Explain how AI BIG DATA and cloud will impact our society.

1. Impact of AI
Bigdata and Cloud
to our Society
Dr.Putchong Uthayopas
Vice President for Information, Kasetsart
University
putchong@ku.th

2. Big Data Cloud and AI are
changing the world

5. Source: Wikipedia (cloud computing)
What is Cloud
Computing?
• Cloud Computing is a kind of
Internet-based computing that
provides shared processing
resources and data to computers
and other devices on demand.

6. Power Grid
Inspiration for
Computing?:
Deliver ICT services
as “computing
utilities” to users

7. Google Cloud Data Center

8. Example of
cloud
computing
usage
Storage:
• Dropbox, google drive, iCloud
Computing
• Amazon AWS, Google cloud platform
Application
• Gmail, google calendar, Microsoft office 360
Entertainment
• Netflix, amazon video
Online Market
• Amazon, ebay, ali-express

9. Why Cloud
Computing?
Scalability and Elasticity
Centralized Management
Centralized security
Rich programming Environment

10. Cloud
Deployment
Model

11. Cloud Service
Models

14. Pfizer on Amazon Cloud
• Pfizer, Inc. applies science and global resources to improve
health and well-being at every stage of life. The company
manufactures medicines for people and animals.
• Challenge
• Pfizer’s high performance computing (HPC) software and systems
for worldwide research and development (WRD) support large-scale
data analysis, research projects, clinical analytics, and modeling.
• Solution
• Pfizer has now set up an instance of the Amazon VPC (Virtual
Private Cloud) to provide a secure environment with which to carry
out computations for WRD. The VPC has enabled Pfizer to respond
to these challenges by providing the means to compute beyond the
capacity of the dedicated HPC systems, which provides answers in a
timely manner.
• Benefit
• Pfizer did not have to invest in additional hardware and software,
which is only used during peak loads; that savings allowed for
investments in other WRD activities.”

15. The Khan Academy Scales and Simplifies with Google App Engine
• Khan Academy is a not-for-profit that produces and posts a vast collection of free educational
online videos about math and science topics ranging from algebra and trigonometry to biology
and economics. Its hugely popular website (www.khanacademy.org) have students answer
some 1.5 million practice questions per school day.
• What they wanted to do
• Find a scalable solution and outsource server maintenance
• Focus resources on improving the user experience
• What they did
• Deployed Google App Engine to host and maintain KhanAcademy.org
• What they achieved
• Ability to support 3.8 million unique visits each month, along with 1.5 million practice
questions served and answered every school day
• Functionality that lets students chart their progress through profiles built via the Google
App Engine
• A system that easily handles usage surges

16. • Netflix needs to support seamless global
service of video content to million of users.
• Solution
• AWS enables Netflix to quickly deploy
thousands of servers and terabytes of storage
within minutes.
• Benefit
• Users can stream Netflix shows and movies
from anywhere in the world, including on the
web, on tablets, or on mobile devices such as
iPhones.

20. Big Data is
transforming
the World

21. What Big Data is
about?
• Collecting massive amount of Data
• Sensor, social network
• Do something meaningful with it
• making decision, predicting future.
• How many teenager age 15-20 use
Samsung phone
• Which mobile phone people age 45 are
most likely to choose

22. Everyday Big Data

24. https://www.slideshare.net/sfamilian/working-with-big-data-jan-2016-part-1/6-CONTEXT_WHATS_BIG_DATAHOW_BIG

25. Property of Big
Data 3V

26. Data variety

27. Why ?
• Improve product and service
• Increase customer
satisfaction/behavior
• Improve operation efficiency
• Understand emerging market
trends
Value of big data is
in the insights it
produces when
analyzed
discovered
patterns, derived
meaning, indicators
for decisions.
http://www.intel.com/content/dam/www/public/us/en/documents/product-briefs/big-data-cloud-technologies-brief.pdf )

28. Store indefinitely Analyze See results
Gather data
from all sources
Iterate
New big data thinking: All data has value
All data has potential value
Data hoarding
No defined schema—stored in native format
Schema is imposed and transformations are done at query time (schema-on-read).
Apps and users interpret the data as they see fit
28

29. What will
happen?
How can we
make it happen?
Predictive Analytics
Prescriptive Analytics
What
happened?
Why did
it happen?
Descriptive Analytics
Diagnostic Analytics
Value

