Health : Healthcare :: Learning : Education

With the advent of open education resources, social networking technologies and new pedagogies for online and blended learning, we are in the early stages of a significant disruption in current models of education. ‘Learning’ is beginning to peel away from ‘Education’ as a separate market, with its own set of opportunities and challenges for practitioners, technologists, and entrepreneurs. While ‘education’ is driven by schools, colleges, and governments, ‘learning’ focuses on empowering the individual to take charge of their learning.

Interestingly, a similar phenomenon is occurring in healthcare, fueled by the confluence of similar trends and technologies: open health resources, social networking technologies and new methodologies for consumer engagement. ‘Health’ is starting to emerge as a separate and disruptive market, with its own opportunities and challenges. While ‘healthcare’ is driven by providers, payers, and governments, ‘health’ focuses on empowering the consumer to take charge of their health and wellness. 

In this talk, I discuss recent trends in these two industries, explain why they are analogous, and discuss opportunities for user experience, big data, analytics and social capital research. I provide examples of social, mobile, and game technologies that are creating the disruption, and highlight key research challenges that are yet to be addressed.


Invited talk at UC Berkeley, iSchool “Thought Leaders in Data Science and Analytics”, April 11, 2012. 

SLIDES

Taking Advantage of Now: The Consumer Health & Wellness Scene

Taking Advantage of Now:
The Consumer Health & Wellness Scene

  • Lifestyle Innovation: defining successful business models
  • Opportunities on the horizon for corporates and investors
  • Innovation Partnering: strategic alliances at work to maximize consumer health & wellness appetite
  • Moving Goalposts: Market assessment to increase product pipelines

Moderator:

  • Will Rosenzweig, co-founder and Managing Director – Physic Ventures

Panelists:

  • Mark Murrison, President, Marketing and Innovation – MDVIP
  • Jack Young, Senior Investment Manager – Qualcomm Ventures
  • Mark Kapcynski, Corporate Development – Experian Consumer Direct
  • John Deedrick, Managing Director – Linn Grove Ventures
  • Ashwin Ram, Research Fellow & Area Manager of Socio-cognitive Computing, PARC a Xerox company
Invited panel presentation at IBF Consumer Health & Wellness Innovation Summit, Newport Beach, CA, February 9, 2012
ibfconferences.com/health-wellness-innovation-summit.html

Transforming the Industry: Watson in Education

Watson, named after IBM founder Thomas J. Watson, was built by a team of IBM scientists with valuable help from research partners from Carnegie Mellon University, University of Texas, University of Southern California, University of Massachusetts, University of Trento (Italy), MIT, RPI, and the University of Albany. The team set out to accomplish a grand challenge—to build a computing system that rivals a human’s ability to answer questions posed in natural language with speed, accuracy and confidence. Watson passed its first test on Jeopardy! in February 2011, but the real test will be in applying the underlying systems, data management and analytics technology across different industries, especially in education.

Invited panel presentation at IBM Watson in Education: Transforming the Industry, IBM Almaden Research Center, November 16, 2011

Socio-Semantic Conversational Information Access

The main contributions of this thesis revolve around development of an integrated conversational recommendation system, combining data and information models with community network and interactions to leverage multi-modal information access. We have developed a real time conversational information access community agent that leverages community knowledge by pushing relevant recommendations to users of the community. The recommendations are delivered in the form of web resources, past conversation and people to connect to. The information agent (cobot, for community/ collaborative bot) monitors the community conversations, and is ‘aware’ of users’ preferences by implicitly capturing their short term and long term knowledge models from conversations. The agent leverages from health and medical domain knowledge to extract concepts, associations and relationships between concepts; formulates queries for semantic search and provides socio-semantic recommendations in the conversation after applying various relevance filters to the candidate results. The agent also takes into account users’ verbal intentions in conversations while making recommendation decision.

