Posts Tagged ‘cognitive media’

Augmented Social Cognition for Consumer Health and Wellness

In a recent Wall Street Journal essay, Marc Andreessen wrote: “Software is eating the world. Over the next 10 years, I expect many more industries to be disrupted by software. Healthcare and education are next up for fundamental software-based transformation.”

What is the impending disruption in healthcare, and what new technologies are driving it? I argue that the problem is not healthcare but health: creating new consumer-centric approaches to health and wellness that increase engagement, improve health literacy and promote behavior change.

The web is evolving from information (portals) to interaction (social/mobile) to influence: shaping attitudes and behaviors. This creates a unique opportunity to address the problem of consumer health and wellness. But, to do this effectively requires a new kind of technology: user modeling. It also requires an innovation methodology that is fundamentally about people, not technology.

At PARC, our research in Augmented Social Cognition is centered around the confluence of three technologies: social, mobile, and user modeling. I discuss these technologies and explain how we leverage artificial Intelligence (AI) and case-based reasoning (CBR) techniques to model users and create effective and sustainable behavior change.

Invited talk at CBR-2013 Industry Day, Saratoga Springs, NY, July 8, 2013.
VIEW SLIDES:

From Dr Google, to Dr Facebook, and beyond…

I recently appeared on the ABC Health Report radio program.

Joel Werner: Do you ever go online to search for symptoms that you’re experiencing? I do it all the time, and it’s a trend that has picked up the nickname ‘Dr Google’. For Ashwin Ram, Dr Google is just one step on the path to future healthcare…

The Intelligent Web: Shaping Behavior at the Intersection of Health, Wealth, & Choice

The web is evolving from information (portals) to interaction (social/mobile). The next stage will be about influence: shaping attitudes and behaviors. To do this effectively requires a new kind of technology: user modeling. It also requires an innovation methodology that is fundamentally about people, not technology.

I discuss three big ideas in innovation for consumer engagement and behavior change, and illustrate using examples from healthcare, education, and financial services.

Invited keynote at Amplify: Business Innovation and Thought Leadership, June 2013, Australia.

SLIDES:

Socio-Semantic Conversational Information Access

We develop an innovative approach to delivering relevant information using a combination of socio-semantic search and filtering approaches. The goal is to facilitate timely and relevant information access through the medium of conversations by mixing past community specific conversational knowledge and web information access to recommend and connect users and information together. Conversational Information Access is a socio-semantic search and recommendation activity with the goal to interactively engage people in conversations by receiving agent supported recommendations. It is useful because people engage in online social discussions unlike solitary search; the agent brings in relevant information as well as identifies relevant users; participants provide feedback during the conversation that the agent uses to improve its recommendations.

Socio-Semantic Conversational Information Access

by Saurav Sahay, Ashwin Ram

WWW-2012 Workshop on Community Question Answering on the Web (CQA-12).

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

Social Media for Health and Wellness 2.0

The Internet has surpassed physicians as the leading source of health information. With the advent of the social web, Health 2.0 is emerging as a strong segment with 34% of consumers using social resources such as blogs and forums to locate health information. Yet information overload leads to “search engine fatigue” that discourages users.

We advocate a consumer-centric approach to healthcare information access that increases engagement and improves health literacy.  Artificial Intelligence (AI) techniques can be used to support human effort, creating a new generation of “intelligent web” technologies. These technologies can combine the benefits of the “information web” (timely, relevant health information) with those of the “social web” (human interaction, support, comfort). Our vision is to promote well-being and prevention before illness, support and information during illness, and comfort to family and friends in a natural, social, yet private manner.

Invited talk at Humana Innovation Conference: Connect, Collaborate, Create (C3), Louisville, KY, September 23, 2011.

SoCS Computational Models and Techniques: A Case Study

I spoke today at the NSF Workshop on Social-Computational Systems (SoCS) on Mike Pazzani‘s Computational Models and Techniques panel with Tuomas Sandholm, Lise Getoor, and Tina Eliassi. We were asked to address the questions of what computation can teach us about socially intelligent systems, and what problems are encountered when applying existing technologies to such systems.

I focused on two key SoCS challenges : impedance mismatch, and research-at-scale. Let me explain.

What can computation teach us about SoCS? If we begin with technology, we’ll encounter the key challenge of “impedance mismatch” between people and technology. The technology, however good, may not address people’s needs. Instead, let’s reverse the question: What do socially intelligent systems teach us about computational technology?

Consider, as a case study, the problem of education: building a SoCS system to help students learn. Our first pass was a collaborative learning site with a state-of-the-art collaboration platform, a kind of “Google Docs meets WebEx meets Etherpad meets Skype on steroids”. While the site was useful, we learned that students didn’t use most of the features we had built. The issue was impedance mismatch: the technology did not address education problems from a student perspective.

What, then, are these problems? There are two: Access (scale) and engagement. To tackle the impedance mismatch, we need to design technology that provides the right affordances (in the Gibsonian sense) for student behaviors that address those problems.

We created a vision for Open Social Learning that blends, not Google Docs and WebEx, but Facebook and World of Warcraft. With funding from NSF, NIH, GRA, and Gates/Hewlett NextGenLC, and partnerships with MIT, Yale, NYU, and many others, we rethought the site from Education to SoCS to Learning Theories to Design Principles to Affordances to Architecture to User Experience (UX) to Mechanisms. (See slides and references below.) This process resulted in a fundamentally disruptive idea, one driven not by technology but by the SoCS it was to support.

