Develop Your AI Strategy with These Trends in Mind. - PowerPoint PPT Presentation

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Develop Your AI Strategy with These Trends in Mind.

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Develop Your AI Strategy with These Trends in Mind. Watch this ready to watch webinar to learn about the key concepts of AI and learn the organizational steps to prepare for the change inclusive of what it is happening in the corporate world to its implications. Register to watch the webinar and learn all you need to know about Artificial Intelligence: You can also check out other ready to watch Webinars through registering once through the same link given above. – PowerPoint PPT presentation

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Title: Develop Your AI Strategy with These Trends in Mind.


1
Develop Your AI Strategy with These Trends in
Mind
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Agenda
  • Key Concepts of AI
  • Trends What organizations are doing with AI
  • How AI fits into organizations IT Strategies
  • How will AI interconnect applications in the
    organizational ecosystems

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What is Artificial Intelligence?
  • What is Artificial Intelligence (AI)?
  • Using computers to solve problems
  • Or make automated decisions
  • For tasks that, when done by humans,
  • Typically require intelligence

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Limits of Artificial Intelligence
  • Strong Artifical Intelligence (A?GI)
  • Computers thinking at a level that meets or
    surpasses people
  • Computers engaging in abstract reasoning
    thinking
  • This is NOT what we have today
  • There is no evidence that we are even close to
    Strong AI
  • Weak Pattern-Based Artificial Intellige?nce
  • Computers solve problems by detecting useful
    patterns
  • Pattern-based AI is an Extremely powerful tool
  • Has been used to automate many processes today
  • Driving, language translation, cancer diagnosis,
    fraud detection
  • This is the dominant mode of AI today

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Major AI Approaches
  • Two Major AI Techniques
  • Logic and Rules-Based Approach (Still Relevant)
  • Machine Learning (Pattern-Based Approach)

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Types of AI
Deep Learning Predictive Analytics
Machine Learning
Translation Classification, Clustering
Information Extraction
Natural Language Processing (NLP)
Speech to Text Text to Speech
Speech
Interference Engine Knowledgebase
Artificial Intelligence (AI)
Expert Systems
Reduction Classical Probabilistic, Temporal
Planning, Scheduling Optimization
Reactive Machines Limited Memory Theory of Mind,
Self-aware
Robotics
Image Recognition Machine Vision
Vision
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Logic and Rules-Based AI
  • Logic and Rules-Based Approach
  • Representing processes or systems using logical
    rules
  • Top-down rules are created for computer
  • Computers reason about those rules
  • Can be used to automate processes
  • Expert Systems
  • Turbotax
  • Personal income tax laws
  • Represented as logical computer rules
  • Software computes tax liability

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Machine Learning (Pattern based)
  • Machine Learning (ML)
  • Algorithms find patterns in data and infer rules
    on their own
  • Learn from data and improve over time
  • These patterns can be used for automation or
    prediction
  • ML is the dominant mode of AI today

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Machine Learning Uses
Computer Translation
Automated recommendations
Self-Driving Vehicles
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Machine Learning Main Points
Learning Pattern Detection Data Self-Programmin
g
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Example Email Spam Filter
Spam or Wanted Email?
System detects patterns in Email About likely
markers of spam
Earn Cash
Bayesian Inference
Detected Pattern Emails with Earn Cash More
likely to be spam email
Earn Cash detected in 10 of Spam emails 0 of
wanted emails
Can use such detected patterns to make automated
decisions about future emails
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Intelligent Results Without Intelligence
For some (not all) complex tasks Requiring
intelligence Can get intelligent automated
results without intelligence By finding
suitable Proxies or Patterns
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Proxies for Intelligent Results Without
Intelligence
Statistical Machine Translation People use
advanced cognitive skills to translate
Google finds statistical correlations by
analyzing previously translated documents
Produces automated translations using
statistical likelihood as a proxy for
underlying meaning
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Hybrid Systems
  • Many successful AI systems are hybrids of
  • Machine learning Rules-Based Hybrids
  • e.g. Self-driving cars employ both approaches
  • Human intelligence AI Hybrids
  • Also, many successful AI systems work best when
  • They work with human intelligence
  • AI systems supply information for humans
  • Humans make decisions from information

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Technology Enhancing (Not Replacing) Humans
Humans Computers
Humans Alone
gt
Computers Alone
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Limits on Artificial Intelligence
  • Artificial Intelligence Accomplishments
  • Automate many things that couldnt do before
  • Limits
  • Many things still beyond the realm of AI
  • No thinking computers
  • No Abstract Reasoning
  • Often AI systems Have Accuracy Limits
  • Many things difficult to capture in data
  • Sometimes Hard to interpret Systems

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Organizations are using AI to Free human
ingenuity to focus on strategy
Business Capabilities, Processes, etc.
  • Better able to manage repeatable process
  • Improve workflow standardization
  • Reduce multiple systems reliance
  • Frictionless Straight Through Process
  • Reduce errors and operating cost
  • Increase risk management
  • Enhance customer experience and engagement

