With the successful adoption of cloud-based services and the increasing capabilities of smart connected/wireless devices, the software and consumer electronics industries are turning towards innovating solutions within the Internet-of-Things (IoT) to offer consumers (and enterprises) smart solutions that take the dynamics of the real-world into consideration.
The vision is to bring the awareness of what happens in the real-world, how people live and how smart devices operate in the real world into the view and control of the digital world. Here the digital world is the totality of the Internet, the Web, and the private and public cloud services.
In this session, we will look at key technical trends and their increasing interdependency in the areas of real-world Sensing, Perception, Machine Learning, Context-awareness, dynamic Trust Determination, Semantic Web and Artificial Intelligence which are now enabling ambient intelligence and driving the emergence of Intelligence Systems within the Internet of Things. We will also look at the challenges that such interdependencies expose, and the opportunities that their solutions offer to the industry.
Ambient Intelligence perspective from IoT insightPrasan Dutt
This presentation was given to National Institute of Technology, Tiruchirapally (NITT) during Version'16 which is an all India MCA meet. The theme of the meet was Ambient Intelligence which was termed as WITURA by organizing team.
(There is not any copyright violation intended in this slide and purely intended for educational purpose. )
Ambient intelligence is an emerging discipline that brings intelligence to our everyday environments and makes those environments sensitive to us. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, pervasive computing, and artificial intelligence.
Ambient Intelligence perspective from IoT insightPrasan Dutt
This presentation was given to National Institute of Technology, Tiruchirapally (NITT) during Version'16 which is an all India MCA meet. The theme of the meet was Ambient Intelligence which was termed as WITURA by organizing team.
(There is not any copyright violation intended in this slide and purely intended for educational purpose. )
Ambient intelligence is an emerging discipline that brings intelligence to our everyday environments and makes those environments sensitive to us. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, pervasive computing, and artificial intelligence.
Ambient Intelligence (AmI) refers to a vision of the future information society where intelligent interfaces enable people and devices to interact with each other and with the environment. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, pervasive computing, and artificial intelligence. Because these contributing fields have experienced tremendous growth in the last few years, AmI research has strengthened and expanded. Because AmI research is maturing, the resulting technologies promise to revolutionaries daily human life by making people's surroundings edible and adaptive.
Pervasive computing is the growing trend towards embedding microprocessors in everyday objects so they can communicate information.
The words pervasive and ubiquitous mean "existing everywhere”.
It is also called “Ubiquitous computing”
Distributed Computing “two or more computers communicating and co-ordinating by message passing”.
Its also called as "Parallel computing”.
Ubiquitous computing is one of the most prodiously growing topic which will be covering all facets of life.In the course of ordinary activities, someone "using" ubiquitous computing engages many computational devices and systems simultaneously, and may not necessarily even be aware that they are doing so. This model is considered an advancement from the older desktop paradigm. More formally, ubiquitous computing is defined as "machines that fit the human environment instead of forcing humans to enter theirs".
Pervasive Computing - Let us Pervade our FutureKarthikeyan V
Pervasive Computing or Ubiquitous computing is one of the latest trends in computing. Get to know the principles, mechanism and the possible applications of pervasive computing. Come, let us pervade our future.
Pervasive computing also known as Ubiquitous computing (ubicomp) is a concept in software engineering and computer science where computing is made to appear everywhere and anywhere. Eg:laptop computers, tablets and terminals in everyday objects such as a fridge or a pair of glasses.
•It is also termed as ambient intelligence, Ubiquitous computing ,everyware,physical computing, the Internet of Things, haptic computing, and 'things that think’.
The evolution of pervasive computing towards a Web of ThingsAndreas Kamilaris
This presentation observes and describes the evolution of pervasive computing towards a global, real-time digital web of physical objects and everyday things. Applications in real life are presented, together with future challenges and projections.
Ambient Intelligence (AmI) refers to a vision of the future information society where intelligent interfaces enable people and devices to interact with each other and with the environment. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, pervasive computing, and artificial intelligence. Because these contributing fields have experienced tremendous growth in the last few years, AmI research has strengthened and expanded. Because AmI research is maturing, the resulting technologies promise to revolutionaries daily human life by making people's surroundings edible and adaptive.
Pervasive computing is the growing trend towards embedding microprocessors in everyday objects so they can communicate information.