30. Big Data and BNK48
https://www.mangozero.com/mangozero-review-zocial-eye-with-bnk48-
data-2017/

31. BNK48 Big Data
• การเก็บข้อมูลจากการเช็คคา Keyword
ต่างๆ ที่เกี่ยวข้องกับวง เช่น BNK48,
#BNK48, บีเอ็นเค48 เป็นต้น
• ระยะเวลาที่เก็บข้อมูลเฉพาะครึ่งปีหลัง คือ
ตั้งแต่วันที่ 1 มิ.ย. – 28 ธ.ค. 2017
• มีการใส่ข้อมูลเพจและ Social หลักของวง
รวมถึง YouTube Channel เพื่อให้มี
ความแม่นยามากขึ้น
• Social Media ที่จัดเก็บได้คือ
Facebook, Twitter, Instagram,
Pantip, YouTube, Web

32. เมื่อดูสถิติการ Engagement ที่มีคนมาสนใจ จะเห็นว่าช่วง
ตั้งแต่วันที่ 18 พฤศจิกายน 2017 สถิติทุก Social มีการพูดถึงเยอะ
ขึ้นอย่างเห็นได้ชัด ซึ่งวันที่ 18 ก็คือวันแรกของการเปิดตัว
Music Video คุกกี้เสี่ยงทายนั่นเอง

33. The Fourth Paradigm: Data-
Intensive Scientific
Discovery
• Increasingly, scientific breakthroughs will be powered
by advanced computing capabilities that help
researchers manipulate and explore massive datasets.
• The speed at which any given scientific discipline
advances will depend on how well its researchers
collaborate with one another, and with technologists,
in areas of eScience such as databases, workflow
management, visualization, and cloud computing
technologies.

34. Science Evolution: The 4th Paradigm
Experiment
al Science
• Discovery
through
Experiments
Theoretical
Science
•Discovery through
the making of
theory to explain
things
Computation
al Science
• Discovery
through
simulation and
modelling
Data
Intensive
Science
• Discovery
through insight
from big data
analytic

35. CANDLE: Exascale Deep Learning and Simulation
Enabled Precision Medicine for Cancer
• Department of Energy (DOE) entered into
a partnership with the National Cancer
Institute (NCI) of the National Institutes of
Health (NIH)
• Part of the Exascale Computing Project
• Solving three challenges
• “RAS pathway problem”—is to understand
the molecular basis of key protein
interactions in the RAS/RAF pathway that is
present in 30% of cancers.
• “drug response problem”—is to develop
predictive models for drug response that can
be used to optimize pre-clinical drug
screening and drive precision medicine
based treatments for cancer patients.
• “treatment strategy problem”—is to
automate the analysis and extraction of
information from millions of cancer patient
records to determine optimal cancer
treatment strategies across a range of
patient lifestyles, environmental exposures,
cancer types and healthcare systems.

36. Very Large and Challenging Problem
• In the RAS pathway problem, we guide multi-scale molecular dynamics (MD) runs through a large-scale
state-space search, using unsupervised learning to determine the scope and scale of the next series of
simulations based on the history of previous simulations. The scale of the deep learning in this problem
comes from the size of the state-space (O(109)) that must be navigated and the number of model
parameters to describe each state (O(1012)).
• In the drug response problem, we use supervised machine learning methods to capture the complex, non-
linear relationships between the properties of drugs and the properties of the tumors to predict response to
treatment and therefore develop a model that can provide treatment recommendations for a given tumor.
The scale in this problem derives from the number of relevant parameters to describe properties of a drug
or compound (O(106)), number of measurements of important tumor molecular characteristics (O(107), and
the number of drug/tumor screening results (O(107)).
• In the treatment strategy problem, we use semi-supervised machine learning to automatically read and
encode millions of clinical reports into a form that can be computed upon. These encoded reports will be
used by the national cancer surveillance program to understand the broad impact of cancer treatment
practices and drive simulations of entire cancer populations to determine optimal treatment strategies for
patient cohorts. The scale of this problem is determined by the number of individual patient records
(O(108)), the scale of the medical vocabulary (O(105), and the scale of the structure output record (O(105)).
When clinical images are added, the input scale jumps an additional two orders of magnitude.

