Adopting elements of modern AI and cognitive computing - including advanced natural language processing, natural interface technologies such as gesture and emotion-recognition, and machine learning - is rapidly becoming a necessity for new applications. As people in all industries are exposed to better, more personalized and responsive experiences with software, they will begin to demand more from every system they use. For product strategists and developers, the issue is not whether to consider modern AI, the issue is how to do so most effectively. Webinar participants will learn: •How to classify and map application attributes to AI technologies and tools; including data attributes, end-user attributes, and context attributes such as weather and location •How to prioritize applications in an existing portfolio for AI-enhancements, and •How to assess organizational readiness for leveraging AI

1. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
A Roadmap for Deploying Modern AI in Business
Adrian Bowles, PhD
Founder, STORM Insights, Inc.
info@storminsights.com

2. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
A Roadmap for Deploying Modern AI in Business
Context - How Did We Get Here?
(and where are we anyway?)
Elements of Modern AI
Are You Ready?
Evaluating Application Requirements
Evaluating Organizational Attributes

3. Context - How Did We Get Here?
(and where are we anyway?)
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.

4. Context - How Did We Get Here? (and where are we anyway?)
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
AI Roots
AGI - Artificial General Intelligence
Focus on replicating intelligence by copying
brain functions
Natural Language Processing (NLP)
Learning and discovery
Heuristics, expert rules…
Logic - symbolic logic and
mechanical theorem proving
Strategy: Replace
Execution: Open concepts
Constraint: Processing
Modern AI
Focus on augmenting intelligence by
evidence-based interaction
Natural Language Processing (NLP)
Learning and discovery
Distributed ML driven by big data
Deep QA techniques
Strategy: Reinforce
Execution: Open code and data
Constraint: Data

5. A Quick Guide to AI Progress/Maturity
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
AI Roots
AGI - Artificial General Intelligence Sloooooow…still crawling
Focus on replicating intelligence by copying
brain functions Still trying to understand nature
Natural Language Processing (NLP) Great results, practical apps
Learning and discovery Solid results using supervised
Heuristics, expert rules… and unsupervised ML algorithms
Logic - symbolic logic and Well understood
mechanical theorem proving
Strategy: Replace
Execution: Open concepts
Constraint: Processing

6. A Quick Guide to AI Progress/Maturity - Key Disruptors
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Autonomous systems (based on ML)
Self-driving cars
Drones
Augmented Intelligence systems
Medical diagnostics
Complex systems maintenance
and materials planning
Agents for personal productivity
Apple Siri
Microsoft Cortana
Google Now
Time
Maturity

7. Machine Learning
Human
Sensors/
Systems
Infrastructure
Input Output
Visualization
Narrative Generation
Voice/NLP
Video/Images
Reports
Gestures
Emotions
Text/NLP
Surface Structured Data
Surface Structured Data
Reports
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Data
Management
Alt/Neuromorphic
Hardware
Professional
Services
Perception
Elements of Modern AI

8. 0. Foundation
Experience-
Based
Learning
1. Learn
2. Interact
3. Expand
Integrate
Augmented/Virtual
Reality
Confidence-
weighted
Reporting
Motivation
reflection
inference
Natural Cognitive Processes
deduction
Hypothesis
Generation
&Testing
reasoning
Natural
Language Processing
Cloud
…
Analytics
Data Management
Neuromorphic
Architectures
Learning
Perception
A Framework for Cognitive Computing
Copyright (c) 2015-2016 by STORM Insights Inc. All Rights reserved.

9. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
In the news…

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In the news…

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In the news…

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In the news…

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Open Source and
Modern AI…

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Apache Spark
Spark MLlib (distributed ML framework)
GraphX (distributed graph processing framework)
Commercialized by Databricks, …
Open Source and Modern AI…

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16. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
In the news…

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In the news…

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In the news…

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In the news…

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In the news…

21. Are You Ready for Modern AI?
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
The goal is to assess the fit between application requirements and
available modern AI tools/technology.
Evaluating Application Requirements
Evaluating Organizational Attributes
The goal is to assess organizational-readiness to effectively leverage
available modern AI tools/technology.
Tools
Technology
Talent

22. Evaluating Application Requirements
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
As with all new technology, adding an AI-based application to your portfolio
requires at least a preliminary a risk-assessment exercise. Is the application:
Critical to the enterprise?
Time sensitive?
All or nothing? (can we define incremental releases to mitigate risk?)
Visible? (success AND failure)

23. Evaluating Application Requirements
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Look at the data, NLP requirements, learning requirements and user-comfort with ND* responses.
For each of the key questions in this section, answer
Critical (not worth building the app without this feature/function)
Nice to have (this would be a differentiator, but we can do without…for now)
Won’t be missed or noticed by our users
Unsure
*ND -non-deterministic - responses, used to contrast probability-based answers
with those based on certainty.

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Is a specific modern AI capability required, or can conventional tools and
techniques adequately address your data acquisition, management and
analysis requirements?
Evaluating Application Requirements
Look at the data, NLP requirements, learning requirements and user-comfort with ND* responses.
What percentage of the data required for this application comes from
Highly structured data (records from tables, formatted directories, databases...)
Internal (data that is owned or licensed, such as customer data or mailing lists)
Third-party data that needs to be procured
Unsure
Text (books, magazines, newspapers, social media feeds...)
Your internal data (data that is owned or licensed)
Third party data that needs to be procured
Unsure
Audio, video
Your internal data (data that is owned or licensed)
Third party data that needs to be procured
Unsure
Critical
Nice to have
Won’t be missed
Unsure

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How important is it for your customers/users to be able to interact with
this application in NL? Will multi-language support be required now or in
the future?
What level of language sophistication is expected from the users, and
what level is required from the application?
Are the NL requirements critical for
Input only?
Output only?
Conversation to refine an interactive session?
Evaluating Application Requirements
Look at the data, NLP requirements, learning requirements and user-comfort with ND* responses.
Critical
Nice to have
Won’t be missed
Unsure

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How important (and feasible) is the ability to provide the “right answer” rather
than several alternatives, ranked by confidence or probability?
How important is it for the application to be able to explain how it arrived at its answer(s)? (will
the user trust or question the results, how much interaction will be required vs how much will
they want). Is the risk of a “bad” answer quantifiable? (from customer satisfaction to medical
malpractice to financial loss in a stock recommendation engine)
Evaluating Application Requirements
Look at the data, NLP requirements, learning requirements and user-comfort with ND* responses.
Critical
Nice to have
Won’t be missed
Unsure

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How important is it for this application to improve its performance by learning
from its own experience rather than being updated by your own team?
Evaluating Application Requirements
Look at the data, NLP requirements, learning requirements and user-comfort with ND* responses.
Can you update it fast enough to be useful (consider the rate of new data and
changed data that must be analyzed to be effective)?
Is this application in a regulated domain?
Do you have data that can be used to train the system?
Critical
Nice to have
Won’t be missed
Unsure

28. Evaluating Organizational Attributes
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
The goal is to assess organizational-readiness to effectively leverage
available modern AI tools/technology.

29. Evaluating Organizational Attributes
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
For each of the modern AI tools/technologies identified as critical in the previous
assessment step - e.g. NLP, machine-vision/video analysis, data management including
knowledge of ontologies, taxonomies, etc, machine learning techniques (supervised,
unsupervised, reinforcement..),
determine…
Actual skill set requirements (including level with technologies vs tools)
Available skills
Internally
Partners
Contractors
Market pool
Tools
Technology
Talent
Anyone can buy tools, talent is the hard part.

30. For more information:
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
adrian@storminsights.com
Twitter @ajbowles
Skype ajbowles
Upcoming Webinar Dates & Topics
March 10 Machine Learning Adoption Strategies
Theme: Cognitive Computing
April 14 Getting Started with Streaming Analytics and the IoT
Theme: Smart Data and the Internet of Things
May 12 Emerging Data Management Options: Graph Databases
Theme: Smart Data Management