The words pervasive and ubiquitous mean "existing everywhere”.
It is also called “Ubiquitous computing”
Distributed Computing “two or more computers communicating and co-ordinating by message passing”.
Its also called as "Parallel computing”.
Ubiquitous computing is one of the most prodiously growing topic which will be covering all facets of life.In the course of ordinary activities, someone "using" ubiquitous computing engages many computational devices and systems simultaneously, and may not necessarily even be aware that they are doing so. This model is considered an advancement from the older desktop paradigm. More formally, ubiquitous computing is defined as "machines that fit the human environment instead of forcing humans to enter theirs".
Pervasive Computing - Let us Pervade our FutureKarthikeyan V
Pervasive Computing or Ubiquitous computing is one of the latest trends in computing. Get to know the principles, mechanism and the possible applications of pervasive computing. Come, let us pervade our future.
Pervasive computing also known as Ubiquitous computing (ubicomp) is a concept in software engineering and computer science where computing is made to appear everywhere and anywhere. Eg:laptop computers, tablets and terminals in everyday objects such as a fridge or a pair of glasses.
•It is also termed as ambient intelligence, Ubiquitous computing ,everyware,physical computing, the Internet of Things, haptic computing, and 'things that think’.
The evolution of pervasive computing towards a Web of ThingsAndreas Kamilaris
This presentation observes and describes the evolution of pervasive computing towards a global, real-time digital web of physical objects and everyday things. Applications in real life are presented, together with future challenges and projections.
This presentation given in the un-conference on technology and art is a first glance into new research on taking event-driven technology to create a new type of literature.
In this presentation, Adithya introduces the topic of IoT and associated trends. His interest area lies in application of IoT to do proactive fulfillment, reduce downtime through remote monitoring in the healthcare space.
Artificial Intelligence: Case-based & Model-based ReasoningThe Integral Worm
This presentation covers case-based and model-based reasoning for artificial intelligence. Topics covered are as follows: case-based reasoning, case-based reasoning components; case base, retriever, adapter, refiner, executor, and evalutator; and model-based reasoning.
Dynamic Semantics for the Internet of Things PayamBarnaghi
Ontology Summit 2015 : Track A Session - Ontology Integration in the Internet of Things - Thu 2015-02-05,
http://ontolog-02.cim3.net/wiki/ConferenceCall_2015_02_05
A wide assortment of technologies that are advancing at exponential rates and beginning to converge. They are enabling entrepreneurs to do what was once only previously possible for governments and large corporations. These exponential technologies will help us solve many of humanity's grand challenges, including energy, education, water, food, and health.
Join us for a discussion on what these technologies are and how they affect your world.
Finding a scalable open-source IoT framework that reliably and securely connects your devices to the cloud while fitting your business needs, not dictating them, turns out to be a little more challenging than it first looks.
For a business or professional service, an IoT system needs to be able to offer four things
1) Scalability
Be able to scale the solution in a manner that doesn't have operating costs/bandwidth run out of control.
2) Be secure
Operate in a secure environment that prevents the system losing date or being hi-jacked.
3) Use open-standards throughout
Be based on open-source standards to avoid proprietary lock-in and allow the business to control its own destiny, contribute, collaborate, partner or quickly and easily find help in the community, if required.
4) Manage & Inter-operate
The framework must allow that allow remote day-to-day device management and interoperability with other sensors & systems
Find out more about how the Creator IoT Framework meets these challenges
We are linear thinkers . We project where things are going to be by looking at the past .
We are moving from Scarcity >>> Abundance and we need to adapt our mindset to the new reality !
What are the key changes we need in our mindset to succeed in the new era of exponentially growing technologies ?
Data Governance and the Internet of ThingsDATAVERSITY
Several years back there were already more devices connected to the internet than people. It is estimated that more than 20 billion devices will be connected by 2020 and that number will never fall. Connecting to the internet implies the transfer of data. The numbers of devices and what they transfer imply a lot of data. Who is governing all of this data?
Join Bob Seiner for this month’s installment of Real-World Data Governance to expand your appreciation of the data issues that pertain to the Internet of Things (IoT). You may be surprised how much of what you already know about data governance applies to governing this new definition, production and use of data.