37. 7 Big Data Use Cases for
Healthcare
• 1. Analyzing Electronic Health Records (EHRs) – Doctors sharing EHRs can aggregate and analyze
data for trends that can reduce healthcare costs. Sharing data between physicians and healthcare
• 2. Analyzing Hospital Networks – Consider the power of analyzing trends in hospital care. For
example, centralizing analysis of medical instruments in a pediatric ward can isolate possible infant
infection trends earlier
• 3. Control Data for Public Health Research – Using analytics normalizes raw patient data to fill gaps
in public health records that can affect regulations as well as providing better care.
• 4. Evidence-Based Medicine – Using evidence-based medicine, the doctor can match symptoms to
a larger patient database in order to come to an accurate diagnosis faster and more efficiently.
• 5. Reducing Hospital Readmissions – Hospital costs are rising partially because of high readmission
rates within 30 days of patient release. Using big data analytics in order to identify at-risk patients
based on past history, chart information, and patient trends, hospitals can identify at-risk patients
and provide the necessary care to reduce readmission rates.
• 6. Protecting Patients’ Identity – Insurers like UnitedHealthcare are using big data analytics in
order to detect medical fraud and identity theft. The company uses analytics on speech-to-text
records from calls to the call center to identify potential fraudsters. The insurance company also
uses big data in order to predict which types of treatment plans are more likely to succeed.
• 7. More Efficient Medical Practice – Using big data, the practice was able to analyze more than
2,200 processes and procedures. As a result, the practice was able to streamline workflow, shift
clinical tasks from doctors to nurses, reduce unnecessary testing, and improve patient satisfaction
https://imaginenext.ingrammicro.com/data-center/7-big-data-use-cases-for-healthcare

38. Examples of How
Big Data solved
Public Problems
https://www.geos.ed.ac.uk/~gisteac/eeo-agi/2013-14/1_schmid_27092013.pdf

39. Big Data and Government: How the Public Sector
Leverages Data Insights
-New opportunity for innovation
-New insight for services for public
interest
-Enable transparency
-Provision in Insider threats
-Workforce effectiveness
-Emergency response
https://hortonworks.com/article/big-data-and-government-how-the-public-sector-leverages-data-insights/
https://www.sas.com/en_th/insights/articles/big-data/big-data-government.html

40. 3 Key areas in UK
governments for big data
• Improving the experience
of the citizen
• Making the government
more efficient at
delivering their services
• Boosting business and the
wider economy

41. Big data can answer the
following problems
o How changes to tax policy can predict impacts to
the economy
o How the impact of technology will significantly
affect the environment
o How food borne illnesses can pose potential
threats to a community
o Which programs are effective in fighting child or
adult obesity
o How incentive programs can encourage women
to give birth

42. Energy efficiency
CO2 reduction
Local authorities have a key role to meet
CO2 reduction targets: renewable
energies and fuel poverty,
• online publishing of location of roadworks to
reduce congestion and CO2 emission
• Optimising transport and waste management
services
• Reduction in mileage/CO2 emission through
better use of transport

43. Waste collection
Use of GeoInformation to optimise refuse waste collection routes
– Reduction from 18 to 16 collection rounds and to 4 days working
week with cashable annual savings of £153,000 per annum
– Mileage reduction of 12-13 per cent
– Employee overtime will be virtually eliminated,
– Reduction in vehicle fleet
Savings of up to £ 3.1 million over ten years through the use of
GeoInformation in waste management and better targeting of
customers.
– Green savings included 15% reduction in fuel
– 18.8% reduction in waste sent to landfill,
– reductions in CO2 emissions and 40kg reduction per annum of waste
paper recycling from Council offices.

44. Crime analysis
South Yorkshire Police intranet-based police
intelligence solution leads to £ 1 million in annual
savings.
– Basic Analysis of potential crime location:
Demographic Analysis:
– 80 Briefings per day for response teams and
management.
Use of CCTV footage, high resolution aerial photos
and better sharing of intelligent data within police
forces is estimated to save £ 4 million per year across
police forces in England and Wales

45. Online incident report
Use of interactive web mapping to identify fault location
• The information is automatically routed to one of the 8
partnership agencies responsible for the service
• The key benefits to the participating local service
providers are:
– More cost effective contact and feedback from citizen
– Reduction in service costs, with 18,800 fault incidents
logged over 5 years with an approximate net saving of
£60,000
– Cost of remedial action reduced by more accurate
location

46. Consideration for Applying Big Data
http://fredericgonzalo.com/en/2013/07/07/big-data-in-tourism-hospitality-4-key-components/

47. How to start
• Planning the data collection process
• Where is your data?
• Data model , standardization
• Planning the Analytics process
• Where is the accessible data?
Open data
• Analytics tools and
infrastructure
• Planning Data governance
• Process of how to manage the
collection and usage of data
• Who responsible for what?
Clear role and responsibility
• Planning how to secure the data
• Physical security
• Backup and Disaster recovery
• Cyber Security

48. Why
organization
fails with big
data

49. People is the
key

52. Robot and AI is a Science Fiction!
Isaac Asimov's "Three Laws of
Robotics“
1.A robot may not injure a human
being or, through inaction, allow
a human being to come to harm.
2.A robot must obey orders given
it by human beings except where
such orders would conflict with
the First Law.
3.A robot must protect its own
existence as long as such
protection does not conflict with
the First or Second Law.

