language Processing (NLP) Smart Factory
Quiz1 (for 1 and 2) 30 MCQ questions
• Week 5 – AI Vision Applications (Lab 3)
• Week 6 – AI Vision Embedded Systems
Mid-course Test, 40 MCQ Questions
• Week 7 – IoT, Robots and Drones, RPA (Lab 4)
• Week 8 – Economics, AI Project Management and Leadership
• Week 9 – AI Governance and Compliance
• Week 10 – AI Ethics, Legislation and Politics
Quiz2 (for 3 and 4) 30 MCQ Questions
• Week 11 – AI and Cyber Security (Lab 5)
• Week 12 –The Future of AI
• Week 13 – AI and Education
End of Course Final Test, 60 MCQ Questions
eLab Sessions
Five lab sessions will be conducted on related
weeks. The objective of these exercises will be to
further elaborate on the practical aspects via
hands-on practice using the concepts introduced
during the lectures.
• Lab 1: Understanding Neural networks CNN
• Lab 2: NLP Applications
• Lab 3: Machine Vision Applications
• Lab 4: Robotic Process Automation (RPA)
• Lab 5: AI Governance and Cyber Security
Overseas Example:
A powerful collaboration: MIT Sloan and MIT CSAIL
Artificial Intelligence: Implications for Business Strategy (self-paced
online)
Certificate Track: Management and Leadership
Location: Online, 6 weeks
Tuition: US$3,200
Program Days (for ACE Credit) 2
Assessments
All the MCQ tests are open book
• Team Paper with a video
•
•
Team paper
Video
30%
20%
10%
• Individual Powerpoint
10%
• Individual Quiz/Assessment
30%
• Lab Quiz1 MCQ 30 Questions – End of Week 4 (first 3 lectures plus Lab 1 and 2) – 5%
• Test2 Midterm MCQ 40 Questions – End of Week 6 (1-6 lectures)
– 20%
• Lab Quiz2 MCQ 30 Questions – End of Week 10 (7-9 lectures plus Lab 3 and 4) – 5%
• Final Test/Exam
30%
• MCQ 60 Questions (coverage of the entire course plus Lab5)
Team Project Assessments
• Team Paper with a video
• Topic of paper to be about AI implications or opportunities for the future society. Students are required to
work on a group project with a written report (20 A4 pages) plus a video summary (8 minutes).
• Font size for Text to be 11 (e.g. Times Roman) and Headings to be font size 12.
• Margins - minimum 2.5 cm, maximum 3 cm all around.
• Single line spacing.
• Group project outcomes should depict thoughtful discussion and debate on how best to operationalize
new AI opportunities cum its impact to the new digital economy and to also study potential challenges.
The video presentation should show original thinking, creativity and communication skills.
• Individual Powerpoint
• Each team member to produce his own individual powerpoint (20 slides) to show active participation.
• Rubric for Team Project Paper Marking
•
•
•
•
•
Holistic Content (well organised, comprehensive, logically structured)
Innovation (novel, interesting, flexible design)
Critical Thinking (compared pros and cons, handle negatives and possible issues)
Operacy Thinking (how practical, how relevant to real world problem solving and economics)
Paper, video and powerpoints to be submitted by end of week 13
- 40%
- 20%
- 20%
- 20%
Possible Team Projects (some examples)
• Non-Technical Papers
•
•
•
•
•
The impact of AI on the economy
Jobs Changes and AI, or The Future of Jobs as impacted by AI
How would AI Ethics impact enterprise governance
Is AI security a really serious problem?
Why must we care about AI implications to Smart Nation?
• More-technical papers
•
•
•
•
•
Future of AI and AGI
How would AI impact office IT and boost workers’ productivity?
Chatbots and AI
AI Security, or AI and its Operational Safety
What is XAI and why is this important?
Overview of Google Colab as Lab Platform
• Free cloud service hosted by Google to encourage Machine Learning and
Artificial Intelligence.
