Computational models of aggregated social agents have two major faults: (1) inter-individual entrainment is ignored; and (2) rule-sets governing behavior are invariant to history. Together these shortcomings impede our ability to generate realistic models of complex evolving social processes. To illustrate how even simple couplings within an established dyad generates unexpected outcomes, we present our findings from two computer models (agent-based, particle filter) of married couples. With the use of computational modeling, especially when attempting to capture and articulate trajectories of socially aggregated agents, numerous implicit assumptions are made and yet, many if not most, are without an empirical Figure 1: User interface showing parameter sliderbars that foundation. For example, the standard protocol for creating modify interaction characteristics.

For any given forecasting problem, contributors The Aggregative Contingent Estimation System (ACES; receive information about the crowd's beliefs immediately see http://www.forecastingace.com) is a project funded by after providing their own forecast.

One of the original applications of crowdsourcing the construction of knowledge is Wikipedia, which relies entirely on people to contribute, extend, and modify the representation of knowledge. This paper presents a case for combining AI and wisdom of the crowds for the social construction of knowledge. Our social-computational approach to collective intelligence combines the strengths of human cognitive diversity in producing content and the capabilities of an AI, through methods such as topic modeling, to link and synthesize across these human contributions. In addition to drawing from established domains such as Wikipedia for inspiration and guidance, we present the design of a system that incorporates AI into wisdom of the crowds to develop a knowledge base on sustainability. In this setting the AI plays the role of scholar, as might many of the other participants, drawing connections and synthesizing across contributions. We close with a general discussion, speculating on educational implications and other roles that an AI can play within an otherwise collective human intelligence.

This paper presents work using crowdsourcing to assess explanations for supervised text classification. In this paper, an explanation is defined to be a set of words from the input text that a classifier or human believes to be most useful for making a classification decision. We compared two types of explanations for classification decisions: human-generated and computer-generated. The comparison is based on whether the type of the explanation was identifiable and on which type of explanation was preferred. Crowdsourcing was used to collect two types of data for these experiments. First, human-generated explanations were collected by having users select an appropriate category for a piece of text and highlight words that best support this category. Second, users were asked to compare human- and computer-generated explanations and indicate which they preferred and why. The crowdsourced data used for this paper was collected primarily via Amazon’s Mechanical Turk, using several quality control methods. We found that in one test corpus, the two explanation types were virtually indistinguishable, and that participants did not have a significant preference for one type over another. For another corpus, the explanations were slightly more distinguishable, and participants preferred the computer-generated explanations at a small, but statistically significant, level. We conclude that computer-generated explanations for text classification can be comparable in quality to human-generated explanations.

The current era of internet-facilitated bigger data, better tools, and collective intelligence community computing is accelerating advances in many areas ranging from artificial intelligence to knowledge generation to public health. In the health sector, data volumes are growing with genomic, phenotypic, microbiomic, metabolomic, self-tracking, and other data streams. Simultaneously, tools are proliferating to allow individuals and groups to make sense of these data in a participatory manner through personal health tracking devices, mobile health applications, and personal electronic medical records. Health community computing models are emerging to support individual activity and mass collaboration through health social networks and crowdsourced health research studies. Participatory health efforts portend important benefits based on both size and speed. Studies can be carried out in cohorts of thousands instead of hundreds, and it could be possible to apply findings from newly-published studies with near-immediate speed. One operator of interventional crowdsourced health research studies, DIYgenomics, has several crowdsourced health research studies in open enrollment as of January 2012 in the areas of vitamin deficiency, aging, mental performance, and epistemology. The farther future of intelligent health community computing could include personal health dashboards, continuous personal health information climates, personal virtual coaches (e.g.; Siri 2.0), and an efficient health frontier of dynamic personalized health recommendations and action-taking.

This paper focuses on age-related change in sleep and improves our sleep estimation method by employing the feature of such relation between sleep stage and age. In particular, the wake stage increases as the age increases, while Non-REM stage decrease as the age increase. Using such distinctive features, we propose a new determination sleep stages, and introduce it into for our sleep estimation method based on Genetic Algorithms (GAs), which evolve the sleep stage for each person according to the fitness. To investigate an effectiveness of a new determination of sleep stages, we compare the estimated sleep stages of our method employing the proposed fitness function with that of Hirose’s method. The experimental results suggest that our method employed the proposed discretization of sleep stages has a capability to estimate the sleep stage accurately than Hirose’s method.

This paper introduces the ongoing MyFinder project, which was launched in October 2010. The goals of this project are: (1) to propose an intimate personal genome information environment, MyFinder, which supports the search for our inborn talents and maximizes our potential for a meaningful life; and (2) to contribute to scientific discoveries in the biomedical or psychological research domains through intelligent community computing, or in other words, citizen science. This paper describes our research framework and the ongoing challenges to achieving these two goals. We also discuss the technical and social issues related to possible personal genome applications in non-medical domains.

Self-tracking technologies used by healthy self-experimenters and chronic illness patients are relatively new but offer potential to accelerate the discovery of evidence-based protocols in the fields of human biology and medicine. Among both academic researchers and real-world practitioners in these fields there is an ever-present body of misinformation, leading to the proliferation of myth-based protocols in health-promoting lifestyles and treatment. This collection of four case studies spanning seven years’ worth of observations in a self-experimenting endurance athlete and, later, chronically ill individual, aims to bring to attention themost common incorrect assumptions regarding: nutrition, athletic performance, sleep, and treatment of hypothyroidism. We hope that, with these insights about misleading scientific conclusions, artificial intelligence researchers and anyone interested in developing technological solutions for public health purposes, will explore ways to bridge the gap between academic research and real-world practice of optimizing human biology, and rid the misinformation on bothsides.

Although web-based information extraction systems draw much attention, most of such systems assume that the web directly reflects the real world. For instance, Google flu trend, which is one of the-state-of-the-art influenza surveillance systems, relies on the basic idea that the amount of the influenza related search queries directly correlates with the number of the influenza patients. However, the real patients suffering from influenza symptoms are invisible in the web, because they do not use Internet. Considering this gap, this paper employs an infectious model, assuming that a potential patient utilizes Internet at the first sign of flu. The proposed model improves two types of the state-of-the-art systems, Google based system (from 0.837 correlation to 0.928) and Twitter based system (from 0.898 correlation to 0.918). This study demonstrated that a simple model could easily improve the web-based surveillance.

The Web is a giant repository of resources (Service and content), where Discovery and Recommendation systems are used to deliver the best ranked list of relevant web resources that meet user requirements. Nowadays, these systems are based on the simulation and automation of the user search criteria, considering the relation between consumption trends and the different kinds of users’ relationships with their virtual and physical environment, based on the information from the Social Web and mobile device sensors among others. These systems are executed once an explicit query of the user has been received; however, there are resources that are useful in specific situations, where these resources have high probability to be consumed, but, due to absence of a query they are not recommended to the users. In this regard, the question is: how to make a successful Web Resource Recommendation without the user query? In order to answer the question, this research proposal presents a novel approach to Recommend Web Resources based on Dynamic Prediction of User Consumption on the Social Web, which emulates the user behavior, the resource dynamism and the context opportunities, in real time, catching the best situations to make an asynchronous (unexpected by the user) recommendation of a useful Resources; and boost Web Resources consumption.