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The Internet of Things, Ambient Intelligence, and the Move Towards Intelligent Systems

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.

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The Internet of Things, Ambient Intelligence, and the Move Towards Intelligent Systems

  1. 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. 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. 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 Now get ready for a far stranger ride, we never imagined only a few decades ago.
  4. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
  24. 24. 24 Thank You Santa Clara, CA