This document provides an overview of cognitive computing and how to build a first cognitive computing application. It discusses fundamentals like natural cognitive processes, approaches to machine learning, and perception. It also outlines the cognitive computing technology ecosystem, including machine learning platforms, input/output technologies, infrastructure providers, and analytics/visualization tools. Finally, it offers advice on first steps like identifying a domain and data sources, choosing a machine learning model, and building a virtuous learning cycle.

1. Commercial Cognitive Computing
How to choose and build your first cognitive computing application
Adrian Bowles, PhD
Founder, STORM Insights, Inc.
info@storminsights.com

2. Commercial Cognitive Computing
How to choose and build your first cognitive computing application
Webinar topics
Fundamentals of cognitive computing
The cognitive computing ecosystem
First steps

3. I. Fundamentals of Cognitive Computing
Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.

4. Cognitive computing is a problem-solving approach that
uses hardware or software to approximate the form or
function of natural cognitive processes.
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5. Natural Cognitive Processes
Learning
Perception
Motivation
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Start with nature…

6. Learning is the sine qua non, or
essential condition of cognitive computing.
Natural Cognitive Processes
Perception
Motivation
reflectioninferencededuction
Learning
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reasoning

7. Three key approaches to machine learning...
Natural Cognitive Processes
Perception
Motivation
reinforcement
Learning
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unsupervisedsupervised

8. Three key approaches to
Natural Cognitive Processes
reinforcement
Machine
Learning
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unsupervised
supervised
The system is taught to detect or match patterns
based on training data. Learning by example.
The system learns/develops strategies based
on performance feedback.
An unsupervised learning system discovers patterns
based on experience.

9. Machine
Learning
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deep
learning
Deep learning refers to a biologically-inspired approach to
machine learning that leverages a collection of simple processing
units - analogous to neurosynaptic elements - that collaborate to
solve complex problems at multiple levels of abstraction. These
modern neural networks can support supervised, reinforcement,
or unsupervised learning systems. In general, deep learning
solutions require a high degree of parallelism, which may be
implemented in hardware and/or software.

10. Learning
Perception
Motivation
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see tastesmellhear touch
Perception: how we sense/recognize data in the outside world

11. Perception: how we recognize data in the outside world
Perception
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see tastesmellhear touch
Text,
Images,
Surface
structured records…
Speech
Music
Cues
Noise
Sensors:
Temperature
Tactile
Texture

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Perception: obvious structure is easy to process…
but most of the interesting stuff isn’t obvious to a computer.

13. Motivation: why we act.
Natural Cognitive Processes
Learning
Perception
Motivation
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14. Problem Solving
& Learning
Simple:
deterministic,
retrieve/calculate
Response Types
Visible
Reports
Invisible
Memory updates
Input Types
well-structured output from
other systems
Basic Cognitive Computing
Memory
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15. Perception/
Language
Problem Solving
& Learning
Complex: probabalistic
hypothesize, test, rank, select
Creative: discover, generate
Input Class/Type
Visual
Text
Image
Aural
Speech
Music
Cues
Noise
Informative
Touch
Temperature
Tactile
Texture
Taste
Smell
Response Types
Visible (to the environment)
Reports
Verbal/NL Text
Behavioral (system changes)
Haptics
Invisible
Memory updates
Advanced Cognitive Computing
Memory
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16. Cognitive computing is a problem-solving approach that
uses hardware or software to approximate the form or
function of natural cognitive processes.
Two major approaches to cognitive computing:
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17. 1. Neuromorphic architectures:
computer or device-level systems modeled after
biological systems or components, such as neurons
and synapses. These may be implemented in analog,
digital or hybrid hardware.
Cognitive computing is a problem-solving approach that
uses hardware or software to approximate the form or
function of natural cognitive processes.
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18. An IBM - SyNAPSE board
Source: Qualcomm
Neuromorphic Architectures
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19. Cognitive computing is a problem-solving approach that uses
hardware or software to approximate the form or function
of natural cognitive processes.
Learning
Perception
Motivation
reflectioninferencededuction
Two major approaches to cognitive computing:
2. Functional equivalence:
Model the behavior of biological
systems, not their structure.
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reasoning

20. Cognitive computing is a problem-solving approach that uses
hardware or software to approximate the form or function
of natural cognitive processes.
Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.