In this webinar Bob will talk about:
•Clear Description of IoT Focused on the data
•Addressing Data Management Concerns
•Applications of IoT Data
•Dimensions of IoT Data Processes and Quality
•Risk Associated with Interoperability
Governance and IoT Cyber Risks - presented at Defcon-OWASP Lucknow, IndiaDinesh O Bareja
Internet (or Cyber) Governance has a long way to go and is presently fraught with confusion - this being a global phenomenon. Then there is the Internet of Things coming up at top speed which means that we have to face up to the risks that come with the convenience ! A solution for governance and some insight into the IoT risks were presented at the Defcon-OWASP Conference in Lucknow (India) on February 22, 2015
Complete Update of All Exponential Technologies & Singularity cases and its I...Yuri van Geest
Recent cases and examples of different Exponential Technologies from Singularity University and MIT in field of biotech, nanotech, neurotech, AI, robotics, drones, 3D printing, sensors, Quantified Self, Internet of Things, solar energy, mobile, crowdsourcing.
Yuri van Geest: Exponential Organizations - The New Normalsinnerschrader
The world is changing exponentially due to technology, Singularity and globalization. We see the emergence of a new kind of organization: the exponential organization. This is complete reinvention of all building blocks of a classic, linear organization. Why is it here today? Why is it so successful? What is it? How does it organize to scale? How will exponential organizations evolve into blockchain startups (DAOs)? Learn practical insights from not only startups like Slack, Uber, Xiaomi, Airbnb, Waze, Local Motors, Quirky, Github and Tesla but also large organizations like Google, Amazon, Haier and General Electric.
If your organization was built in the 20th century it is doomed to fail in the 21st century. Why? Nobody uses s a mobile phone of 15 years old to survive in todays' business world but we do use organizational models which are 100 years old in todays' exponential era. This does not make sense and explains disruption in each and every vertical market.
*talk at NEXT15*
Semantic, Cognitive, and Perceptual Computing – three intertwined strands of ...Amit Sheth
Keynote at Web Intelligence 2017: http://webintelligence2017.com/program/keynotes/
Video: https://youtu.be/EIbhcqakgvA Paper: http://knoesis.org/node/2698
Abstract: While Bill Gates, Stephen Hawking, Elon Musk, Peter Thiel, and others engage in OpenAI discussions of whether or not AI, robots, and machines will replace humans, proponents of human-centric computing continue to extend work in which humans and machine partner in contextualized and personalized processing of multimodal data to derive actionable information.
In this talk, we discuss how maturing towards the emerging paradigms of semantic computing (SC), cognitive computing (CC), and perceptual computing (PC) provides a continuum through which to exploit the ever-increasing and growing diversity of data that could enhance people’s daily lives. SC and CC sift through raw data to personalize it according to context and individual users, creating abstractions that move the data closer to what humans can readily understand and apply in decision-making. PC, which interacts with the surrounding environment to collect data that is relevant and useful in understanding the outside world, is characterized by interpretative and exploratory activities that are supported by the use of prior/background knowledge. Using the examples of personalized digital health and a smart city, we will demonstrate how the trio of these computing paradigms form complementary capabilities that will enable the development of the next generation of intelligent systems. For background: http://bit.ly/PCSComputing
This is a version of series of talks given at NCSA-UIUC's director seminar, IBM Almaden, HP Labs, DERI-Galway, City Univ of Dublin, and KMI-Open University during Aug-Oct 2010 (replaces earlier keynote version). It deals with couple of items of the vision outlined at http://bit.ly/4ynB7A
A video of this presentation: http://www.ncsa.illinois.edu/News/Video/2010/sheth.html
Link to this talk as http://bit.ly/CHE-talk
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
Artificial Intelligence Research Topics for PhD Manuscripts 2021 - PhdassistancePhD Assistance
Imagine a world where knowledge isn’t limited to humans!!! A world in which computers will think and collaborate with humans to create a more exciting universe. Although this future is still a long way off, Artificial Intelligence has made significant progress in recent years. In almost every area of AI, such as quantum computing, healthcare, autonomous vehicles, the internet of things, robotics, and so on, there is a lot of research going on. So much so that the number of annual Published Research Papers on Artificial Intelligence has increased by 90% since 1996.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/2Sdlfn4
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
In the last decade, workplaces have started to evolve towards digitalisation. In the future people will work in digitally connected environments where personalisation is enabled, collaboration is improved and data sharing and information management are automated. Ultimately, these future workplaces will provide context-aware artificial intelligence (AI) and decision support that leverage both localised information and broader community knowledge whenever needed.
