The presentation I have at the Chicago UD Meetup on Sept 22nd.

1. How to Design Products with
Cognitive Computing and Big Data

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3. Industry Overview
01.
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4. Cognitive Computing
Def.
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5. “Cognitive Computing makes context computable.”
“Cognitive computing systems redefine the nature of the
relationship between people and their increasingly pervasive
digital environment.”
“Their output may be prescriptive, suggestive, instructive, or
simply entertaining.”
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https://cognitivecomputingconsortium.com/resources/cognitive-computing-defined/#1467829079735-c0934399-599a

6. Attributes of Cognitive Computing?
+ Adaptive
+ Interactive
+ Iterative and stateful
+ Contextual
Copyright 2016. rMark Bio, Inc.
https://cognitivecomputingconsortium.com/resources/cognitive-computing-defined/#1467829079735-c0934399-599a

7. Cognitive Computing versus A.I.
Cognitive learns and helps advise a user.
Artificial Intelligence learns and does all the work.
However… both have very similar properties.
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8. Big Data Approaches
Def.
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9. Very specific criteria and you
are trying to generalize to
include more individuals like
the ones that meet your
specific criteria.
(Who else is like me?)
Top Down vs. Bottom Up
start here >
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10. With a massive number of
possibilities, you are trying to
apply criteria to get to what
matters most to you.
(Filtered Search)
Bottom Up vs. Top Down
start here >
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11. Fidelity and Normalization
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+ The amount of data + The speed in which
data is received.
+ The different types
of data
Volume Velocity Variety

13. Cognitive & Big Data Uses
Def.
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Text Mining
Ability to search and identify key
information across hundreds of
disparate data types and sources.
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Search here….

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Looking at who is in a network and
the type of influence they have.
Influence
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Because you like this…

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Rate and rank potential based on criteria.
Scoring
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19. You like these types of songs…
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Automatically group Users based
on a plurality of attributes
Categorization
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Others like you also purchased…

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All of these techniques
provide similar value.
Time
Savings
Ease of
Identification
New
Opportunities
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23. 02.
Challenges for Product Designers

24. How do we find the sweet spot between
too much and too little information?
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+ Frequency + Details + Decisions

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Layered User Interface
+ Provide Access Contextual Data
+ Provide Additional Features

26. How do we enable the feeling of ah-ha?
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Chat Bots
+ Interact with Users
+ Gain insights
+ Provide the “ah-ha” moment

28. How do we enable feedback to be
meaningful, yet quick?
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Widgets
+ Key insights in Contextual Data
+ Easily Extensible
+ Tap into Cognitive System for
dynamic recommendations - IBM
Watson, Microsoft and Google.

30. Closing Thoughts.
03.

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+ Interfaces that changes with user needs.
+ Adapt dynamically based on usage
patterns,
Adaptable Interfaces

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Adaptable Interfaces
+ Interfaces that changes with user needs.
+ Adapt dynamically based on usage
patterns,

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Adaptable Architecture
http://www.ibm.com/developerworks/library/wa-uihealth/

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Adaptable Interfaces
Research
+ Accommodating Individual Differences through an
Adaptive User Interface by
+ David Benyon Computing dept., Open University
+ Adaptable User Interface Generation
+ Lawrence Chung, Dept. of Computer Science University of Texas
+ User Modeling and User-Adapted Interaction
+ Gerhard Fisher, Dept. of Computer Science University of Colorado

35. Copyright 2016. rMark Bio, Inc.
Multi-faceted Teams
+ Teams combined with designers,
application developers, platform
developers and data scientists.

36. Thank you.
@jasonmcsmith
jason@rmarkbio.com