AI is the science and engineering of creating intelligent machines and software. It draws from fields like computer science, biology, psychology and linguistics. The goal is to develop systems that can perform tasks normally requiring human intelligence, like visual perception, decision making and language translation. Some key applications of AI include machine learning, expert systems, natural language processing and computer vision. As AI systems continue advancing, they are becoming better than humans at certain tasks like playing strategic games.
Multipleregression covidmobility and Covid-19 policy recommendationKan Yuenyong
Multiple Regression Analysis and Covid-19 policy is the contemporary agenda. It demonstrates how to use Python to do data wrangler, to use R to do statistical analysis, and is enable to publish in standard academic journal. The model will explain whether lockdown policy is relevant to control Covid-19 outbreak? It cinc
Monitoring world geopolitics through Big Data by Tomasa Rodrigo and Álvaro Or...Big Data Spain
Data from the media allows to enrich our analysis and to incorporate these insights into our models to capture nonlinear behaviour and feedback effects of human interaction, assessing their global impact on the society and enabling us to construct fragility indices and early warning systems.
https://www.bigdataspain.org/2017/talk/monitoring-world-geopolitics-through-big-data
Big Data Spain 2017
16th - 17th November Kinépolis Madrid
A final project presentation on the project based on THE GDELT Database.
Complete Report : https://samvat.github.io/ivmooc-gdelt-project/The GDELT Project - Final Report.pdf
NG2S: A Study of Pro-Environmental Tipping Point via ABMsKan Yuenyong
A study of tipping point: much less is known about the most efficient ways to reach such transitions or how self-reinforcing systemic transformations might be instigated through policy. We employ an agent-based model to study the emergence of social tipping points through various feedback loops that have been previously identified to constitute an ecological approach to human behavior. Our model suggests that even a linear introduction of pro-environmental affordances (action opportunities) to a social system can have non-linear positive effects on the emergence of collective pro-environmental behavior patterns.
Multipleregression covidmobility and Covid-19 policy recommendationKan Yuenyong
Multiple Regression Analysis and Covid-19 policy is the contemporary agenda. It demonstrates how to use Python to do data wrangler, to use R to do statistical analysis, and is enable to publish in standard academic journal. The model will explain whether lockdown policy is relevant to control Covid-19 outbreak? It cinc
Monitoring world geopolitics through Big Data by Tomasa Rodrigo and Álvaro Or...Big Data Spain
Data from the media allows to enrich our analysis and to incorporate these insights into our models to capture nonlinear behaviour and feedback effects of human interaction, assessing their global impact on the society and enabling us to construct fragility indices and early warning systems.
https://www.bigdataspain.org/2017/talk/monitoring-world-geopolitics-through-big-data
Big Data Spain 2017
16th - 17th November Kinépolis Madrid
A final project presentation on the project based on THE GDELT Database.
Complete Report : https://samvat.github.io/ivmooc-gdelt-project/The GDELT Project - Final Report.pdf
NG2S: A Study of Pro-Environmental Tipping Point via ABMsKan Yuenyong
A study of tipping point: much less is known about the most efficient ways to reach such transitions or how self-reinforcing systemic transformations might be instigated through policy. We employ an agent-based model to study the emergence of social tipping points through various feedback loops that have been previously identified to constitute an ecological approach to human behavior. Our model suggests that even a linear introduction of pro-environmental affordances (action opportunities) to a social system can have non-linear positive effects on the emergence of collective pro-environmental behavior patterns.
Adversarial Analytics - 2013 Strata & Hadoop World TalkRobert Grossman
This is a talk I gave at the Strata Conference and Hadoop World in New York City on October 28, 2013. It describes predictive modeling in the context of modeling an adversary's behavior.
Massive Data Analysis- Challenges and ApplicationsVijay Raghavan
We highlight a few trends of massive data that are available for corporations, government agencies and researchers and some examples of opportunities that exist for turning this data into knowledge. We provide a brief overview of some of the state-of-the-art technologies in the massive data analysis landscape. Then, we describe two applications from two diverse areas in detail: recommendations in e-commerce, link discovery from biomedical literature. Finally, we present some challenges and open problems in the field of massive data analysis.
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
High Performance Data Analytics and a Java Grande Run TimeGeoffrey Fox
There is perhaps a broad consensus as to important issues in practical parallel computing as applied to large scale simulations; this is reflected in supercomputer architectures, algorithms, libraries, languages, compilers and best practice for application development.
However the same is not so true for data intensive even though commercially clouds devote many more resources to data analytics than supercomputers devote to simulations.
Here we use a sample of over 50 big data applications to identify characteristics of data intensive applications and to deduce needed runtime and architectures.