• Options to choose to run CPU,GPU or TPU
• Utilises the Jupyter notebook environment to execute your code
• Allows simultaneous access by different team members for editing
• Supports many popular machine learning libraries which can be loaded easily in
the notebook
• Most importantly, you will need to get a Google account.
• Give it a try. Start off with
https://www.tensorflow.org/tutorials/quickstart/beginner
• Click on… Google Colaboratory notebook file.
Colab Usage Guide: Resources
• https://www.geeksforgeeks.org/how-to-use-google-colab
• https://analyticsindiamag.com/a-beginners-guide-to-using-googlecolab/
• https://www.tutorialspoint.com/google colab/google colab quick g
uide.htm
• https://www.analyticsvidhya.com/blog/2020/03/google-colabmachine-learning-deep-learning/
• https://towardsdatascience.com/10-tips-for-a-better-google-colabexperience-33f8fe721b82
Important things to note
1. Mounting your google drive to Colab
2. Activate GPU or TPU
3. Run bash commands
4. Stop Colab from disconnecting during training
5. Installing libraries
6. Upload files
7. Other accounts needed
1. Mounting your google drive to Colab
• This function is useful for getting access to the files in google drive and saving the data
and models once the training is down
• The code for it is:
• from google.colab import drive
drive.mount('/content/gdrive’)
• After running the code, a link will be given in the output and redirected to the
permission page for the authentication code that is to be entered in the output
Redirected permission page
1. Mounting your google drive to Colab
• Once you have allowed permission, a verification code will be generated and you have
to copy the code into the textbox given in the output
1. Mounting your google drive to Colab
• Once you have entered the verification code, you should receive an output:
• Mounted at /content/drive
2. Activate GPU or TPU
• The default hardware used in Google Colab is CPU
• You must enable either GPU or TPU for more computationally demanding tasks such as machine
learning
• Click on: “Runtime” → “Change runtime type” → “Hardware accelerator”. Then select the desired
hardware.
3. Run bash commands
• Google Colab allows users to run terminal codes
• Bash commands can be run by prefixing the command with “!”
• Examples:
• Download dataset from the web with !wget <ENTER URL>
• Install libraries with !pip install <LIBRARY> or !apt-get install <LIBRARY>
• Run an existing .py script with !python script.py
• Make sure that you are in the correct directory before executing
• %cd <file path>
• Clone a git repository with !git clone <REPOSITORY URL>
4. Stop Colab from disconnecting during training
• This is important especially if you are doing model training
• Google Colab will automatically disconnect after running a task for 12 hours or idle for
more than 30 minutes
• Open your Chrome DevTools by pressing F12 or ctrl+shift+i on Linux and enter the
following JavaScript snippet in your console:
• function KeepClicking(){
console.log("Clicking");
document.querySelector("colab-connect-button").click()
}
setInterval(KeepClicking,60000)
• This function makes a click on the connection-button every 60 seconds to ensure that
the notebook is not idle and model training will continue until the end
Usage of the JavaScript code
5. Installing libraries
• Although most of the commonly used python libraries are preinstalled, there will be some missing libraries required
• Code:
• !pip install [package name] OR
• !apt-get install [package name]
• Place the code in a cell and execute it
6. Upload files
• https://colab.research.google.com/notebook
s/io.ipynb
• On the left side of the GUI, you will a file
folder that you can click on it. It will show an
explorer view of the file directories at your
colab site. Do a mouse click at 3 dots icon, for
options to create a new directory, delete files
or folder or “upload” a file.
• If you have python code, you could cut the
entire text out and paste it into a Juypter cell.
7. Other accounts that could be useful
• Get a Kaggle account, to get access to data and tutorials.
• Kaggle.com
• https://www.youtube.com/watch?v=GJBOMWpLpTQ Join competition
• https://www.kaggle.com/learn/overview?utm medium=youtube&utm source=c
hannel&utm campaign=yt-learn free courses
• Got a Github account to get access to codes/Juypter notebooks.