53. “AI is the new electricity” — Andrew Ng
Founder of google brain project

54. ปัญญาประดิษฐ์ (AI) คืออะไร
https://www.globalxfunds.com/ai-big-data-the-future-of-the-digital-world/
• ศาสตร์แขนงหนึ่งทางด้านวิทยาศาสตร์และเทคโนโลยีที่มีพื้นฐานมา
จากวิชาวิทยา การคอมพิวเตอร์ ชีววิทยา จิตวิทยา ภาษาศาสตร์…
• ความฉลาดเทียมที่สร้างขึ้นให้กับสิ่งที่ไม่มีชีวิต ...
• Neural Network เป็นการพยายามที่จะจาลองสมองมนุษย์
• เซลล์ประสาทและจุดเชื่อมต่อ
• Machine Learning / Deep Learning
https://www.youtube.com/watch?v=O5xeyoRL95U&list=PLrAXtm
ErZgOeiKm4sgNOknGvNjby9efdf

57. Driving Factor for the AI growth
• Big data has been collected on the cloud
• Social media data , location , mobile phone,
vehicle
• Better and cheape sensors everywhere
• Improved algorithm especially deep
learning
• Much more powerful hardware
• Multicore-CPU
• Much bigger memory
• Much faster network
• Faster and bigger storage
• Faster accelerator hardware : GPU

59. Big Data is driving AI
● Machine learning needs lots of data to learn for better prediction
or clustering.
● Deep learning needs millions of images for training/ text data for
feature representation.
● IoT produces lots of sensor data (minuitely, hourly, daily) useful for
machine learning.
● Everydays (mobile/Internet) business transactions create lots of
data used customer marketing/promotion.
“In fact, 90% of the world’s data has been generated since 2015 .That
year, the digital universe, i.e., the reservoir of data created and copied,
totaled less than 10 zettabytes—that would be 10, followed by 21 zeros.
By 2020, it is expected to grow more than four times to 44 zettabytes.
Just five years after that, it could reach 180 zettabytes.”
Watermark, “Artificial intelligence is the fourth industrial revolution,” Jan 18, 2018
Forbes, “IoT Mid-Year Update From IDC And Other Research Firms,” May 16, 2016

60. AI is driving Big Data as well!

61. สร้างปัญญาประดิษฐ์ได้อย่างไร (How-to)
• ตัวแบบ Machine learning model from scratch
• ตัวแบบ Deep learning model from scratch
สกัด
คุณลักษณะ
เตรียมข้อมูล
ฝึกฝน สร้าง
ตัวแบบ
นาไปใช้
ทดสอบ ตัว
แบบ
เตรียมข้อมูล ศึกษาข้อมูล ปรับ
ข้อมูล
ฝึกฝน สร้างตัวแบบ นาไปใช้
ทดสอบ ตัว
แบบ

62. เครื่องมือสร้างปัญญาประดิษฐ์
• ช่วยเหลือในด้านใดบ้าง
• การสร้างดาต้าเซต การทา annotation /clean ข้อมูล
• ตัวแบบ off-the-shelves
• สร้างตัวแบบ หา hyper-พารามิเตอร์ อัตโนมัติ
• จัดการครบวงจร
62

63. รูปแบบของเครื่องมือที่ใช้
• AI as an application
• AI as a service
• AI libraries for developers
• AI Studio for developers
• AI Infrastructure management

64. 64

65. การใช้งาน AI
• ด้านภาพวิดีโอ
• ด้านเสียง ภาษาธรรมชาติ
• ด้านวิทยาการข้อมูล วิเคราะห์ นาเสนอ
• ด้านหุ่นยนต์ IoT
6565
Image classification Image segmentation Image generation
Video
Segmentation Generation/translation
Voice/Text classification

68. Video/Image
Voice/Text
Data Science/Sensor data

69. 69
https://recruitingdaily.com/r
obotics-automation-will-
provide-talent-feed-shift/
https://tech.co/news/healthcare-robot-
surgeons-ai-2017-07
https://medium.com/topbot
s/future-factories-how-ai-
enables-smart-
manufacturing-
c1405f4ec0e6
https://www.cio.com/article/323
9924/cios-listen-up-voice-
recognition-meets-the-
printer.html
https://www.youtube.com/watch?v=K4
u4Dpl6NKk
http://vision.cs.uiuc.edu
/projects/activity/
https://software.intel.com/en-us/articles/automatic-defect-
inspection-using-deep-learning-for-solar-farm
https://www.nasa.gov/mission_pages/Grace
/multimedia/Drought.html
Solarcell defect Drought Rainfall
Surgery/Healthcare Robotics/manufacturing Sensor data
monitoring
Car insurance
Text2speech Surveillance
Activity Recog.

