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

Santa Clara (IEEE Region 6) EventSeptember [28 or 29], 2012

Dr. George Vaněček, Jr.Senior Principal Scientist, FutureWei Technologies, Inc.

Our Next 40 Minutes…

1. From Internet to Internet of Things (IoT)and Beyond

2. Growing Intelligence in the IoT3. Something about Sensing, Real-world

Perception, and Context-Awareness4. The Tools of the trade5. Key Takeaways6. Key Technologies

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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!

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http://ww

w.unc.edu/~unclng/Internet_H

istory.htm

Now get ready for a far stranger ride, we never imagined only a few decades ago.

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 OverloadAbility 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 AwarenessDigital-world is becoming aware of the

physical-world: Internet of all things

Here comes the Internet of ThingsThe 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.

IoT will bring us theSmart Connected World

Smart Homes

Smart Buildings

Smart Cars

Smart Phones

Smart City

Smart Grid

Smart Community

Smart Highways

The rise of Ambient Intelligence: The Personalization, Socialization, and Real-world Awareness of the Internet

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Today

ManyMany(Personal)(Personal) Computers for Computers for OneOne Person Person

Size

Number

Computer

One Computer for Many People

Tomorrow

ManyMany(Hidden) (Hidden) ComputersComputersfor for ManyManyPeoplePeopleOne Computer

per One Person

So, What is Ambient Intelligence?

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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

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).

From Existing to Intelligent (Organic Computing) SystemsNeed intelligence in systems thatdynamically adapt to their environment and

tasks, and areSelf-Configuring,Self-Describing/Explaining,Self-Healing,Self-Protecting,Self-Organizing,Context-aware, andReactive and Proactive.

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

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, and5. act to control their environments.

They are computational systems that behave intelligently and rationally, to

AmI Pipeline to WisdomUn

ders

tand

ing

Ability to Act

Information

UnderstandRelationships

Knowledge

UnderstandPatterns

Wisdom

UnderstandPrinciples

Going from “What happened” to “What will happen”

SensePervasive sensing of

the Environment

PerceiveAbility to

infer reln’s from data

ActTake an

action on behalf of

the humans

LearnLearn form patterns

and events

AdaptAdapt

based on needs

Data

Actuators andOn-Line Services

AnticipateReact

Execute

Unstructured, Semi-Struct., andStruct’ed On-line and Sensor Data

Modern AI Explosion

1950 1980 1990 2010

AI P

rom

ises

and

Exp

ecta

tions

1970

Decision TreesFinite State MachinesSymbolic ReasoningLogic Programming

General Problem SolverLisp Programming Language

Fraud DetectionSpam Filters

Search EnginesNatural Language Processing

Biometric, face/fingerprint detectionRobotic and Machine Translation

Speech UnderstandingBusiness IntelligenceCollective Intelligence

Data MiningAutonomous Systems

Machine LearningPredictive AnalysisPattern Matching

Sensing, PerceptionReal-world ModelingBehavior Modeling

…

Prolog Declarative LanguageNeural Networks

Knowledge RepresentationsCommercial Expert Systems

Before 1990, classic AI goals were to surpass human intelligence in

Language,Reasoning, &Abstract Problem Solving

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.

MapReduceHadoopMongoDB

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}

{}

Machine Learning and AI Join Forces

Build systems that learn about self and environmentCreate Situated Autonomous Decision Systems– in dynamic environments over extended time

entrusted to handle complex tasksTeach autonomous systems how to handle time, change, and event streams.

Most systems do not handle time and changes wellBuild 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”

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

Taxonomies: explicit hierarchical specifications of related categories/entities and rules to differentiate themDomain-Specific Ontologies: descriptions of entities and their relationships

AmI Means for Building Models of the Real-World

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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…

Semantic Web, the Web of Data andthe 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 dataA 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

Refocusing on Context-AwarenessRefocusing on Context-Awareness

Microformatsdata embedded within XHTML

Metadatastatements 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 DefinedData Models

Resource Description FrameworkDefines and describes data andRelations among them

ContentTags

When machines generate content, they will also need to define the context

OntologiesOWLWeb Ontology Language

“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

Key Technologies to FollowSemantic Web– Meta Data– Ontologies and

TaxonomiesHTML5 and D3jsRTW and WebSocketsData 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, …

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Thank YouGeorge.Vanecek@gmail.comSanta Clara, CA