00 ai-one - overview content analytics

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

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00 ai-one - overview content analytics

  1. 1. ai-one™biologically inspired intelligence© ai-oneinc. 2012
  2. 2. Biologically Inspired Intelligence logic creativity© ai-oneinc. 2012
  3. 3. Better decision-making through BIGARTNER © is strongly promoting BI strategy as well as consulting theindustry about how to get the best use of BIGARTNER’s© position: Maximizing decision impactthrough business intelligence (BI) increases enterpriseeffectiveness at all levels, contributing to mission or growthgoals by enabling workers and managers to directbusiness or mission decisions toward desired outcomes.© ai-oneinc. 2012
  4. 4. BI DefinitionThere are multiple definitions of BI. The following definition is ourpreferred understanding…Business intelligence (BI) mainly refers to computer-based techniquesused in identifying, extracting, and analyzing business data. BItechnologies provide historical, current and predictive views of businessoperations.BI uses technologies, processes, and applications to analyze mostlyinternal, structured data and business processes while competitiveintelligence gathers, analyzes and disseminates information with atopical focus on company competitors. Business intelligence understoodbroadly can include the subset of competitive intelligence.-- From WIKIPEDIA©
  5. 5. Information & Data are the inputs for BIThe most important factor is the value of data input in BI processesFacts to validate information value1. 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. 6. Information & Data are the inputs for BIThe sources are structured & unstructured and in various dimensionsThe rectangle must fit into the circle!The challenge is to extract actionable knowledge from complex data thatcontains many different types of information is constantly changing.Humans have has an innate capacity to find patterns among differentsets of attributes quickly and easily. Our brains are hard-wired to findsimilarities and differences by evaluating context.ai-one’s API enables computers to analyze complex data to find patternsin a way similar to a human – by simply finding the keys to context.The HSDS, or holosemantic data space, makes it possible to find themost unusual relationships – such as when a rectangle fits into a circle –even when the signal is very faint.© ai-oneinc. 2012
  7. 7. …ai-one - Content Analytics Traditional ai-one© ai-oneinc. 2012
  8. 8. Hybrid solutionsGARTNER © 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-oneinc. 2012
  9. 9. Hybrid solutionsGARTNER © 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-oneinc. 2012
  10. 10. Cool Vendors in Content Analytics, 2012ai-one is featuered in GARTNER © 2012 Cool Vendor Report:“Data is growing in volume, variety, velocity and complexity. CoolVendors in content analytics offer innovative approaches, toolsand technologies for analyzing text, images, video or speech,and for finding and acting upon insights and patterns acrosscontent 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-oneinc. 2012
  11. 11. …ai-one is listening to the data –ai-one can give you an answer toa question, you did not know toask!...changing the “search”function to a “find” function© ai-oneinc. 2012
  12. 12. …ai-one –… solves two problems:• Sense making in unknown data• Generalizing multi layer higher order pattern foundation© ai-oneinc. 2012
  13. 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-oneinc. 2012
  14. 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-oneinc. 2012
  15. 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-oneinc. 2012
  16. 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-oneinc. 2012
  17. 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-oneinc. 2012
  18. 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-oneinc. 2012
  19. 19. ai-one™ SDK | The Learning Machine … the SDK: Core Utilities (sensors) MVPs Documentation Best Practice Source Samples© ai-oneinc. 2012
  20. 20. The ai-one approach© ai-oneinc. 2012
  21. 21. ai-one –… our SDK is an API to build a learning machine… ai-one enables biologically inspired intelligence in computing© ai-oneinc. 2010
  22. 22. SDK with | Source, MVPs & Utilities…© ai-oneinc. 2012
  23. 23. The content fingerprint© ai-oneinc. 2012
  24. 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-oneinc. 2012
  25. 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-oneinc. 2012
  26. 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-oneinc. 2012
  27. 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-oneinc. 2012
  28. 28. ai-one – The Next Evolution inInformation and CommunicationsTechnology?… recognizing the content… understanding the meaning and generalizing its application… deciding about its importance… knowing what to do with this learned information© ai-oneinc. 2012
  29. 29. Thank You!ai-one inc. ai-one ag ai-one gmbh5711 La Jolla Blvd., Flughofstrasse 55, Koenigsallee 35a,Bird Rock Zürich-Kloten GrunewaldLa Jolla, CA 92037 8152 Glattbrugg 14193 Berlincell: +18585310674 cell: +41794000589 cell: +4915112830531main: +18583641951 main: +41448284530 main: +493047890050© ai-oneinc. 2012
  30. 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 GERsemantic system agSwitzerland R&D LABWalt DiggelmannTomi DiggelmannManfred Hoffleisch2003 2004 2005 2006 2007 2008 2009 2010 2011 Fundamental Theorie R&D Applied Solutions R&D API and libraries development API and libraries commercializationThe first 6 years werecharacterized by a very sharpfocus on R&D. A new fundamental GLOBUStheory also requires a wholeinfrastructure to be built. Hence wefirst had to create a developmentenvironment (API/libraries) for thecommercialization.So far we have spent approx.7.0 Mio. of investment capital forR&D. © ai-one inc. 2010 © ai-one inc. USA, ai-one ag, SUI , Diggelmann / Hoffleisch 1985 - 2010

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