Konica Minolta - Artificial Intelligence White PaperEyal Benedek
The evolution of artificial intelligence in the workplace
Since the first appearance of the words “artificial intelligence” more than 60 years ago, our imaginations have been sparked. Imagine creating computers that simulate human intelligence.
AI has the potential to profoundly influence our lives, perhaps to the point when our world can be better understood and even predicted. In workplaces we can develop systems through which AI may evolve. And Konica Minolta is progressing with the concept of intelligent hubs which will provide businesses with insight, support and greater collaboration.
By combining our core technologies with transformative solutions in the digital workplace, we’re evolving to become a problem-solving digital company creating new value for people and society.
Smart Data for you and me: Personalized and Actionable Physical Cyber Social ...Amit Sheth
Featured Keynote at Worldcomp'14, July 2014: http://www.world-academy-of-science.org/worldcomp14/ws/keynotes/keynote_sheth
Video of the talk at: http://youtu.be/2991W7OBLqU
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is human health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information, etc.). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will forward the concept of Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If I am an asthma patient, for all the data relevant to me with the four V-challenges, what I care about is simply, “How is my current health, and what is the risk of having an asthma attack in my personal situation, especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city. I will present examples from a couple of these.
Artificial intelligence is already all around you, from web search to video games. AI methods plan your driving directions, filter your spam, and focus your cameras on faces.
Artificial Intelligence Research Topics for PhD Manuscripts 2021 - PhdassistancePhD Assistance
Imagine a world where knowledge isn’t limited to humans!!! A world in which computers will think and collaborate with humans to create a more exciting universe. Although this future is still a long way off, Artificial Intelligence has made significant progress in recent years. In almost every area of AI, such as quantum computing, healthcare, autonomous vehicles, the internet of things, robotics, and so on, there is a lot of research going on. So much so that the number of annual Published Research Papers on Artificial Intelligence has increased by 90% since 1996.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/2Sdlfn4
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
JIMS IT Flash , a monthly newsletter-An Initiative by the students of IT Department, shares the knowledge to its readers about the latest IT Innovations, Technologies and News.Your suggestions, thoughts and comments about latest in IT are always welcome at itflash@jimsindia.org.
Visit Website : http://jimsindia.org/
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The Internet of Things, Ambient Intelligence, and the Move Towards Intelligent Systems
1. The Internet of Things,
Ambient Intelligence and the
Move Towards Intelligent
Systems
Santa Clara (IEEE Region 6) Event
September [28 or 29], 2012
Dr. George Vaněček, Jr.
Senior Principal Scientist, FutureWei Technologies, Inc.
2. Our Next 40 Minutes…
1. From Internet to Internet of Things (IoT)
and Beyond
2. Growing Intelligence in the IoT
3. Something about Sensing, Real-world
Perception, and Context-Awareness
4. The Tools of the trade
5. Key Takeaways
6. Key Technologies
2
3. It has been a great ride
Let’s celebrate the Internet and the Web,
and everyone who had some small part in
them, on changing the world for the better!
3
http://www.unc.edu/~unclng/Internet_History.htm
Now get ready for a far stranger ride, we
never imagined only a few decades ago.
4. Looking ahead to Future Internet/Web,
more Challenges remain…
Ubiquitous personalization and individualization
– Global Identity, Trust, Presence, Ownership, …
Portability between Silos
– Of Identities, Profiles, and Content
Information Overload
Ability to scale beyond human-only usage
– More addresses, storage/transport capacity, devices, …
Machines Understanding of Data
– Unstructured, Semi-structured and Structured data
Interoperability with
– Open Data, Formats, and APIs
Context/Situation/Intent Awareness
Digital-world is becoming aware of the
physical-world: Internet of all things
5. Here comes the Internet of Things
The Internet will connect billions of people through
mobile and embedded smart devices.
Real-time communication and the accessibility to
any information on-line will enrich people and
machines; …
the Internet will connect everyday things integrated
into people’s every day lives.
– More equipment will be connected to the Internet than
people by a factor of 8 to 1 (50 Billion by 2020).
IoT will integrate many industry verticals
(e.g., healthcare, energy, transportations) into smart
*/city/building/home environments.
IoT will be centric to people’s needs and
every day existence.
Ambient Intelligence will expand.
