Connective Learning

Connective Learning
Based on the experience of tons of failures
with a couple of successes
Dr. Virach Sornlertlamvanich
Assistant Rector for Research and Innovation, Thammasat University
SIIT, Thammasat University
Chair of Digital Cluster, RUN
virach@gmail.com, https://virach.com
KU Presidents Forum, Kaset Fair, 1 Feb 2019

“Creativity is just
connecting things.”
- Steve Jobs

Eight Challenges Innovations Confront
1. Find an idea
2. Develop a solution
The idea is one thing; a working solution is another
3. Seek sponsorship and funding
4. Reproduction
Scale up issue:- HW vs SW
5. Reach your potential customer
Wheel, steam engine, and freeze-dried foods were innovations that existed before 100
BCE, but it took centuries for innovators to position each of them in ways the average
person could use
6. Beat your competitors
The wise innovators keeps an eye on his peers’ work for purposes of collaboration,
inspiration, or tactical recognizance
7. Timing
Why automobiles are rated in horsepower and electric lights in candles. As great as your
idea is, will the culture be ready when it’s finished?
8. Keep the lights on
The myths of Innovation, Scott Berkun, O’Reilly, 2007
Learning

Paradigm Shift in
Research and Education

Lesson Learned from
the Past Research Strategy
Many successes in Research,
but very few can be Commercialized.
WHY?
Business
Academ
ic
The key is Problem Setting
- What topics that can serve both Research
interest and Industrial needs?
- How the Research topics can be realized
for Industrial needs?
- How the Industrial needs can be
interpreted to Research interest?
Boost up
Research and
Innovation

Issues
• Mismatch between research interest and industrial
needs
• Unmatched skill sets
• Research vs. Development
• Prototyping vs. Production
• Proving vs. Operating
• Lack of proper business model for the research i.e.
licensing, pricing, etc.
• Lack of experience of supporting team, PI, PM, PC, etc.
Resulting in “On the shelf” vs. “Off the shelf” research

R&D 1.0
R&D Industry
R&D 2.0
R&D Industry
R&D 3.0
R&D Industry
R&D 4.0
R&D Industry
Research Industry Result
Output oriented Technology sourcing Useless
- Market oriented
- High price
- Requirement
unmatched
- High cost
- Discontinuous dev.
and service
- Re-development
- Not ready to use
- Unusable research
Targeted budget Cost reduction Development only
- Development
oriented
- Lots of fund
- Avoid internal
research
- Fund acquirement
- Superficial dev
- Low research work
Co-research Research privilege R&I value added
- TM
- STEM
- Incubation center
- Co-proposal
- Save cost
- Tax exemption
- STI product
- High value product
Research Business No collaboration
- Researcher driven - Market driven
- OEM
- On the shelf
- No R&D product
(expecting)

Research Industry Result
Output oriented Technology sourcing Useless
- Market oriented
- High price
- Requirement
unmatched
- High cost
- Discontinuous dev.
and service
- Re-development
- Not ready to use
- Unusable research
Targeted budget Cost reduction Development only
- Development
oriented
- Lots of fund
- Avoid internal
research
- Fund acquirement
- Superficial dev
- Low research work
Co-research Research privilege Share cost
- TM
- STEM
- Incubation center
- Co-proposal
- Save cost
- Tax exemption
- Superficial proposal
- Impractical
Research Business No collaboration
- Researcher driven - Market driven
- OEM
- On the shelf
- No R&D product
(probably)
R&D 1.0
R&D Industry
R&D 2.0
R&D Industry
R&D 3.0
R&D Industry
R&D 4.0
R&D Industry
R&D 4.0
R&D Industry

R&D Trap
2010
1980
1990
2000

We need a design of
connective learning

Require and Deliverable
University Industryrequire
Research
Innovation
Market
Researcher
RA
deliverable
Publication
Patent
Prototype
Research
Innovation
Graduate
Revenue
deliverable
Product
Business
Patent
Revenue

Require and Deliverable
Research
Innovation
Market
Researcher
RA
deliverable
Publication
Patent
Prototype
Research
Innovation
Graduate
Revenue
deliverable
Product
Business
Patent
Revenue
Non-STI
product
Unused
research
Research and Industry Mismatch Problems
Problem is not that the research is not good, or researchers do not perform,
but it is the problem of research authorities who do not know how to make use of it.

Require and Provide
Budget
Equipment
Topic
provide
Research
Innovation
Consultancy
Network
Research
RA
provide
Outsource
Equipment
Topic
Staff
require
Research
Innovation
Market
Researcher
RA
deliverable
Publication
Patent
Prototype
Research
Innovation
Graduate
Revenue
deliverable
Product
Business
Patent
Revenue
CRDC-SIIT (2016-2018)
Center for Research and Development Collaboration

CRDC-SIIT as a Role of Catalyst (2016-2018)
Center for Research and Development Collaboration @SIIT
•Faculty member
•Researcher
•Policy execution
•Funding agency
•Co-research space
•Course work
•Seminar, training
•Business partner
•Investor
Business
Partnership
Innovation
hub
Network of
Professional
Support of
Policy and
Sourcing
BINS

GD.FINDI
(Production Line Simulator)
• J Industry LEXER
• T Industry DHAS TPA
• Univ. SIIT, TNI
Seminar ConsultancyR&D Education Business
MAT2016

