biologically inspired intelligenceai-one™

© ai-one inc. 2012

Biologically Inspired Intelligence

creativitylogic

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GARTNER© is saying: 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.

Better decision-making through BIGARTNER © is evangelizing strongly the BI strategy and is teaching & consulting, how the industry should get best use of BI

© ai-one inc. 2012

BI Definition

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.

WIKIPEDIA©

We have found that there are multiple definitions on BI. The one we display is the one which we prefer from our opinion and understanding

BI’s input is Information & DataThe most important factor is the information/data- quality where BI processes will start from. Therefore its essential that we only count relevant information by qualification.

1. Source Who is the Source (sender). Do we know it, did we work with it before, could there be a change in quality since last usage,…

2. Receiver Who is the receiver. Do we know him, did we work with him before, could there be a change in quality since last usage,…

Did receiver further transport the information or behave and/or make decisions on it…

3. Content What is the content of the information exchanged

Facts to validate information quality

© ai-one inc. 2012

BI’s input is Information & DataThe sources are structured & unstructured and in various dimensions

The rectangle has to fit into the circle!

The challenge is, to fit different types and dimensions of data and information (content), of different quality levels and find the Information value in it! Humans are very talented in doing so. We have a brain capacity which is natively enabled to work hybrid.

ai-one found a way to enable computers to think and analyze very similar as Hybrid. The HSDS, holosemantic data space, is the perfect environment where we can fit the rectangle into the circle.

© ai-one inc. 2012

…ai-one - Content Analytics

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Traditional

ai-one

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GARTNER© Chart from the L.A. 2012 Congress

ai-one has been positioned by GARTNER© as a hybrid solution:

Combining structured data and content (unstructured)

Hybrid solutionsGARTNER © is positioning content analytics into:Structured, Hybrid and Content.

© ai-one inc. 2012

Hybrid solutionsGARTNER © is positioning content analytics into:Structured, Hybrid and Content.

GARTNER© Chart from the L.A. 2012 Congress

The ai-one hybrid approach:

The HSDS, holosemantic data space, is the environment where multi layer higher order pattern are found and where heterarchical structures are analyzed. The HSDS is the perfect environment for the challenges 1; 2; & 3

© ai-one inc. 2012

Cool Vendors in Content Analytics, 2012GARTNER © is featuring ai-one in the latest 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: AnalysisWhat You Need to Knowai-oneCo-Decision TechnologyMattersightThoughtWeb

… gives you an answer to a question, you did not know that you have to ask….!

…ai-one is listening to the data –

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… solved two problems:

• Sense making in unknown data

• Generalizing multi layer higher order pattern foundation

…ai-one –

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Traditional AI/KM

creativitylogic

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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!

Traditional AI/KM

creativitylogic

© ai-one inc. 2012

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!

© ai-one inc. 2012

…the ai-one hybrid– The holosemantic data space combines the LOGIC & Creative data processing in a n-dimensional data space including space time

PIM Process In Memory, and “where the circle fits the rectangle”

The Fundamental TheoryGeneral introduction | The enabling elements

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Motivationwe refer to the intrinsic activation of goal-oriented behavior which is like a clock

driven by a flywheel

Self-organizationin combination with the motivation and in order to optimize information structure

this is a key of function of our holosemantic data space

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

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. Also here, multi layer higher order complexity is enabled.

The Fundamental TheoryGeneral 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:

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© ai-one inc. 2012

… the SDK:

CoreUtilities (sensors)MVPsDocumentationBest PracticeSource Samples

ai-one™ SDK | The Learning Machine

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The ai-one approach

… our SDK is a API to build a learning machine

… ai-one enables biologically inspired intelligence in computing

…ai-one –

© ai-one inc. 2010

SDK with | Source; MVPs; Utilities…

© ai-one inc. 2012

© ai-one inc. 2012

The content fingerprint

The Corporate Structure

© ai-one inc. 2012

ai-one inc.Corporate HQ

La Jolla CA

ai-one gmbhEurope Sales & Support

Berlin

ai-one agResearch Lab

Zurich

• 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

The Sales Concept for the Solution

© ai-one inc. 2012

ai-one™Distribution Network

OEM-PartnerSW & HW Vendors

Consulting PartnerExperts in Various Markets

Solution ProviderIn-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 because high dependency ones the

approach is established • High exit and cash potential because already installed JV - Partnerships

The ai-one Incubation Strategy

© ai-one inc. 2012

ai-one inc.Corporate HQ

La Jolla CA

ai-one gmbhEurope Sales & Support

Berlin

ai-one agResearch Lab

Zurich

ai-ibiomics gmbhGenomics Joint Venture

Forensity AGSwiss Forensic Solutions

Brainup AGData Intelligence

Business CasesMultiple vertical markets as SW or HW solutions

© ai-one inc. 2012

Biometry:Forensics:Intelligent Services:Security:Fraud:Sociology:Data bases:Computing:Life Science:Pharmacy:Dermatology:more…

Pattern recognition … Tracks, patterns, profiles … Profiles, behavior, semantics Cryptography, compression Fraud, camouflage… Behavior- & human profiles Analyses, data mining … Intelligence in computing Pattern recognition Clinical tests, profiling Cosmetics, pattern recognition

… recognizing the content… understanding the meaning and

generalizing its application… deciding about its importance… knowing what to do with this

learned information

…ai-one – The Next Evolution Stage in ICT?

© ai-one inc. 2012

Thank You!

© ai-one inc. 2012

ai-one inc. 5711 La Jolla Blvd., Bird RockLa Jolla, CA 92037

cell:+18585310674main:+18583641951

ai-one ag Flughofstrasse 55, Zürich-Kloten8152 Glattbrugg

cell:+41794000589main:+41448284530

ai-one gmbh Koenigsallee 35a, Grunewald14193 Berlin

cell:+4915112830531main:+493047890050

ai-one ™

© ai-one inc. USA, ai-one ag, SUI , Diggelmann / Hoffleisch 1985 - 2010

© ai-one inc. 2010

2003 2011

semantic system agSwitzerland R&D LAB

Walt DiggelmannTomi DiggelmannManfred Hoffleisch

20072004 2005 2006 2008 2009 2010Fundamental Theorie R&D Applied Solutions R&D API and libraries development API and libraries commercialization

New name for Swiss company: ai-one ag

Founding world HQ: ai-one inc. USA

Founding European HQ: ai-one GmbH GER

The media picks up the story

GLOBUS

The first 6 years are characterized with a very sharp focus on R&D. A new fundamental theory also requires a whole infrastructure to be built up upon. 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 forR&D.

Early stage partners

The History of ai-one™