The Learning Layer: Making it a Reality

The Learning Layer: October 2011Making it a Realitybuilding learning into the fabric of business® © ManyWorlds, Inc October 17, 2011 Slide 1

We are Now Officially in the “Era of Adaptation” inwhich Systems Learn from their Experiences with Us . . . Mainframes PCs Web 1.0 Web 2.0 Information Technology Trends Adaptation = Dominant Themes Connectivity Speed 1950 1960 1970 1980 1990 2000 2010 2020October 17, 2011 © ManyWorlds, Inc. Slide 2

Adaptive Systems: Overview Traditional Adaptive Behavior System Users System Patterns Users Socially Unaware Socially AwareOctober 17, 2011 © ManyWorlds, Inc. Slide 3

The Adaptive Discovery Imperative . . . Ever Increasing Flood of Content and Activity Streams Stuff I subscribe to Stuff I need to know now Subscribe/Follow Adaptive Discovery/ Learning Layer Noise! Stuff I know I need to know now SearchOctober 17, 2011 © ManyWorlds, Inc Slide 4

The Learning Layer . . . www.learninglayer.comOctober 17, 2011 © ManyWorlds, Inc Slide 5

The Learning Layer: Summary Picture an ethereal network that integrates and helps you navigate all of your organization’s information, apps, and people . . . . . . with continuous, intelligent recommendations of information, apps, and people tuned to your personal requirements and activities . . . . . . coupled with detailed explanations of the recommendations . . . . . . and with the ethereal network automatically optimizing itself for your benefit based on its experiences with you and your colleagues. This is The Learning Layer, and it is now a reality . . .October 17, 2011 © ManyWorlds, Inc. Slide 6

Learning Layer Architecture Summary A learning layer is a new type of enterprise system that enables learning to be woven directly into work processes and activities. It is characterized by the following features:  A network-based architecture, where relationships can be by degree  Existing and new social networks, content, and applications can all be seamlessly integrated into a single, complementary network layer  Process workflow can be seamlessly integrated into the network  Learns automatically from its experiences with users  Delivers the results of its learning back to users in the form of intelligent recommendations (of content and people) and associated explanations  Continuously and intelligently self-modifies (adapts) its network structure based on its learningOctober 17, 2011 © ManyWorlds, Inc. Slide 7

Learning Layer Applications Any knowledge-intense organization or process can benefit from a learning layer. Sample applications include:  Enhancing innovation by fostering large-scale and high value serendipity  Taming complex process environments by adapting workflow and the delivery of supporting to the specific experience and skills of the user  Transforming standard training programs into real-time learning that occurs while users are just doing their job  Enhancing customer relationships by adaptively integrating into their knowledge-based processes  Amplifying knowledge transfer from supplier and partner networks, and/or across projects  Retaining and transferring tacit knowledge throughout the human capital life cycle: turning an organization’s “cognitive surplus” into real valueOctober 17, 2011 © ManyWorlds, Inc. Slide 8

Lessons from the Brain . . . Learning Occurs through the Adjusting of the Strength of Connections among Nodes of a Fuzzy Network . . . Human Learning Brain Layer .4 .3 1011 nodes and .9 1014 reconfigurable X .6 connections .3 .2October 17, 2011 © ManyWorlds, Inc. Slide 9

The Learning Layer Structure in Context Fuzzy Networks vs. Hierarchies Scope ofApplication Navigation Scales Efficiently Fuzzy Networks Better Adaptive with Use Hierarchies Good Enough Time October 17, 2011 © ManyWorlds, Inc. Slide 10

The Learning Layer Emerges System People Interactions Learning Behavioral Awareness Self-Modifying Recommendations Recommendations to UsersOctober 17, 2011 © ManyWorlds, Inc. Slide 11

Leading Practice Collaborative “Stack” The Adaptive IT Stack ManyWorlds’ Synxi Learning Layer Learning Layer Apps Social Platforms Social Layer (NewsGator, Yammer, Tibbr, etc.) Process Layer Content & SharePoint 2010 Applications Layer Cloud (Internal or External)October 17, 2011 © ManyWorlds, Inc Slide 12

Building a Learning Layer for SharePoint® and Beyond . . .Summary Synxi™ Learning Layer Architecture Sample Synxi Learning Layer Interfaces/Functions Knowledge Discovery Learning Layer Learning Layer Expertise Discovery (content & topics) Visualization Architect Interface Synxi Learning Layer Network & Algorithms (may be hidden) Integrated Learning Network Behavioral Information & Inferences Continuous Index/Conversion to Learning Network SharePoint Social Networks Other . . .October 17, 2011 © ManyWorlds, Inc. Slide 13

Adaptive Knowledge and Expertise Discovery:Integrated SharePoint/Tibbr Implementation Example . . . Personalized Recommendations Received While Using Tibbr Personalized Recommendations Received While Using SharePointOctober 17, 2011 © ManyWorlds, Inc Slide 14

