An overview how simple semantic solutions can be build if the ai-one LIB/API is used

1. ai-one™
biologically inspired intelligence
© ai-one
inc. 2012

2. Biologically Inspired Intelligence
logic creativity
© ai-one
inc. 2012

3. Better decision-making through BI
GARTNER © is strongly promoting BI strategy as well as consulting the
industry about how to get the best use of BI
GARTNER’s© position: Maximizing decision impact
through business intelligence (BI) increases enterprise
effectiveness at all levels, contributing to mission or growth
goals by enabling workers and managers to direct
business or mission decisions toward desired outcomes.
© ai-one
inc. 2012

4. BI Definition
There are multiple definitions of BI. The following definition is our
preferred understanding…
Business intelligence (BI) mainly refers to computer-based techniques
used in identifying, extracting, and analyzing business data. BI
technologies provide historical, current and predictive views of business
operations.
BI uses technologies, processes, and applications to analyze mostly
internal, structured data and business processes while competitive
intelligence gathers, analyzes and disseminates information with a
topical focus on company competitors. Business intelligence understood
broadly can include the subset of competitive intelligence.
-- From WIKIPEDIA©

5. Information & Data are the inputs for BI
The most important factor is the value of data input in BI processes
Facts to validate information value
1. Source Who is the Source (sender)? Do we know the source? Could
there be a change in value since last use?
2. Receiver Who is the receiver? Do we know the receiver? Is there a
change in attributes and value since last use? Did receiver
further transport the data or behave and/or make decisions
on it?
3. Content What is the content of the information exchanged?
© ai-one
inc. 2012

6. Information & Data are the inputs for BI
The sources are structured & unstructured and in various dimensions
The rectangle must fit into the circle!
The challenge is to extract actionable knowledge from complex data that
contains many different types of information is constantly changing.
Humans have has an innate capacity to find patterns among different
sets of attributes quickly and easily. Our brains are hard-wired to find
similarities and differences by evaluating context.
ai-one’s API enables computers to analyze complex data to find patterns
in a way similar to a human – by simply finding the keys to context.
The HSDS, or holosemantic data space, makes it possible to find the
most unusual relationships – such as when a rectangle fits into a circle –
even when the signal is very faint.
© ai-one
inc. 2012

7. …ai-one - Content Analytics
Traditional
ai-one
© ai-one
inc. 2012

8. Hybrid solutions
GARTNER © is defining 3 types of content analytics :
Structured, Hybrid and Content.
GARTNER© positions ai-
one as a hybrid solution:
Combining structured data
and content (unstructured)
GARTNER© Chart from the L.A. 2012 Congress
© ai-one
inc. 2012

9. Hybrid solutions
GARTNER © defines 3 types of content analytics:
Structured, Hybrid and Content.
The ai-one hybrid
approach:
The HSDS, holosemantic data
space, is the environment
where multi layer higher order
patterns are found and where
heterarchical structures are
analyzed. The HSDS is the
perfect environment for
challenges 1, 2, & 3
GARTNER© Chart from the L.A. 2012 Congress
© ai-one
inc. 2012

10. Cool Vendors in Content Analytics, 2012
ai-one is featuered in GARTNER © 2012 Cool Vendor Report:
“Data is growing in volume, variety, velocity and complexity. Cool
Vendors in content analytics offer innovative approaches, tools
and technologies for analyzing text, images, video or speech,
and for finding and acting upon insights and patterns across
content types and structured data.“
“ai-one provides machine learning technology that mimics how the
brain detects patterns in data, which developers can embed into any
application.“
http://www.gartner.com/DisplayDocument?ref=clientFriendlyUrl&id=1996718
Contents: Analysis
What You Need to Know
ai-one
Co-Decision Technology
Mattersight
ThoughtWeb
© ai-one
inc. 2012

11. …ai-one is listening to the data –
ai-one can give you an answer to
a question, you did not know to
ask!...changing the “search”
function to a “find” function
© ai-one
inc. 2012

12. …ai-one –
… solves two problems:
• Sense making in unknown data
• Generalizing multi layer higher
order pattern foundation
© ai-one
inc. 2012

13. Traditional AI/KM
Focus on logic, Boolean & statistics
approach. Manually programmed fuzziness
and high dependency on quality of
programmers and experts, thesauri and
Ontology as Models.
Problems with speed, intelligence and
incremental updates!
logic creativity
© ai-one
inc. 2012

14. Traditional AI/KM
Focus on neural or fuzzy & statistics
approach. Manually programmed fuzziness
and high dependency on quality of
programmers and experts, thesauri and
Ontology as Models.
Problems with speed, intelligence and
incremental updates!
logic creativity
© ai-one
inc. 2012

15. …the ai-one hybrid–
The holosemantic data space combines LOGIC & CREATIVE data
processing in a n-dimensional data space (including space-time).
PIM Process In Memory, and “where the circle fits the rectangle”
© ai-one
inc. 2012

16. The Fundamental Theory
General introduction | The enabling elements
Motivation
refers to the intrinsic activation of goal-oriented behavior , like a clock driven by a
flywheel
Self-organization
is a key of function of our holosemantic data space in combination with the
motivation and in order to optimize information structure
Impulsive information detection & multiple higher-
order concept formation
a result of the combination between motivation, self-organization and the ai-one™
algorithms
© ai-one
inc. 2012