Only then did it make sense to think about Computation: really real-time collaboration technologies for a highly interactive experience; intelligent recommender systems to help learners connect with relevant content and other learners; mining and analytics to assess learner outcomes; and reputation techniques to establish social capital.

The new OpenStudy.com is an Open Peer-to-Peer Social Learning Community, a place that matches learners studying the same things into live “massively multiplayer study sessions“. The problems of access (scale) and engagement are addressed through two mechanisms: A Luis von Ahn approach where the social community scales itself, and a kind of gamification in which everyone is on the same team.

Great idea—but how do we know it works? The education literature is full of great ideas that don’t work in practice. SoCS data research involves studying large-scale communities; the same applies to SoCS technology design. This is the research-at-scale challenge. Laboratory studies don’t prove much; the research fundamentally requires scale.

After 9 months, OpenStudy has grown into a vibrant community that both provides value to its users and serves as a “living lab” to study and validate the ideas. We’re continuing to research how new technologies can be combined to address the problem of education in a manner that is highly scalable yet interactive and engaging.

To understand what socially intelligent systems teach us about computation, then, requires a new methodology comprised of old ideas about design thinking brought into the new world of Social-Computational Systems at a massive scale.

READINGS

P Adams (2009). Designing for Social Interactions.

Terry Anderson (2007). Distance Learning: Social Software’s Killer App?

J Daniel (1996), cited in JS Brown (2007). Minds on Fire: Open Education, The Long Tail, and Learning 2.0.

RA DeMillo (2011). Abelard to Apple: The Fate of American Colleges and Universities in the Twenty-First Century.

R Friedrich, M Peterson, A Koster (2011). The Rise of Generation C.

Gates Foundation study: JM Bridgeland, JJ Dilulio Jr, KB Morrison (2006), The Silent Epidemic: Perspectives of High School Dropouts.

D Thomas & JS Brown (2011). A New Culture of Learning.

More readings at: Massively Multiplayer Online—Learning?

SLIDES

Open Social Learning Communities

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. The disruption is fueled by a staggering growth in demand. It is estimated that there will be 100 million students qualified to enter universities over the next decade. To educate them, a major university would need to be created every week.

Universities have responded to this need with Open Education Resources—thousands of free, high quality courses, developed by hundreds of faculty, used by millions worldwide. Unfortunately, online courseware does not offer a supporting learning experience or the engagement needed to keep students motivated. Students read less when using e-textbooks; video lectures are boring; and retention and course completion rates are low.

Therein lies the core problem: How to engage a generation of learners who live on the Internet yet tune out of school, who seek interaction on Facebook yet find none on iTunes U, who need community yet are only offered content. We propose a new approach to this problem: open social learning communities, anchored with open content, providing an interactive online study group experience akin to sitting with study buddies on a world-wide campus quad.

This solution is enabled by state-of-the-art web technologies: really real-time collaboration technologies for a highly interactive experience; intelligent recommender systems to help learners connect with relevant content and other learners; mining and analytics to assess learner outcomes; and reputation techniques to establish social capital.  We will discuss these technologies and how they can be combined to address the problem of education in a manner that is highly scalable yet interactive and engaging.

This approach can be used for other types of learning communities. We will show an application to healthcare information access to help consumers learn about their healthcare questions and needs.

Keynote talk at SIPA Conference: Entrepreneurship—Idea Wave 3.0, Mountain View, CA, November 12, 2011.
 
Keynote talk at the International Conference on Web Intelligence, Mining and Semantics (WIMS-11), Sogndal, Norway, May 27, 2011.
 

View the talk:

videolectures.net/wims2011_ram_learning

Read the paper:

www.cc.gatech.edu/faculty/ashwin/papers/er-11-04.pdf

View the slides:

 

 
 

CBArch: A Case-Based Reasoning Framework for Conceptual Design of Commercial Buildings

The paper describes the first phase of development of a Case-Base Reasoning (CBR) system to support early conceptual design of buildings. As specific context of application, the research focuses on energy performance of commercial buildings, and the early identification of energy-related features that contribute to its outcomes. The hypothesis is that bringing knowledge from relevant precedents may facilitate this identification process, thus offering a significant contribution for early analysis and decision-making.

The paper introduces a proof-of-concept for such a system, proposing a novel integration of Case-Based Reasoning, Parametric Modeling (Building Information Modeling), and Ontology Classification. While CBR provides a framework to store and retrieve cases at an instance level, Parametric Modeling offers a framework for rule-based geometric adaptation and evaluation. The ontology is intended to provide a semantic representation, so that new design concepts can be created, classified and retained for further reuse. Potential advantages and limitations of this three-level integration approach are discussed along with recommendations for future development.

CBArch: A Case-Based Reasoning Framework for Conceptual Design of Commercial Buildings

by Andrés Cavieres, Urjit Bhatia, Preetam Joshi, Fei Zhao, Ashwin Ram

AAAI-11 Spring Symposium on Artificial Intelligence and Sustainable Design
www.cc.gatech.edu/faculty/ashwin/papers/er-11-07.pdf
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