6. IoT will bring us the
Smart Connected World
Smart
Homes
Smart
Buildings
Smart
Cars
Smart
Phones
Smart
City
Smart
Grid
Smart
Community
Smart
Highways
7. The rise of Ambient Intelligence:
The Personalization, Socialization, and
Real-world Awareness of the Internet
7
Today
ManyMany
(Personal)(Personal)
Computers forComputers for
OneOne PersonPerson
Size
Number
Computer
One
Computer
for Many
People
Tomorrow
ManyMany
(Hidden)(Hidden)
ComputersComputers
forfor ManyMany
PeoplePeople
One Computer
per One Person
8. So, What is Ambient Intelligence?
8
AmI refers to electronic environments that are
sensitive and responsive to the presence of people
“In an Ambient Intelligence
world, devices work in
concert to support people in
carrying out their everyday
life activities, tasks and
rituals in easy, natural way
using information and
intelligence that is hidden in
the network connecting
these devices.” Source: Wikipedia
Source: Wikipedia
9. The Challenge for the Industry
Create an infrastructure and middleware to
•enable heterogeneous devices to
interoperate,
•to perform assigned tasks, and
•be able to sense, perceive and react
appropriately
•with minimal human intervention
(Organic Computing).
10. From Existing to Intelligent
(Organic Computing) Systems
Need intelligence in systems that
dynamically adapt to their environment
and tasks, and are
Self-Configuring,
Self-Describing/Explaining,
Self-Healing,
Self-Protecting,
Self-Organizing,
Context-aware, and
Reactive and Proactive.
11. 2012: 1.2 Zetabytes (1.2 x 1021
)
Will grow 44 fold in the next Decade
Understanding Data is the
Starting Problem
Digital World: Deluge of Data
– Structured (some)
– Semi-structured (a bit more)
– Unstructured (huge amounts of)
Audio, Video, text, PPT, Doc, …
Physical World: Deluge of Sensor Data
– Mostly Unstructured
Source: IDC
12. Increasing Intelligence in Systems
“Intelligent Systems will exist in environments they
sense and perceive, and from which they learn and
continually act to achieve their objectives.”
1. sense the real-world environments,
2. perceive the world using world
models,
3. adapt to different environments
and changes,
4. learn and build knowledge, and
5. act to control their environments.
They are computational systems that behave intelligently and rationally, to
13. AmI Pipeline to WisdomUnderstanding
Ability to Act
Information
Understand
Relationships
Knowledge
Understand
Patterns
Wisdom
Understand
Principles
Going from “What happened” to “What will happen”
Sense
Pervasive
sensing of
the
Environment
Perceive
Ability to
infer reln’s
from data
Act
Take an
action on
behalf of
the
humans
Learn
Learn form
patterns
and events
Adapt
Adapt
based on
needs
Data
Actuators and
On-Line Services
Anticipate
React
Execute
Unstructured, Semi-Struct., and
Struct’ed On-line and Sensor Data
14. Modern AI Explosion
1950 1980 1990 2010
AIPromisesandExpectations
1970
Decision Trees
Finite State Machines
Symbolic Reasoning
Logic Programming
General Problem Solver
Lisp Programming Language
Fraud Detection
Spam Filters
Search Engines
Natural Language Processing
Biometric, face/fingerprint detection
Robotic and Machine Translation
Speech Understanding
Business Intelligence
Collective Intelligence
Data Mining
Autonomous Systems
Machine Learning
Predictive Analysis
Pattern Matching
Sensing, Perception
Real-world Modeling
Behavior Modeling
…
Prolog Declarative Language
Neural Networks
Knowledge Representations
Commercial Expert Systems
Before 1990, classic AI goals were to
surpass human intelligence in
Language,
Reasoning, &
Abstract Problem Solving
15. Big Data and Data Science
Challenges
The challenges that are being solving for are:
– How to handle large data volumes?
– What data to store?
– How to analyze it?
– How to find significant data?
– How to use it to best advantage?
– How to visualize the data?
– When to analyze the data and when to apply its results?
Big data focuses primarily on statistically finding patterns,
trends, risks, and meaning in large amounts of collected
(unstructured) data from access logs, transactions, tweets,
emails, blogs, etc.
MapReduce
Hadoop
MongoDB
16. Machine Learning
Supervised Learning Algorithms
– K-Nearest Neighbor
– Naïve Bayes
– Support Vector Machines
– Decision Tree Induction
– Etc.