17. Features of ai-one™
The Topic-Mapper™; Ultra-Match™ or Graphalizer™
library and SDK focuses on different solutions:
 Text/Linguistic: Topic-Mapper focuses on LWOs (Light Weight
Ontology) for semantic applications for expert systems; dialogue
robot’s, text & content analysis, keyword generation, matching
associative, semantic decision/conclusion systems.
 Image Analysis/Matching: Ultra-Match focuses on images
where multi layer higher order pattern foundation and complex
pattern or concept matching is important.
 Signal Processing: Pattern recognition in data streams of
various kinds of signals and sources. Multi layer higher order
complexity is enabled here as well.
© ai-one
inc. 2012

18. The Fundamental Theory
General introduction
• Self-optimized information processing
• Self-controlled content organization
• Multiple higher-order concept formation
• Autonomic learning via multiple context recognition
• Self-generalizing of learned concepts
Biologically inspired
intelligence in computing
Leads to:
© ai-one
inc. 2012

19. ai-one™ SDK | The Learning Machine
… the SDK:
Core
Utilities (sensors)
MVPs
Documentation
Best Practice
Source Samples
© ai-one
inc. 2012

20. The ai-one approach
© ai-one
inc. 2012

21. ai-one –
… our SDK is an API to build a
learning machine
… ai-one enables biologically
inspired intelligence in computing
© ai-one
inc. 2010

22. SDK with | Source, MVPs & Utilities…
© ai-one
inc. 2012

23. The content fingerprint
© ai-one
inc. 2012

24. The Corporate Structure
ai-one inc.
Corporate HQ
La Jolla CA
ai-one ag ai-one gmbh
Research Lab Europe Sales & Support
Zurich Berlin
• Offices in La Jolla, Zurich and Berlin
• US Delaware C Corporation with wholly owned subsidiaries
• Founded in 2003 in Zurich; former name: “semantic system ag”
• Approximately 15 FTEs
• Privately funded
© ai-one
inc. 2012

25. The Sales Concept for the Solution
ai-one™
Distribution Network
Consulting Partner OEM-Partner Solution Provider
Experts in Various Markets SW & HW Vendors In-house & Whole Supplier
• Slim and effective ai-one organization
• High scalability trough partners
• Distributed risk because the massive numbers of vertical markets
• Sustainable markets and revenue streams once the approach is established
• High exit and cash potential because of already installed JV - Partnerships
© ai-one
inc. 2012

26. The ai-one Incubation Strategy
ai-one inc.
Corporate HQ
La Jolla CA
ai-one ag ai-one gmbh
Research Lab Europe Sales & Support
Zurich Berlin
Brainup AG ai-ibiomics gmbh
Data Intelligence Genomics Joint Venture
Forensity AG
Swiss Forensic Solutions
© ai-one
inc. 2012

27. Business Cases
Multiple vertical markets as SW or HW solutions
Biometry:  Pattern recognition …
Forensics:  Tracks, patterns, profiles …
Intelligent Services:  Profiles, behavior, semantics
Security:  Cryptography, compression
Fraud:  Fraud, camouflage…
Sociology:  Human behavior profiles
Data bases:  Analyses, data mining …
Computing:  Intelligence in computing
Life Science:  Pattern recognition
Pharmacy:  Clinical tests, profiling
Dermatology:  Cosmetics, pattern recognition
more…
© ai-one
inc. 2012

28. ai-one – The Next Evolution in
Information and Communications
Technology?
… recognizing the content
… understanding the meaning and
generalizing its application
… deciding about its importance
… knowing what to do with this
learned information
© ai-one
inc. 2012

29. Thank You!
ai-one inc. ai-one ag ai-one gmbh
5711 La Jolla Blvd., Flughofstrasse 55, Koenigsallee 35a,
Bird Rock Zürich-Kloten Grunewald
La Jolla, CA 92037 8152 Glattbrugg 14193 Berlin
cell: +18585310674 cell: +41794000589 cell: +4915112830531
main: +18583641951 main: +41448284530 main: +493047890050
© ai-one
inc. 2012

30. ai-one ™
The History of ai-one™
The media Founding world HQ:
picks up the ai-one inc. USA
story
New name for Swiss
company:
ai-one ag
Founding European HQ:
Early stage partners ai-one GmbH GER
semantic system ag
Switzerland R&D LAB
Walt Diggelmann
Tomi Diggelmann
Manfred Hoffleisch
2003 2004 2005 2006 2007 2008 2009 2010 2011
Fundamental Theorie R&D Applied Solutions R&D API and libraries development API and libraries commercialization
The first 6 years were
characterized by a very sharp
focus on R&D. A new fundamental
GLOBUS
theory also requires a whole
infrastructure to be built. Hence we
first had to create a development
environment (API/libraries) for the
commercialization.
So far we have spent approx.
7.0 Mio. of investment capital for
R&D.
© ai-one
inc. 2010
© ai-one inc. USA, ai-one ag, SUI , Diggelmann / Hoffleisch 1985 - 2010