70. Applications area that most affected
Industrial automation
Autonomous vehicle
Consumer retail and
E-commerce
Healthcare
Smart assistant

71. • 1. Powering Infrastructure, Solutions and Services
• 2. Cybersecurity Defense
• 3. Health Care Benefits
• 4. Recruiting Automation
• 5. Intelligent Conversational Interfaces
• 6. Reduced Energy Use And Costs
• 7. Predicting Vulnerability Exploitation
• 8. Becoming More Customer-Centric
• 9. Market Prediction
• 10. Accelerated Reading
• 11. Cross-Layer Resilience Validation
• 12. Accounting And Fintech
• 13. Advanced Billing Rules
• 14. Understanding Intentions And Behaviors
• 15. Proposal Review
https://www.forbes.com/sites/forbestechcouncil/2018/09/27/15-business-applications-for-artificial-intelligence-and-machine-learning/#3119c36f579f

72. • Mining Medical Records
• Assisting in Monotonous
Tasks
• AI Chatbots
• Virtual Healthcare
Assistants
• Treatment Design
• Drug Creation
https://www.intelegain.com/ai-in-healthcare/

74. Impact of Big
Data, AI , and
Cloud to our
Society

75. Great Things about AI,
Big Data and Cloud
• Things going to be smart, connected, and interact
• Understand our demand better
• What movie we should watch tonight?
• AI will be used to optimized our quality of life
• Energy usage, environmental adjustment
• AI and Big Data will help organization function better
• Better decision based on data
• AI will empower users in many ways
• Better medical diagnostics
• Better transportation (smart bus ,self-driving car)
• AI will create many new products and services

76. How should the university
prepare the students, staff
and learning environments?
AI Personnel preparation:
1. Prepare skill to work with AI systems (eg.
curious mindset, becoming a problem finder,
thirst for knowledge and learning)
2. Provide them opportunity to pursue learning
and training program. Provide life-long
learning resources. Provide access to
computer science course online by every
student level.
3. Modernize the course teaching: do not
reward on memorizing, but learning by
doing, favor of curiosity, experimentation.
https://www.entrepreneur.com/article/295520

79. https://www.kuppingercole.com/blog/small/the-ethics-of-artificial-intelligence
Ethics in AI• ฎ้รแห รื ฤณ
• AI has a potential for these systems
to cause harm to individuals as well
as society in general.
• Ethics considerations can help to
better identify beneficial
applications while avoiding harmful
ones.
• 5 major ethical issues that need to
be addressed in relation to AI:
• Bias
• Explainability
• Harmlessness
• Economic Impact
• Responsibility

81. 5G
/IoT/Blockchain
Coming soon near you!

85. https://www.sensorsmag.com/components/smart-sensors-fulfilling-promise-iot

89. Thank You

90. ผศ.ดร.ภุชงค์อุทโยภาศ
• ปัจจุบันดารงตาแหน่งรองอธิการบดีฝ่ายสารสนเทศ มหาวิทยาลัยเกษตรศาสตร์
• ปริญญาตรีด้านวิศวกรรมไฟฟ้าจากจุฬาลงกรณมหาวิทยาลัย ในปี 2527
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• ปริญญาโทด้านวิศวกรรมคอมพิวเตอร์ จาก University of Louisiana at Lafayette ใน ปี2538
• ปริญญาเอกด้านวิศวกรรมคอมพิวเตอร์ จาก University of Louisiana at Lafayette ในปี 2539
• ทางานวิจัยด้านระบบคอมพิวเตอร์แบบขนาน ซุปเปอร์คอมพิวเตอร์และคลาวด์ มีผลงานตีพิมพ์กว่า 160 บทความ
• ผู้อานวยการโครงการไทยกริดแห่งชาติ กระทรวงเทคโนโลยีสารสนเทศและการสื่อสาร ในปี 2549-2551
• รางวัลวิศวกรคอมพิวเตอร์ดีเด่นสาขา System Integrator จาก วิศวกรรมสถานแห่งประเทศไทย ในปี พศ. 2555
• อดีตนายกสมาคม Computational Science and Engineering Association of Thailand
• ประธาน Digital University Forum (University CIO Forum)