Intelligent AI Robots
CRM, ERP, BPM, Transactiona l Systems
Workforce, Team Members, Human Processing
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Representative Roles Created by AI
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Using AI to target how organizations work.
Organization
Technology
Process Methods
KPIs
Target Operating Model
Partners Alliances
People Culture
Competency Performance
Governance Compliance
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Maximizing Human Potential (Smart Technologies)
Statistics
Neural Computation
Pattern Recognition
  • Automated Process Management
  • Machine Learning
  • Robotic Process Automation

AML Automated Process Management
Deep Learning
Data Mining
Artificial Intelligence
  • Data (Information)
  • Signals and Triggers (Trends)
  • Human (Behavior)
  • Probability (Outcome Likelihoods)

Machine Learning
Datasets
Knowledge Discovery https//www.netcomlearning.c
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Application Domains
Computer Science Computability Decentralised
Information Systems Semantic Web Process
CalÐulus
Sociological Data
Spiritual Data
Media Fragmented Discourse Moral Issues Panics
Mobile Opinion Formers Swart Groups
Artificial Intelligence Business Process
Automation Machine Learning Bayesian
Methods Data Repositories Deep LearYiYg
Psychology Social Attitudes Cognitive Properties
Information Processing Methods
Mathematics Game Theory Networks Statistics
Cowpetitive Theory
Cultural Data
Economics Market Theory Micro Macros Capital
Types Market Types
Biology Evolutionary dynamics Systems biology
PlastiÐity Ecology Structure of Ecosystems
Productivity Populations Dynamics Environmental
Controls Biosphere
Socio-Cultural Values Ethics Lifestyles Trends
Demographics Corporate Attitudes
Legal
Intellectual Property Regulations
Public Engagement Corporate Social
RespoYsidility
Technology Protocols Architectures Structures
Security Resilience Usadility
Sociology Social Attitudes Group Dynamics
Theory Social Networks Pluwe TraÐiYg
Political Data
Financial Data
Industrial Data https//www.netcomlearning.com/we
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How has AI been used in Business
  • Over the years, AI research has enabled many
    technological advances, including
  • o virtual agents and chatbots
  • o suggestive web searches
  • o targeted advertising
  • o pattern recognition
  • o predictive analytics

o voice and speech recognition o face
recognition o machine translation o autonomous
driving o automatic scheduling
Many of these are now commonplace and provide
solutions to a great number of business
challenges and complex, real- world problems
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Types of Impact
  • Automated/ Robotic process
  • Automated assistants
  • Automated process management
  • Automated assistants
  • Machine learning
  • Search - indexing and feedback
  • Personalization and recommendations
  • Predictions and analytics
  • Individual journal
  • Research and service options
  • Learning systems
  • Machine learning TOMs
  • Speech, text, language processing
  • Value and risk profiling
  • Probability modeling
  • Customer DNA and data integration

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Where can AI be applied
  • Operational Support
  • Business Support
  • Sales and Marketing
  • Manufacturing and Delivery
  • Legal
  • Skills and Competency
  • Business Continuity
  • Consolidation, Sourcing, Vendor Optimization
  • Strategy and Organization
  • Brand Management
  • Finance Office
  • Human Capital
  • Business and Competitor Intelligence
  • Data and Reporting
  • Governance
  • Risk Management
  • Business Process and Change

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Interconnecting Applications
  • You can use AI technologies to
  • Improve customer services and experience
  • Automate workloads, improve workflow
  • Optimize logistics and repeatable process
  • Increase output and efficiency
  • Prevent outages
  • Predict performance
  • Predict behavior
  • Manage and analyze data
  • Improve your marketing and advertising
  • Better decision making
  • Reduce risk and fraud
  • Depending on the type of AI, smart programs can
    perform
  • Specific individual tasks, such as medical
    diagnosis, electronic trading, robot control,
    etc.
  • Complex, cognitive tasks based on their
    understanding of how certain things (e.g. a
    language or a behavior) work.

Artificial intelligence (AI) is steadily passing
into everyday business use, solving both
business problems and creating opportunities
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Recorded Webinar Video
To watch the recorded webinar video for live
demos, please access the link https//bit.ly/2IwW
7B4
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About NetCom Learning
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Machine Learning - Class scheduled on June 25
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scheduled on June 04 Practical Data Science
with R - Class scheduled on June 11 Beginning
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- Class scheduled on June 04 Beginning Data
Analysis with Python and Jupyter - Class
scheduled on June 11 EDX11097 Deep Learning
Explained - DAT236x OD20774 Perform Cloud
Data Science with Azure Machine Learning MOD
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The Changing Role of Security Leadership Get
Your Head in the Cloud The Growing Importance of
Cloud Certifications From Desktop to Mobile
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Introduction to Software Defined Networking (SDN)
Preparing and Architecting for Machine Learning
The Applications of SDN in Cisco (Part 2) How to
Transition from AutoCAD to Revit BIM - What You
Must Know Let the Battle Begin SaaS, PaaS and
IaaS
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THANK YOU !!!
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