• Go to https://github.com/join
• Type a user name, your email address, and a password.
• Choose Sign up for GitHub, and then follow the instructions.
Module 1:
Introduction to AI (Deep Learning) and AI-driven
disruptions?
• Why is Deep Learning/AI revolutionary?
• Explaining how Deep Neural Networks (DNN) works.
• Compare Deep Learning (DL) with Machine Learning (ML).
• Digitalisation and the 4th Industrial revolution.
• How are jobs affected?
Deep Learning (deep neural network)
• https://en.wikipedia.org/wiki/Deep learning
• Family of machine learning methods based on learning representations of data.
• An observation (e.g., an image) can be represented in many ways such as a vector
of intensity values per pixel, or in a more abstract way as a set of edges, regions of
particular shape, etc..
• Some representations make it easier to learn tasks (e.g., face recognition or facial
expression recognition) from examples.
• Replacing handcrafted features with efficient algorithms for unsupervised or semisupervised feature learning and hierarchical feature extraction.
• http://www.datarobot.com/blog/a-primer-on-deep-learning/
• Youtube:
https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYjzLfQRF3EO8sYv Stanford 2017 Lecture 1 | Introduction to Convolutional
Neural Networks for Visual Recognition
Who’s who in Deep Learning
• Mar 2013, Geoffrey Hinton and two of his graduate students, Alex
Krizhevsky and Ilya Sutskever, were hired by Google. (bought
DNNResearch)
• Dec 2013, Facebook hired Yann LeCun to head its AI Lab.
• 2014 Google also acquired DeepMind Technologies, a British start-up
that developed a system capable of learning how to play Atari video
games using only raw pixels as data input.
• 2014, Baidu hired Andrew Ng (ex-Stanford) and Adams Coates to head
their new Silicon Valley based research lab. He started Coursera. Left
Baidu.
• Deep Speech Engine (noisy environment) for voice commands, GPU
Difference between ML (statistical) vs DL
• https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificialintelligence-machine-learning-deep-learning-ai/
• Machine Learning at its most basic is the practice of using algorithms to
parse data, learn from it, and then make a determination or prediction about
something in the world. So rather than hand-coding software routines with a
specific set of instructions to accomplish a particular task, the machine is
“trained” using large amounts of data and algorithms that give it the ability
to learn how to perform the task.
• Machine learning came directly from minds of the early AI crowd, and the
algorithmic approaches over the years included decision tree learning,
inductive logic programming. clustering, reinforcement learning, and
Bayesian networks among others. As we know, none achieved the ultimate
goal of General AI, and even Narrow AI was mostly out of reach with early
machine learning approaches.
Difference between ML (statistical) vs DL
• https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificialintelligence-machine-learning-deep-learning-ai/
• As it turned out, one of the very best application areas for machine learning
for many years was computer vision, though it still required a great deal of
hand-coding to get the job done. People would go in and write hand-coded
classifiers like edge detection filters so the program could identify where an
object started and stopped; shape detection to determine if it had eight
sides; a classifier to recognize the letters “S-T-O-P.” From all those handcoded classifiers they would develop algorithms to make sense of the image
and “learn” to determine whether it was a stop sign.
• Good, but not mind-bendingly great. Especially on a foggy day when the sign
isn’t perfectly visible, or a tree obscures part of it. There’s a reason computer
vision and image detection didn’t come close to rivaling humans until very
recently, it was too brittle and too prone to error.
Possible H(v)
And after
Digital Human
Example of DeepFakes
Exciting Demos
• GPT3
• https://research.aimultiple.com/gpt/ (several movie demos here)
• More:
• https://www.entrepreneur.com/article/368843
• https://www.theguardian.com/commentisfree/2020/sep/08/robot-wrotethis-article-gpt-3
An introduction to Reinforcement Learning
• https://www.youtube.com/watch?v=JgvyzIkgxF0
• Video – 16 min 26 sec
Business Models are being disrupted
• Business Models are changing.