21. Cognitive
Computing
HW ArchitectureNeuromorphic
von
Neumann-based
Cognitive Workloads
Natural
Language Processing
Hypothesis Gen/&
Testing
Confidence-
Weighted
Reporting
Supporting Workloads
Experience-
Based
Learning
Shallow-
structured
Data
Descriptive
Analytics
Deep-
structured
Data
Predictive/
Prescriptive
The Cognitive Computing Landscape
Natural Cognitive Processes
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Learning
Perception
Motivation

22. II. The Cognitive Computing Technology Ecosystem
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23. 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
The Cognitive Computing Technology Ecosystem
Data
Management
Alt/Neuromorphic
Hardware
Professional
Services
Analytics
Reports
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24. Human
Sensors/
Systems
Infrastructure
Input Output
Visualization
Narrative Generation
Voice/NLP
Video/Images
Reports
Gestures
Emotions
Text/NLP
Surface Structured Data
Surface Structured Data
The Cognitive Computing Technology Ecosystem
Data
Management
Alt/Neuromorphic
Hardware
Professional
Services
Analytics
Reports
Machine Learning
Metamind
IBM
Ersatz Labs
Scaled Inference
Microsoft
Saffron
IP Soft
Numenta
ai-one
Digital Reasoning
Google
Nervana Systems
BigML
Sentient Technologies
Vicarious
Skymind
Lumiata
wise.io
Dato
Kimera SystemsH2O
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25. Human
Sensors/
Systems
Infrastructure
Input Output
Visualization
Narrative Generation
Voice/NLP
Video/Images
Reports
Gestures
Emotions
Text/NLP
Surface Structured Data
Surface Structured Data
The Cognitive Computing Technology Ecosystem
Data
Management
Alt/Neuromorphic
Hardware
Professional
Services
Analytics
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Reports
Solution Providers
IBM
Watson
Accenture
IP Soft
Cognitive Scale
Altilia
H2O