We propose a big data version of the famous Berkeley dwarfs and NAS parallel benchmarks.
Our analysis builds on the Apache software stack that is well used in modern cloud computing.
We give some examples including clustering, deep-learning and multi-dimensional scaling.
One suggestion from this work is value of a high performance Java (Grande) runtime that supports simulations and big data
Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...Geoffrey Fox
Keynote at Sixth International Workshop on Cloud Data Management CloudDB 2014 Chicago March 31 2014.
Abstract: We introduce the NIST collection of 51 use cases and describe their scope over industry, government and research areas. We look at their structure from several points of view or facets covering problem architecture, analytics kernels, micro-system usage such as flops/bytes, application class (GIS, expectation maximization) and very importantly data source.
We then propose that in many cases it is wise to combine the well known commodity best practice (often Apache) Big Data Stack (with ~120 software subsystems) with high performance computing technologies.
We describe this and give early results based on clustering running with different paradigms.
We identify key layers where HPC Apache integration is particularly important: File systems, Cluster resource management, File and object data management, Inter process and thread communication, Analytics libraries, Workflow and Monitoring.
See
[1] A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures, Shantenu Jha, Judy Qiu, Andre Luckow, Pradeep Mantha and Geoffrey Fox, accepted in IEEE BigData 2014, available at: http://arxiv.org/abs/1403.1528
[2] High Performance High Functionality Big Data Software Stack, G Fox, J Qiu and S Jha, in Big Data and Extreme-scale Computing (BDEC), 2014. Fukuoka, Japan. http://grids.ucs.indiana.edu/ptliupages/publications/HPCandApacheBigDataFinal.pdf
Presentation at the AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013
Additional related material at: http://wiki.knoesis.org/index.php/Smart_Data
Related paper at: http://www.knoesis.org/library/resource.php?id=1903
Abstract: We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. We organize our research around the five V's of Big Data, where four of the Vs are harnessed to produce the fifth V - value. To handle the challenge of Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle the challenge of Variety, we resort to the use of semantic models and annotations of data so that much of the intelligent processing can be done at a level independent of heterogeneity of data formats and media. To handle the challenge of Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize new concepts, entities and facts. To handle Veracity, we explore the formalization of trust models and approaches to glean trustworthiness. The above four Vs of Big Data are harnessed by the semantics-empowered analytics to derive Value for supporting practical applications transcending physical-cyber-social continuum.
Data Science Innovations is a guest lecture for the Advanced Data Analytics (an Introduction) course at the Advanced Analytics Institute at University of Technology Sydney
Adversarial Analytics - 2013 Strata & Hadoop World TalkRobert Grossman
This is a talk I gave at the Strata Conference and Hadoop World in New York City on October 28, 2013. It describes predictive modeling in the context of modeling an adversary's behavior.
Massive Data Analysis- Challenges and ApplicationsVijay Raghavan
We highlight a few trends of massive data that are available for corporations, government agencies and researchers and some examples of opportunities that exist for turning this data into knowledge. We provide a brief overview of some of the state-of-the-art technologies in the massive data analysis landscape. Then, we describe two applications from two diverse areas in detail: recommendations in e-commerce, link discovery from biomedical literature. Finally, we present some challenges and open problems in the field of massive data analysis.
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
High Performance Data Analytics and a Java Grande Run TimeGeoffrey Fox
There is perhaps a broad consensus as to important issues in practical parallel computing as applied to large scale simulations; this is reflected in supercomputer architectures, algorithms, libraries, languages, compilers and best practice for application development.
However the same is not so true for data intensive even though commercially clouds devote many more resources to data analytics than supercomputers devote to simulations.
Here we use a sample of over 50 big data applications to identify characteristics of data intensive applications and to deduce needed runtime and architectures.
We propose a big data version of the famous Berkeley dwarfs and NAS parallel benchmarks.
Our analysis builds on the Apache software stack that is well used in modern cloud computing.
We give some examples including clustering, deep-learning and multi-dimensional scaling.
One suggestion from this work is value of a high performance Java (Grande) runtime that supports simulations and big data
Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...Geoffrey Fox
Keynote at Sixth International Workshop on Cloud Data Management CloudDB 2014 Chicago March 31 2014.
Abstract: We introduce the NIST collection of 51 use cases and describe their scope over industry, government and research areas. We look at their structure from several points of view or facets covering problem architecture, analytics kernels, micro-system usage such as flops/bytes, application class (GIS, expectation maximization) and very importantly data source.
We then propose that in many cases it is wise to combine the well known commodity best practice (often Apache) Big Data Stack (with ~120 software subsystems) with high performance computing technologies.
