The Era of Cognitive Computing8wriz40sy4t1ph08pf5vo1dp-wpengine.netdna-ssl.com/wp-content/up… ·...
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The Era of Cognitive
Computing
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Why Now?
We’ve been hearing about this forever:
• Fuzzy Systems
• Artificial Intelligence
• Natural Language Processing
These things:
• Gaming - $64 billion
• Search – indexed knowledge
• SaaS – app marketplace
• Devices – compute everywhere
• Also…
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Google Acquires
Deep Mind
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IBM Watson
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Why Is Smart Required for IoT
and Cloud?
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Cloud
■ The umbrella term for anything available over a network
■ Relevant attributes which typify and classify architectures
include
■ Public or private
■ Virtualized or non-virtualized
■ Service oriented or person oriented
■ Hardware oriented or platform oriented or software oriented
■ Organizationally oriented or personally oriented
■ Secure or unsecure
■ Paid or free
■ Paid by quality attribute or paid by operational attribute
■ Guaranteed or unguaranteed
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Internet of Things
■ Identifying all physical and virtual objects on a network
■ Relevant attributes which will typify and classify architectures
may include
■ Type of IoT identity (hardware, network, software, service,
invoker, agent, intelligent agent, independent intelligent agent,
provocateur)
■ Size or scope of object (molecular -> planetary)
■ Data type/volume consumption/production
■ Power consumption/production
■ Location and Mobility
■ Object interaction power in virtual, physical or both
■ Intention and Autonomy
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Proposed Hierarchy of IoT
Identities
■ Provocateur - Intelligent agent with intention (human level)
■ Independent Intelligent Agent - Intelligent agent acting without permission
■ Intelligent Agent – Agent with a degree of reasoning capacity
■ Agent – Invoker which changes addresses in some way
■ Invoker – Service which calls other services
■ Service – Software object which returns a complex response
■ Software – Network object which returns a simple response
■ Network – An object which is addressable over a network
■ Hardware – An object which is identifiable over a network
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How is Smart Implemented Now
■ Advanced Search – Genetic, Graph Theory
■ Inferencing (Deductive, Inductive)
■ Fuzzy Reasoning
■ Optimization
■ Learning
■ Interpreting and Language
■ Negotiation
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Searching for Information
■ Information has to be constructed from data and context
■ There is more data and information in the world than we can
process
■ Intelligent search is key to our ability to make use of
information
■ Common applications: business intelligence, lifestyle
optimization, interest optimization
■ This is what Watson is really aimed at - semantic interaction of
people and systems
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The Rules We Live By
■ Most companies have large numbers of commonly modified
rules
■ Inferencing allows us to
■ deduce new information within context (forward-chaining)
■ induce information from existing data (backward-chaining)
■ Common Applications: Insurance rates and converage, retail
pricing and discounts, purchase decisions, lifestyle choices
■ “If the train is late let me sleep in”
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Fuzzy Reasoning and Controllers
■ Humans and business work on ‘fuzzy definitions’ which is simply that most things are both true and not true
■ “It is cold in Sweden” may be true to a Texan but not an Eskimo!
■ “A cup is also a bowl” can be more or less true
■ “That hotel is extremely expensive” for me but Bill Gates?
■ Allows our devices to be more precise and selective in decision making and reasoning
■ “Pre-heat the car when it is very cold”
■ “We buy very high quality business supplies”
■ Common Applications: Energy utilization, mechanical controllers, human definitional input
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Optimization
■ Business processes, graph navigation, optimal path traversal,
and business integration all involve process optimizations
■ Multi-processes integration beyond the simplicity of a single
service (physical or virtual) control much of our lives
■ Utilization of embedded process engines and optimization
allows for maximum flexibility of physical and virtual agents
■ Common Applications: multi-partner business transactions,
automated delivery systems, personal travel itineraries, multi-
device automation
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Learning
■ More and more data and choice is available to system software
■ As automation and autonomy become ubiquitous training in desired outcomes is necessary for personal and business
■ The vast amount of data and information requires grouping, characterizing and classifying
■ Neural networks and decision trees
■ Common applications: Food, travel and personal preferences, natural language processing, optimal energy input/output, security threat detection
■ Welcome Azure ML
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Thing to Thing Communication
■ Language, dialect, grammar, vocabulary and pronunciation are all relevant in IoT communications and configuration
■ Knowledge and language ontology and dictionary will be essential to self-configuration (and therefore adoption)
■ This may be the single most difficult task in the IoT
■ Even humans struggle with this constantly
■ ‘Molecular’ data element combinations are not solidified (what is an address, a name, a birthday)
■ Common applications: Thing configuration and communication, business analytics, service orchestration, personal identity management (pay for use)
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Negotiation
■ As systems begin to represent us there is more and more
conflict
■ “What is the best price we can get for pencils for employees”
■ Using negotiation techniques to avoid conflict with game
theory
■ Common applications: Device resource allocation and
utilization, purchasing
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Considering Value and Risk
■ Value to Who?