26. Visualization*Narrative GenerationVoice/NLP
Video/Images
Gestures
Emotions
Text/NLP
The Cognitive Computing Technology Ecosystem: I/O Technologies
• Affectiva
• BeyondVerbal
• Emotient
• Limbic
• Nuance
• Nviso
• Expect Labs
• Gridspace
• IBM
• Maluuba
• Nuance
• PopupArchive
• Skymind
• SpaceTimeInsight
• Viv Labs
• Wit.ai
• ABBYY
• Altilia
• Cortical.io
• IBM
• idibon
• Kaypok
• Luminoso
• Maluuba
• Wit.ai
• BRS Labs
• Clarifai
• Dextro
• Madbits (twitter)
• Mindops
• Skymind
• Teradeep
• Visenze
• Narrative Science
• OnlyBoth
• APX Labs
• EyeSight
• GestureTek
• LeapMotion
• Nod
• Intel
*Visualization functionality is often bundled with analytics software.
We currently combine these in a single category.
Machine Learning
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27. Data Management
Alt/Neuromorphic
Hardware
Professional
Services
Analytics/Visualization
The Cognitive Computing Technology Ecosystem: Infrastructure
• Accenture
• Cognitive Scale
• Deloitte
• Hitachi
• IBM
• Xerox
• Artificial Learning
• DWave
• HRL Laboratories
• IBM
• Nervana Systems
• Qualcomm
• Teradeep
• 1010data
• Actuate
• Adatao
• Alpine Data Labs
• Alteryx
• Altilia
• Angoss
• Attivio
• Birst
• Civis Analytics
• ClearStoryData
• Connotate
• Context Relevant
• Dataiku
• Datameer
• Emerald Logic
• First Rain
• ForeSee
• Fractal Analytics
• Guavus
• IBM
• indico
• Inkiru (Wamart)
• KNIME
• KXEN (SAP)
• LiftIgniter
• Loop AI Labs
• MathWorks (Matlab)
• Microsoft
• Mu Sigma
• Actian
• Aerospike
• Alation
• Altilia
• Basho
• Caspio
• Cognitive Scale
• Cognizant Technology
• Couchbase
• CrowdFlower
• CumuLogic
• Data Bricks
• DataRPM
• DataStax
• DataWeb, Inc.
• DDN
• diffbot
• GigaSpaces
• Giraph
• Grappa
• GridGain
• Hortonworks
• HP Vertica
• IBM
• import io
• kimono
• MapR
• Nara Logics
• NuTonian
• Opera Solutions
• Oracle
• Palantir
• Pentaho
• Prediction IO
• Predixion
• Qliktech
• Quid
• Rapid Miner
• Revolution Analytics(MSFT)
• Salford Systems
• SAP
• SAS Institute
• SiSense
• Spark Beyond
• Spotfire (Tibco)
• StatSoft (Dell)
• Synthos Technologies
• Teradeep
• ThinkBig Analytics (Teradata)
• Versium
• Wolfram Mathematica
• Yhat
• MarkLogic
• MongoDB
• NeoTechnology
• Oracle
• Paxata
• RainStor
• Reactor
• SAP
• Space Curve
• Viv Labs
Machine Learning
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30. III. First Steps
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31. Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
Early adopters…
healthcare (payers, providers, patients), diagnoses, intern
training, self service…
legal - from due diligence to compliance
security - from police to HSA to DoD…
call centers, supporting complex products
retail - recommendations from outdoor/adventure goods…
travel/hospitality - taking the burden off the customer to
recommend solutions
telecomm - managing operations
The ideal app…
Performs a function that is already being done by skilled professionals who
can’t keep up with the data, or are too expensive, or that involves high risk.

32. Document-centric
Data-centric
Behavior/
event-centric
Knowledge-
centric
Data Model Source
Manual
&
Systems
Systems
&
Sensors
Systems
Sensors
Machine-learning
Cognitive Computing
Municipal Knowledge
Figure 12-1. Foundations of Cognitive Computing for Smarter Cities
from Cognitive Computing and Big Data Analytics, Hurwitz, Kaufman & Bowles, 2015
Analysis
Descriptive/
Predictive/
Learning
Descriptive/
Predictive
Descriptive
Maturity
Silos
Shared
Integrated
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Can a cognitive computing application disrupt your industry?

33. Copyright (c) 2015 by STORM Insights Inc. All Rights reserved.
Start with the hard questions!
Do you have the skills?
Do you have the data?
Are your customers ready for probabilistic or non-deterministic answers?
(can they deal with uncertainty and multiple possible answers?)
Does anybody else have the data?
Will NLP add value in the eyes of your customers?
How important is it to be able to explain how the system got an answer or made a
recommendation…? (medical diagnosis - HIGH, recommending a sweater, not so much)
How important is it for the system to improve its performance over
time? (vs consistent answers)

34. 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
Which technologies are most important to serving your customers?
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35. Identify the domain
Choose a primary machine learning model
for general supervised learning, identify the attributes and sources
of training data
for reinforcement learning, identify events/states that need to be
reinforced (positive or negative)
for unsupervised learning, identify discovery parameters
Identify the data sources
Build/buy decision time…
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Ready?

36. Identify
Data Sources
Generate
Hypothesis
Ingest Data
RefineTrain System
Operate/
Run System
Identify Anomalies
& New Patterns
Baseline
Knowledge
Before you start, can you build a virtuous cycle for your domain?
Refine/
Update Corpus
Corpus
Data
Ontologies
Taxonomies
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Human
Sensors/
Systems
Voice/NLP
Video/Images
Gestures
Emotions
Text/NLP
Surface Structured
Data

37. For more information:
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email adrian@storminsights.com