Unsupervised Learning Alg.s
– K-Means
– Expectation Maximization
– Etc.
16
ML refers to a statistical suite of algorithms and
paradigms for finding patterns in data.
Training
Set {x}
Training
Algorithm:
X’
D
{[Patterni,Di]}
{D}
{}
17. Machine Learning and AI Join Forces
Build systems that learn about self and environment
Create Situated Autonomous Decision Systems
– in dynamic environments over extended time
entrusted to handle complex tasks
Teach autonomous systems how to handle time,
change, and event streams.
Most systems do not handle time and changes well
Build Agents that exhibit life-long Machine Learning
(ML) rather than ML algorithms that learn one thing
only.
Create an interchangeable world knowledge for
Intelligent Systems.
Source: AAAI-96
From “What happened” to “What will happen”
18. AmI Requires ways to Model and
Represent the Worlds
with the understanding of
– Global Identities of people, places and
things,
– Multiple contexts,
– Situations,
– Intents,
– Trust based on evidence, and
– Behaviors and Habits,
Time and Histories.
18
19. Taxonomies: explicit hierarchical specifications
of related categories/entities and rules to
differentiate them
Domain-Specific Ontologies: descriptions
of entities and their relationships
AmI Means for Building Models
of the Real-World
19
Knowledge Bases: Graph-based entity-
relational knowledge repositories
Software Agents: Autonomous programs
sensing and performing various duties.
Engines that can analyze, reason, plan, predict
Semantic Web…
20. Semantic Web, the Web of Data and
the Meaning of Data
When the web can
understand content, it
will then better satisfy
people and machines
requests
A Web where the
context of content is
defined by data
A Web capable of
reading and
understanding
content and context
Tim Berners-Lee
Around 2006, the Semantic
Web emerged as an evolution
to Web 2.0 to be
21. Refocusing on Context-AwarenessRefocusing on Context-Awareness
Microformats
data embedded within XHTML
Metadata
statements about the
world in a manner that
machines can understand
unambiguously
When authors create
content, they will need
to define the context
that links the content
to their target
audience
Cross-linked and Defined
Data Models
Resource Description Framework
Defines and describes data and
Relations among them
Content
Tags
When machines
generate content, they
will also need to define
the context
Ontologies
OWL
Web Ontology Language
22. “The Internet is a Changing”
Key Takeaways
– The digital world is becoming more aware of
the real world
– Systems are becoming more intelligent and
autonomous
– Everyday things are getting connected
– Technology and computing is becoming
transparent
– Rapid innovation is driving major changes in
the IoT
– Ambient Intelligence and Organic Computing
is gaining Industry focus22
23. Key Technologies to Follow
Semantic Web
– Meta Data
– Ontologies and
Taxonomies
HTML5 and D3js
RTW and WebSockets
Data Fusion
– Processing
unstructured/semi-
structured Data
Machine Learning
Information/Knowledge
Repositories
– NoSQL Databases
– Graph Databases
– Knowledge Bases
Dynamic and Post-
Functional Languages
– Scala, Python,
Java 8, Groovy,
Haskell, Lisp,
Javascript, …
23
Course Description :
With the successful adoption of cloud-based services and the increasing capabilities of smart connected/wireless devices, the software and consumer electronics industries are turning towards innovating solutions within the Internet-of-Things (IoT) to offer consumers (and enterprises) smart solutions that take the dynamics of the real-world into consideration.
The vision is to bring the awareness of what happens in the real-world, how people live and how smart devices operate in the real world into the view and control of the digital world. Here the digital world is the totality of the Internet, the Web, and the private and public cloud services.
In this session, we will look at key technical trends and their increasing interdependency in the areas of real-world Sensing, Perception, Machine Learning, Context-awareness, dynamic Trust Determination, Semantic Web and Artificial Intelligence which are now enabling ambient intelligence and driving the emergence of Intelligence Systems within the Internet of Things. We will also look at the challenges that such interdependencies expose, and the opportunities that their solutions offer to the industry.
So, where this all heading and why?
Scalability
George, can you reword this with simpler words
This is the How.
Xerox now staff its 50K call center jobs using based on algorithms that try it separate creative from inquisitive people, and hire only the creative one. [source: WSJ 2012, Meet the new Boss> Big Data]