The document discusses the Fourth Industrial Revolution, which involves emerging technologies like artificial intelligence, big data, robotics, and more. It provides details on the drivers of the Fourth Industrial Revolution, including artificial intelligence, blockchain, big data, the internet of things, and digital innovation. The document also summarizes perspectives from Jack Ma on how to respond to the changes brought by the Fourth Industrial Revolution and the role of machine learning in processing data. Finally, it gives examples of companies that use machine learning like Google, Facebook, and financial institutions.
2. HELLO!
I am Aditya Randika
You can find me at:
https://www.linkedin.com/in/aditya-
randika-1b1b5aa8/
2
3. THE FOURTH INDUSTRIAL
REVOLUTION – KLAUS SCWAB
Fist (1784)
Mechanical
Production,
Railroads, and
Steam Power
Third (1969)
Automated
Production,
Electronics
and Computer
Second (1870)
Mass Production,
Electrical Power,
and the Advent of
the assembly line
3
Forth (Now)
Artificial
Intelligence,
Big Data,
Robotics, and
More to Come
5. THE FOURTH INDUSTRIAL REVOLUTION
DRIVERS
Artificial
Intelligence
• Machine
Learning
• Deep Learning
• Machine
Intelligence
Industrial 4.0
Blockchain
• Linux
Hyperledger
• Ethereum,
based smart
contract
5
Big Data
• Internet of
Things (IOT)
• Web
Scrapper
• Social Media
Data Mining
Internet
• Cyber
Physical
System
• All Device
Connected
• Mobile First
Innovation
• Digitalizing Existed
Business (Uber, GO
Jek)
• Startups Booming
• Venture Capital,
Angel Investor
6. JACK MA ON INDUSTRIAL REVOLUTION 4.0 :
▪ Second Industrial Revolution bring machine that can
replace human power.
▪ Third Industrial Revolution bring computer and
automatization on production line.
▪ Fourth Industrial Revolution bring AI (Artificial
Intelligence)
This revolution will make many people unhappy
because AI with Machine Learning Algorithm can
replace human works.
6
7. JACK MA ON RESPONDING TO INDUSTRIAL
REVOLUTION 4.0 :
▪ Education is not enough, now you have to be creative and
innovative.
▪ Push local product that have higher sale chance on global
market with use of internet, specially product common in
local market, rare, and high demand at global market.
▪ Inclusion on trading (Every business have same opportunity
on trading regardless of it’s size) and extra focus on
education.
▪ Start today. “ Fix your roof when the sun still at the sky –
Jack Ma” because we cant prevent Industrial 4.0 distruption.
7
8. MACHINE LEARNING
▪ Machine Learning can create it own
mathematical models. Prior to
Machine Learning, analyst build
mathematical models and it can’t
adapt quickly if there any changes is
the data.
▪ Using neuron at the brain principle, so
Machine Learning didn’t need
explicitly program to find pattern in the
data.
▪ Depend on how much data and
quality of the data.
▪ Previously hard to do by computer
such as recognizing pattern in image,
now can be achieve by Machine
Learning.
8
9. TRADITIONAL VS MACHINE LEARNING
9
Study The
Problem
Write Rules
Evaluate
Analyze
Error
Launch
Train ML
Algorithm
Evaluate
Solution
Launch
Update
Data
Data
Machine Learning
Can Be
Automated
Hands On Machine Learning with Scikit-Learn & Tensorflow – Aurelien Geron
Traditional
10. MACHINE LEARNING AT PROCESSING DATA
10
Deep Learning – Ian Goodfellow, Youshua Bengio, Aaron Courville
Input
Hand –
Designed
Program
Output
Traditional – Rule Based System
Machine Learning / Representation Learning
Input Features
Mapping
from
Features
Output
Learn From Data
11. WHO USE MACHINE LEARNING?
11
Google
Facebook
Man Group
(https://www.cnbc.com/2017/09/28/man-group-one-of-
worlds-largest-funds-moves-into-machine-learning.html)
Telkomsel
(https://www.cnnindonesia.com/teknologi/201708241155
29-213-236939/veronika-cara-telkomsel-bicara-dengan-
pelanggan/)
PT KAI
(https://kai.id/?_it8tnz=TWc9PQ==&_8dnts=WkdWMFlX
bHM=&_4zph=TVRFPQ==&_24nd=TVRrd01RPT0=)
Morgan Stanley
(https://www.bloomberg.com/news/articles/2017-05-
31/morgan-stanley-s-16-000-human-brokers-get-
algorithmic-makeover)
12. Jack Ma on Surviving For The Next 30 Years
12
IQ, EQ and LQ
(Love Quotient)
Wisdom not
Muscle
Secret of China
Grow so Fast
Future-Proof Our
Kids
• Respect Others
• Care Others
• Compete by Wisdom
• Compete by Knowledge
• Chinese Learn a lot
• Open Minded
• Learn from others
• Learn Things That Computers Cannot
• Creative, Innovate, Sportsmanship,
Culture
• Value and believe.
13. Morgan Stanley’s 16,000 Human Brokers Get
Algorithmic Makeover
13
all them cyborgs. Morgan Stanley is about to augment its 16,000 financial advisers with machine-learning
algorithms that suggest trades, take over routine tasks and send reminders when your birthday is near.
The project, known internally as “next best action,” shows how one of the world’s biggest brokerages
aims to upgrade its workforce while a growing number of firms roll out fully automated platforms called
robo-advisers. The thinking is that humans with algorithmic assistants will be a better solution for
wealthy families than mere software allocating assets for the masses.
At Morgan Stanley, algorithms will send employees multiple-choice recommendations based on things
like market changes and events in a client’s life, according to Jeff McMillan, chief analytics and data
officer for the bank’s wealth-management division. Phone, email and website interactions will be
cataloged so machine-learning programs can track and improve their suggestions over time to generate
more business with customers, he said.
“We’re desperately trying to pattern you and your behavior to delight you with something you may not
have even been asking for, but based on what you have been doing, that you might find of value,”
McMillan said in an interview. “We’re not trying to sell you, we’re trying to find the things you want and
need.”
Faced with competition from cheaper automated wealth-management services and higher expectations
set by pioneering firms like Uber Technologies Inc. and Amazon.com Inc., traditional brokerages are
starting to chart out their digital future. It turns out that the best hope human advisers have against
robots is to harness the same technologies that threaten their disruption: algorithms combined with big
data and machine learning.
https://www.bloomberg.com/news/articles/2017-05-31/morgan-stanley-s-16-000-human-brokers-
get-algorithmic-makeover