We describe this and give early results based on clustering running with different paradigms.
We identify key layers where HPC Apache integration is particularly important: File systems, Cluster resource management, File and object data management, Inter process and thread communication, Analytics libraries, Workflow and Monitoring.
See
[1] A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures, Shantenu Jha, Judy Qiu, Andre Luckow, Pradeep Mantha and Geoffrey Fox, accepted in IEEE BigData 2014, available at: http://arxiv.org/abs/1403.1528
[2] High Performance High Functionality Big Data Software Stack, G Fox, J Qiu and S Jha, in Big Data and Extreme-scale Computing (BDEC), 2014. Fukuoka, Japan. http://grids.ucs.indiana.edu/ptliupages/publications/HPCandApacheBigDataFinal.pdf
Presentation at the AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013
Additional related material at: http://wiki.knoesis.org/index.php/Smart_Data
Related paper at: http://www.knoesis.org/library/resource.php?id=1903
Abstract: We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. We organize our research around the five V's of Big Data, where four of the Vs are harnessed to produce the fifth V - value. To handle the challenge of Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle the challenge of Variety, we resort to the use of semantic models and annotations of data so that much of the intelligent processing can be done at a level independent of heterogeneity of data formats and media. To handle the challenge of Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize new concepts, entities and facts. To handle Veracity, we explore the formalization of trust models and approaches to glean trustworthiness. The above four Vs of Big Data are harnessed by the semantics-empowered analytics to derive Value for supporting practical applications transcending physical-cyber-social continuum.
Data Science Innovations is a guest lecture for the Advanced Data Analytics (an Introduction) course at the Advanced Analytics Institute at University of Technology Sydney
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
Maximizing the value of data, computing, data science in an academic medical center, or 'towards a molecularly informed Learning Health System. Given in October at the University of Florida in Gainesville
Microsoft: A Waking Giant in Healthcare Analytics and Big DataDale Sanders
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
Microsoft: A Waking Giant In Healthcare Analytics and Big DataHealth Catalyst
In 2005, Northwestern Memorial Healthcare embarked upon a strategic Enterprise Data Warehousing (EDW) initiative with the Microsoft technology platform as the foundation. Dale Sanders was CIO at Northwestern and led the development of Northwestern’s Microsoft-based EDW. At that time, Microsoft as an EDW platform was not en vogue and there were many who doubted the success of the Northwestern project. While other organizations were spending millions of dollars and years developing EDW’s and analytics on other platforms, Northwestern achieved great and rapid value at a fraction of the cost of the more typical technology platforms. Now, there are more healthcare data warehouses built around Microsoft products than any other vendor. The risky bet on Microsoft in 2005 paid off.
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
In this context, Dale will talk about:
His up and down journey with Microsoft as an Air Force and healthcare CIO, and why he is now more bullish on Microsoft like never before
A quick review of the Healthcare Analytics Adoption Model and Closed Loop Analytics in healthcare, and how Microsoft products relate to both
The rise of highly specialized, cloud-based analytic services and their value to healthcare organizations’ analytics strategies
Microsoft’s transformation from a closed-system, desktop PC company to an open-system consumer and business infrastructure company
The current transition period of enterprise data warehouses between the decline of relational databases and the rise of non-relational databases, and the new Microsoft products, notably Azure and the Analytic Platform System (APS), that bridge the transition of skills and technology while still integrating with core products like Office, Active Directory, and System Center
Microsoft’s strategy with its PowerX product line, and geospatial analysis and machine learning visualization tools
BIMCV, Banco de Imagen Medica de la Comunidad Valenciana. María de la IglesiaMaria de la Iglesia
Según Hal Varian (experto en microeconomía y economía de la información y, desde el año 2002, Chief Economist de Google) “En los próximos años, el trabajo más atractivo será el de los estadísticos: La capacidad de recoger datos, comprenderlos, procesarlos, extraer su valor, visualizarlos, comunicarlos serán todas habilidades importantes en las próximas décadas. Ahora disponemos de datos gratuitos y omnipresentes. Lo que aún falta es la capacidad de comprender estos datos“.
Enterprise Analytics: Serving Big Data Projects for HealthcareDATA360US
Andrew Rosenberg's Presentation on "Enterprise Analytics: Serving Big Data Projects for Healthcare" at DATA 360 Healthcare Informatics Conference - March 5th, 2015
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
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.
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
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.
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
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
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
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
58.
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
63. รูปแบบของเครื่องมือที่ใช้
• AI as an application
• AI as a service
• AI libraries for developers
• AI Studio for developers
• AI Infrastructure management
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/
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
77.
78.
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