Integrated Learning Layer Makes it Happen . . . Knowledge & Expertise Social Discovery Platform SharePoint 2010 you are here Synxi Learning Layer: Integrated Contextualization you are here contextual equivalentOctober 17, 2011 © ManyWorlds, Inc Slide 15

Adaptive Discovery: How it Works Recommendations of Content, People, and Topics Popularity New to you? Relevancy & Quality Recency Ratings People like you Inferred Relative Expertise Topic 1 Topic 2 Topic 3 Topic 4 . . . . . . . . . . . . . Topic N Personalization Inferred Interests Topic 1 Topic 2 Topic 3 Topic 4 . . . . . . . . . . . . . Topic N ContextualizationOctober 17, 2011 © ManyWorlds, Inc Slide 16

ManyWorlds’ Epiture® SaaS PlatformMaking a Comprehensive Learning Layer a Reality Today! AI Epiture KM/Content/ Learning Social Portals Layer Networks Technology Adaptive Processes Examples . . .October 17, 2011 © ManyWorlds, Inc. Slide 17

Self-Evolving Networks of KnowledgeAutomatic Taxonomy Adaptation in Action . . .October 17, 2011 © ManyWorlds, Inc. Slide 18

Tunable Recommendations with Detailed ExplanationsAnd Sometimes with a Little Humor . . .October 17, 2011 © ManyWorlds, Inc. Slide 19

Visualization of People NetworksDynamically Generated from Inferences of InterestsOctober 17, 2011 © ManyWorlds, Inc. Slide 20

Sample ManyWorlds Learning Layer InventionsPatent/ Patent/App. No. Invention Title App. No. Invention Title7,904,341 Adaptive Commerce Systems and Methods 13/267,984 Contextually Transformed Learning Layer7,860,811 Adaptive Recommendation Explanations 13/267,997 Contextually Integrated Learning Layer7,831,535 Adaptive Knowledge Lifecycle Management . . . 13/268,007 Contextual Scope-Based Discovery Systems7,739,231 Mutual Commit People Matching Process 13/268,025 Expertise Discovery Methods and Systems7,606,772 Adaptive Social Computing Methods 13/268,035 Portable Inferred Interest and Expertise Profiles7,539,652 Adaptive Self-modifying and Recombinant Systems 13/268,047 Serendipitous Recommendations System and Method7,526,464 Adaptive Fuzzy Network System and Method 13/268,137 Activity Stream-Based Recommendations System and Method7,526,459 Adaptive Social and Process Network Systems 13/268,145 Adaptive Expertise Clustering System and Method7,526,458 Adaptive Recommendations Systems 13/269,958 Physical Site-Contextualized Recommender7,493,294 Mutually Adaptive Systems 13/269,969 Physiological-Responsive Recommender System and Device7,375,838 Method and System for Customized Print Publication and 13/269,979 System and Method for Adaptive Soundtracks Management6,795,826 Fuzzy Content Network Management and Access 13/270,019 System and Method for Adaptive Videos12/773,119 Mutual Interest Inferencing System and Method 13/270,027 System and Method for Adaptive Games12/344,537 Adaptive Social Network Management 13/270,039 System and Method for Adaptive Media Instances12/172,236 Influence-based Social Network Advertising 13/270,049 Temporally Sequenced Recommendations in Subscription-Based Systems12/179,053 Location-Aware Adaptive Advertising 13/270,054 Temporally Sequenced Adaptive Recommendations of Activities12/139,487 Adaptive Computer-based Personalities 13/212,755 Proximity-based People Matching System and Method11/690,823 Affinity Propagation in Adaptive Network-Based Systems 13/212,774 Environment-Responsive People Matching System and Method12/978,124 Explanatory Advertising Systems and Methods 13/206,234 Gesture-Responsive Advertising System and Method12/893,958 Adaptive Knowledge Lifecycle Management Methods 13/206,265 Gesture-Responsive Advertising Process13/038,111 Contextual Commerce Systems and Methods 13/208,602 Environment-Responsive Advertising System and Method13/212,676 People Matching in Subscription-based Communities 13/208,613 Dynamically-Assembled Advertisement System and Method13/212,729 Search-based People Matching System and Method 13/208,631 Subscription-based Community Advertising System and Method October 17, 2011 © ManyWorlds, Inc. Slide 21

ManyWorlds’ Learning Layer Products/ServicesSummary ManyWorlds can deliver internet or intranet learning layer capabilities to your organization hosted on your own IT infrastructure, or through ManyWorlds-managed (SaaS) environments. We currently offer:  Full-function Epiture stand-alone learning layer SaaS platform  Synxi adaptive knowledge and expertise discovery web part/solutions for SharePoint 2010 (with additional modules and connectors to other social network/collaborative platforms available soon)  ManyWorlds-managed (SaaS) SharePoint 2010 with Synxi adaptive knowledge and expertise discovery web part/solutions (with additional modules available soon) www.synxi.comOctober 17, 2011 © ManyWorlds, Inc. Slide 22

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The Learning Layer: Making it a Reality

by SteveFlinn on Mar 23, 2010

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