Compact EV
• J Industry FOMM
• T Industry Bangchak
• Univ. SIIT
Evaluation R&D Business
Thai PBS
2016/1/31

Mimamori System
(Elderly Care
Support System)
• J Industry AIVS
• T Industry Banphaeo
Hospital, Sawangkanives
• Univ. SIIT
Evaluation R&D Business

DTAC AI Lab
• Established in 2017
• Sponsor 8-10 research projects
• To connect data science
students to leading professors
with real-world business cases.
• Initially, focus on time-
consuming processes best left
to machines

SIIT-LINE Developer Challenge
• It is the first hackathon that
Line provides specifically to SIIT
students
• It is a programming and
business model competitions
• Duration: 4-months (Aug-Dec
2018)
• During the program, Line
provides Line APIs and
mentoring & tutorial sessions
for SIIT students to develop the
applications
• Based on 2018, 9 teams (27
students) joined the
competition

AI Boom
Opportunity and Risk

What is AI?
--classic and modern aspects--
• From a behavioral point of view, is an artificial
agent that shows certain characteristics of
intelligence like:
• Perception
• Knowledge acquisition
• Knowledge representation
• Reasoning
• Planning
ó Regression
ó Deep learning
ó Modeling
ó Prediction
ó Recognition

Differences within AI
Artificial Intelligence
• General AI
• Vertical AI (Expert Systems)
• Natural Language Processing
• Computer Vision
• Machine Learning
• ...

Thinking, Fast and Slow
by Nobel laureate Daniel Kahneman on AI (2011)
--The two systems--
http://upfrontanalytics.com/market-research-system-1-vs-system-2-decision-making/

Multilayered neural
networks to vast amounts
of data
Enable machines to
improve at tasks with
experience
Mimic human intelligence
using logic, if-then rules,
decision trees, machine
learning and deep learning
Deep Learning (Neural learning from data with high quality, but imperfect results)
Watson (Associative learning from data with high quality, but imperfect results)
Semantic Web (Knowledge graph links formation from extraction, clustering and learning)
Modern AI is making some huge strides

AI advancement that brings about the 3rd AI Boom
• Thinking Machines
• DeepBlue Chess Machine (1997)
• IBM Watson Quiz Show (2011)
• DeepMind AlphaGo (2016)
• Self-Driving Cars
• RHINO Museum Tour Guide (1997)
• DARPA Grand Challenge (2005)
• Google Self-driving Car (2011)
• Smart Assistants
• Apple Siri Personal Assistant (2011)
• Amazon Echo & Alexa (2014)
• Google Home & Assistant (2016)
Byoung-Tak Zhang, “Human-Level AI and Video Turing Test”
Google’s AlphaGo AI narrowly beats the
world’s top human Go player 2017
SIliconangle
Geospatialworld
Pocket-lint

Action
Execution
Control
Input
Acquisition
Recognition
Comparable AI Frameworks
Data Processing Storage Analysis
AI x RobotAI x IoT
Visualization
Sensing Recognition Modeling Planning Action
Accumulating Knowledging Understanding Solving
Data Visualization

How can AI be our
educational partner?
Elderly care robots Fujitsu Pepper
JRA
CyberDyne
HRP
Cinnamon
Panasonic
Aibo
Asimo

Learning AI
— Online Self-Learning MOOC Style —
https://futuremonger.com/artificial-intelligence-learning-phd-or-mooc-selflearning-458059725421

Lecture Note Generation
(VDO Summarization)

Summary
Multilingual support
VDO Playback
Educational Partner
for self-learning
Scene search
Hyper link
Word Cloud

Speech-to-Text Translate Summarize
Keyword
Extraction
Lecture Note
Word Cloud
Subtitle
Synchronize
Subtitles
System Architecture

Challenges
• Data -> Annotated Data -> Cooked Data
• Data -> Metadata -> Hyper Data
Open, Share and Connect
In concern with:
• Data Accessibility
• Privacy
• Security
• Intellectual Property

Challenges
• Current AI is nothing more than a machine that has a
capability to learn.
• AI should not only be able to learn and reason, it should also be able
to interact and react.
• AI platforms should do more than answer simple questions.
They should be able to learn at scale, reason with purpose,
and naturally interact with humans. They should gain
knowledge over time as they continue to learn from their
interactions, creating new opportunities for business and
positively impacting society.
• AI will result in net job gain. Reskill for new job role to work
with AI

Summary 1/2
• Lesson learned from the past research strategy
• Mismatch between research interest and industrial
needs
• Unmatched skill sets
• Lack of proper business model for the research i.e.
licensing, pricing, etc.
• Lack of experience of supporting team, PI, PM, PC, etc.
• Critical factors for success in university-industry
research projects
• Technology
• Corporate usefulness
• Corporate capacity
• Corporate confidence

Summary 2/2
• Challenge in CRDC’s University-Industry Link
• From Require and Deliverable model
to Require and Provide model
• CRDC-SIIT as a role of catalyst
• Business partnership
• Innovation hub
• Network of professional
• Support of policy and sourcing
• Win-win university-industry collaboration
• Research faculty
• Market potential
• STI value added product
• Research facility
• Capacity building
• Research publication

Connective Learning

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