One of the goals of this thesis is to develop an innovative approach to delivering relevant information using a combination of social networking, information aggregation, semantic search and recommendation techniques. The idea is to facilitate timely and relevant social information access by mixing past community specific conversational knowledge and web information access to recommend and connect users with relevant information. Language and interaction creates usable memories, useful for making decisions about what actions to take and what information to retain.

Cobot leverages these interactions to maintain users’ episodic and long term semantic models. The agent analyzes these memory structures to match and recommend users in conversations by matching with the contextual information need. The social feedback on the recommendations is registered in the system for the algorithms to promote community preferred, contextually relevant resources. The nodes of the semantic memory are frequent concepts extracted from user’s interactions. The concepts are connected with associations that develop when concepts co-occur frequently. Over a period of time when the user participates in more interactions, new concepts are added to the semantic memory. Different conversational facets are matched with episodic memories and a spreading activation search on the semantic net is performed for generating the top candidate user recommendations for the conversation.

The tying themes in this thesis revolve around informational and social aspects of a unified information access architecture that integrates semantic extraction and indexing with user modeling and recommendations.

Read the dissertation:

Socio-Semantic Conversational Information Access

by Saurav Sahay

PhD dissertation, College of Computing, Georgia Institute of Technology, November 2011.

smartech.gatech.edu/handle/1853/42855

Crowdsourcing: From Phenomenon to Business Model

Crowdsourcing is changing both the way we work, as well as the way Internet applications are designed and delivered. It’s no longer just the domain of technologists (who can now achieve breakthroughs together never before achievable); crowdsourcing is now ripe for enterprise professionals to understand and leverage the possibilities for their business goals.

From Wikipedia and YouTube, crowdsourcing has moved to a $5B “crowd worker” industry with applications proliferating for productivity, research, marketing, advertising, creative development, corporate workflow management, language translations, and much more — see a list of projects here.

I discuss social networks as a kind of crowdsourcing, with unique benefits and challenges.

Invited panel presentation at PARC Forum, Palo Alto, CA, November 10, 2011
 

View the panel discussion:

www.parc.com/event/1456/crowdsourcing-ceo-expert-panel.html

Case-Based Reasoning Research And Development

This book constitutes the refereed proceedings of the 19th International Conference on Case-Based Reasoning, held in London, UK, in September 2011. The 32 contributions presented together with 3 invited talks were carefully reviewd and selected from 67 submissions. The presentations and posters covered a wide range of CBR topics of interest both to practitioners and researchers, including CBR methodology covering case representation, similarity, retrieval, and adaptation; provenance and maintenance; recommender systems; multi-agent collaborative systems; data mining; time series analysis; Web applications; knowledge management; legal reasoning; healthcare systems and planning systems.
Find the book:

Case-Based Reasoning Research and Development | Lecture Notes in Artificial Intelligence, Vol. 6880

edited by Ashwin Ram and Nirmalie Wiratunga

Springer, October 20, 2011, ISBN 978-3-642-23290-9
www.springer.com/computer/ai/book/978-3-642-23290-9

Robust and Authorable Multiplayer Storytelling Experiences

Interactive narrative systems attempt to tell stories to players capable of changing the direction and/or outcome of the story. Despite the growing importance of multiplayer social experiences in games, little research has focused on multiplayer interactive narrative experiences. We performed a preliminary study to determine how human directors design and execute multiplayer interactive story experiences in online and real world environments. Based on our observations, we developed the Multiplayer Storytelling Engine that manages a story world at the individual and group levels. Our flexible story representation enables human authors to naturally model multiplayer narrative experiences. An intelligent execution algorithm detects when the author’s story representation fails to account for player behaviors and automatically generates a branch to restore the story to the authors’ original intent, thus balancing authorability against robust multiplayer execution.

Read the paper:

Robust and Authorable Multiplayer Storytelling Experiences

by  Mark Riedl, Boyang Li, Hua Ai, Ashwin Ram

in Seventh International Conference on AI and Interactive Digital Entertainment (AIIDE-2011).
www.cc.gatech.edu/faculty/ashwin/papers/er-11-06.pdf
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