The Era of Cognitive

Computing

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…

Google Acquires

Deep Mind

IBM Watson

Why Is Smart Required for IoT

and Cloud?

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

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

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

How is Smart Implemented Now

■ Advanced Search – Genetic, Graph Theory

■ Inferencing (Deductive, Inductive)

■ Fuzzy Reasoning

■ Optimization

■ Learning

■ Interpreting and Language

■ Negotiation

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

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”

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

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

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

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)

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

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

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

Architecture Value

• Profitability

• Constituent Value

• Reuse

• Grow Market Size

• Grow Market Quality

What is “creates value”?

What is Good?

suitable or efficient for a purpose

beneficial or advantageous

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

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

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

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

The use, disclosure, reproduction, modification, transfer, or transmittal of this work without the

written permission of IASA is strictly prohibited. © IASA 2009

Skill Taxonomy

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

Career Path

End Module