•
•
•
•
•
Banks going to the cloud
CloudATM
Taxi electronics get obsoleted by mobile app
eSIM coming
Paperless
59
Disruptions rocking the world of IT
• Kodak was the leader in producing film negatives. In 2013, it
went bankrupt. The company invented digital cameras. The
pace of change was faster than expected. Interestingly,
Fujitsu survived.
• Disruptions, challenging IT today:
• Cloud First (SIA)
• Fintech /Blockchain
• National Digital Identity (Smart Nation), mobile phone is the
central enabler!
• IoT and 4th Industrial Revolution
• Connected Cars, Robots and Drones
• Big Data, AI (embedded AI)
• Quantum Computing
• Facebook saga, GDPR, regulations on fake news
60
Late 2017, McKinsey Global Institute Report
• https://insights.dice.com/2018/10/03/4-tech-jobs-risk-automation-artificialintelligence/
• Automation will leave many technology jobs relatively untouched.
“Workers of the future will spend more time on activities that machines
are less capable of, such as managing people, applying expertise, and
communicating with others,” that report read. “The skills and
capabilities required will also shift, requiring more social and emotional
skills and more advanced cognitive capabilities, such as logical reasoning
and creativity.”
• Other studies have positioned automation as helping tech pros perform
their jobs more effectively. For example, one study from Puppet
demonstrated that automated tools had taken over roughly a third of
testing and configuration-management duties from DevOps specialists,
freeing them to pursue more proactive tasks.
Late 2017, McKinsey Global Institute Report
• But automation won’t prove a good thing for every tech
pro. Many companies will look at these labour-saving
tools and decide they can get by with smaller teams of
technologists.
• While high performers will benefit from automation, it
stands to reason that not everyone will survive when
new, more sophisticated platforms come online. With
that in mind, here are four tech jobs most at risk from
automation and A.I. The following will be negatively
affected:
•
•
•
•
Datacenter Administrators
Help Desk Staff
Programmers
Data Analysts
Andrew Ng’s words of wisdom
• Much has been written about AI’s potential to reflect
both the best and the worst of humanity. For example,
we have seen AI providing conversation and comfort to
the lonely; we have also seen AI engaging in racial
discrimination.
• Yet the biggest harm that AI is likely to do to individuals
in the short term is job displacement, as the amount of
work we can automate with AI is vastly bigger than
before.
• As leaders, it is incumbent on all of us to make sure we
are building a world in which every individual has an
opportunity to thrive. Understanding what AI can do
and how it fits into your strategy is the beginning, not
the end, of that process.
Conclusion
• Massive job loss is not immediate.
• When will it start… already started, and will accelerate quickly.
• Covid crisis will pressure all companies to go digital much faster, e.g. cosmetics
manufacturing, schools, banking services.
• Is it old wine in new bottle; like global warming, globalisation etc, that we have
experienced before?
• How will AI make its greatest impact?
• Changes to world order, nature of our economy, how we will live, our hopes and aspiration
for the future etc.
• Who will benefit most?
• The various Internet giants
• You! As you have seen the future.
• What else is important?
• Cyber and data security
Reading (Reinforcement Learning):
• View this Rajiv production for reinforcement learning (2017), Deep
Q Learning for Video Games - The Math of Intelligence
#9, 12 Aug 2017, 9 min https://www.youtube.com/watch?v=79pmNdyxEGo
• We're going to replicate DeepMind's Deep Q Learning algorithm for Super Mario
Bros! This bot will be able to play a bunch of different video games by using
reinforcement learning. This is the first video in this series that uses libraries
(Keras & Gym) because if it didn't, the code would be way too long for a short
video. I'll make a longer, in-depth version without libraries soon. Code for this video:
https://github.com/llSourcell/deep_q_...