■ Individuals
■ Governments and NGOs
■ Vendors and Service
Integrators
■ For Profit – non-vendor
■ What type of Value
■ Lifestyle|Social Value
■ Financial Value
■ Customer|Operational
Value
■ Societal|Human Value
■ Risk to Who?
■ Individual
■ Corporation
■ Governments
■ What type of Risk?
■ Physical
■ Financial
■ Societal
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How Smart Becomes Value
■ There is a world of ‘new’ objects to sell to the world
■ There is an unlimited number of ways to incorporate new
inventions into multiple channels, services and ‘products’
■ Learning about your customers and partners
■ Dynamically allocating resources and processes
■ Optimized pathing
■ Planning and forecasting
■ Configuration management and ease of use
■ Human interaction and reasoning
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Architecture Value
• Profitability
• Constituent Value
• Reuse
• Grow Market Size
• Grow Market Quality
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What is “creates value”?
What is Good?
suitable or efficient for a purpose
beneficial or advantageous
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What does Smart Mean Tomorrow
■ We must begin to consider systems as more than software
services
■ Autonomy – the degree to which systems can act without
permission
■ Power (to influence) – the amount of influence or size of outcomes a
system can achieve
■ Resources (to command and use) – the size and makeup of objects
a system may use
■ Motivation – as systems gain more power and autonomy we will
need to understand
■ Combat – when systems with autonomy, power and resources
disagree about outcomes
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The use, disclosure, reproduction, modification, transfer, or transmittal of this work without the written permission
of IASA is strictly prohibited. © IASA 2014
Accomplishments
■ More than 2,500 individuals trained globally in 2014
■ More than 2000 individuals certified Y2D
■ Core courses updated to version 4.0
■ Capabilities Guidebook project launched (http://www.iasaglobal.org/iasa/Capabilities_Guidebook.asp)
■ CITA-S certification launched
■ Solution and Enterprise course and certification launched
■ Major companies standardizing to Iasa skills & certifications: Avanade, AstraZeneca, Volvo, Citrix, Dell, Costco, Microsoft, TMobile
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Iasa business model
The business model canvas
OFFER
CHANNELS
RELATIONSHIPS Customers
REVENUE STREAMSCOST CENTRES
KEY
PARTNER
KEY
RESOURCES
KEY
ACTIVITIES
Source: Canvas by businessmodelgeneration.com
Architects
Large Companies
Career Growth
Problem Solving
Giving Back
Personal Network
Knowledge Resource
Events
Subscriptions
MembershipSponsorship Education
People Events
Membership
Education
Events
Communities
Thought leaders
Communities
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Iasa Strategy Map
Programs Measures
Financial Community
Membership
Education
Chapter Levels
Member Sat
Job Opportunities
Customer Membership Drive
Chapter GEM
# Members
Member Programs
Process Community 3.0
Program Development
Techniques
# new programs
People, Knowledge GEM Training Guide
Chapter Leader
Contribution to
value
Member
ValueGrow
Revenue
Improve
Quality
Grow
Membership
Increase
Program
Participation
Content
Development
Program
Development
Community
Development
TechnologyPeople
Training
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The use, disclosure, reproduction, modification, transfer, or transmittal of this work without the
written permission of IASA is strictly prohibited. © IASA 2009
Skill Taxonomy
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Engagement
EnterpriseFinance Sales LOB IT
Business Capability
Data Center
Software Architect
Software
Architect
Software
Architect
Business
Architects
Information
Architects
Infrastructure
Architects
Enterprise
Architects
Interns
Interns
Interns
Interns
Interns
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Career Path
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End Module