A significant shift is underway as the fields of health and biology are re-organizing into the larger ecosystems of information sciences and complexity sciences. The era of big data is transforming all economic sectors including health and biology. Three big health data streams are being integrated into a standardized investigative method in the realization of personalized medicine – creating individualized risk profiles and interventions such that medical conditions may be combatted during the 80% of their life-cycle while they are still pre-clinical. These three big health data streams are traditional medical data, ‘omics’ data (genomics, microbiomics, proteomics, etc.), and biometric quantified-self daily analytic data. Sequencing costs have continued to decrease such that consumer ‘omics’ data is increasingly available. Simultaneously, the potentially fast-arriving wearable electronics platform (smartwatches, disposable patches, augmented eyewear, etc.) means that it could become possible to unobtrusively collect vast amounts of previously-unavailable objective metric data for each individual and parlay this into personalized physical and mental health optimization platforms. Two experimental protocols are presented here putting this model of integrated health data streams into action and extending recent social intelligence genomics research into the realm of cognitive performance genomics. The DIYgenomics Quantified Creativity study investigates potential linkage between personal genomics and the creative process of the individual. The DIYgenomics Thinking Fast and Slow study examines cognitive bias in thinking (loss aversion and optimism bias) versus personal genomic profiles. The studies integrate big health data streams including traditional health data, personal genomics, quantified self-reported data, standardized questionnaires, and personalized intervention.

Online social media has become an integral part of our social lives. Online social interactions are distinct from face-to-face interactions. Different social media types have enabled novel forms of social exchanges to take place. Individuals vary greatly in their online behaviors and preferences. The current research argued for the significance of understanding individual differences in social media behaviors from brain structure variability. Using a novel approach that combined methodologies from personality and neuroscience, this research found that variations in social media behaviors and preferences were reliably reflected in brain structure. Interestingly, the general preference for an online mode of social interaction reflected decreased volumes of grey matter in regions involved in facial and speech processing. The associations between patterns of media behaviors and brain structure obtained in this research had demonstrated the feasibility of adopting a neuroscience approach to explain the complex differences in media behaviors.

This paper proposes techniques for preliminary disease detection and personal symptom tracking adopting concepts and methods of web information retrieval. The proposed approaches are inspired by web users’ behavior. People look for information of symptoms from Internet. Therefore, considering information in Web pages, the developed system proposes possible diseases related to one or more queried symptoms. Moreover, these queried symptoms would be recorded in the query log so that the user could utilize these records to trace the history of symptoms, further to manage their own health or provide them to doctors as reference. As ranking detected diseases needs professional knowledge, we instead evaluate relevancy of retrieved sentences containing detected diseases in both strict and lenient metrics. Experimental results support the proposed ranking approach. The techniques described in this paper are also implemented to develop an Android application called “Health Generation”. In this application, the detected disease is further linked to its Wikipedia introduction and the nearby clinics are listed. Users can utilize the GPS function provided by cell phones to plan the route for them. Through the proposed approaches and the application to provide medical information and solutions according to users’ need and further to help users manage their health is the aim of this research.

Understanding the human mind and increasing individual happiness are important goals in artificial intelligence (AI) and well-being science. The recent revolution in portable self-tracking devices in the data-driven wellness movement and participatory-driven wellness communities, such as the Quantified Self community, provides us with new opportunities to collect psychological or physiological data for understanding the human mind.  While new technologies make it possible to track our daily behavior and various biological signals such as physiological or genetic data more easily, one of the important remaining challenges is to discover our own truly meaningful personal values. Citizen science, scientific research by crowdsourcing or human-based computation, is a new and challenging framework that promotes interdisciplinary research in the fields of computer science, life/brain science, and social psychological/behavioral science, which may introduce new paradigms to the AI community. We have been working on citizen science projects related to the area of personal genomics and have developed a personal genomics information environment named MyFinder. The developed platform supports the search for our inherited talents and maximizes our potential for a meaningful life. In particular, we are interested in the human mind and the personal genome. In this paper, we introduce our MyFinder Project and present the results of a recent study on “social intelligence genomics and empathy building”, and discuss issues involved in exploring our mind within the context of personal genomics.

This paper describes a newly proposed approach towards investigating how social humanoid robots can learn creative conceptualizations through interaction with children in metaphor-guided pretense play using a componential creativity framework. We describe this metaphor-guided pretense play approach and then illustrate it by describing a social robot interaction pretense play scenario between a child and a humanoid robot.

The marketplace for definitions and theories of creativity is crowded indeed. No hard consensus exists on the elements of an all-embracing theory, or on what specific sub-processes and representations are required to support creativity, either in humans or in machines. Yet commonalities do exist across theories: a search for novelty and utility is implied by most theories, as is the notion that an innovation can be considered creative only if it is not too novel, and can be adequately grounded in the familiar and the understandable. Computational creativity (CC) is the pursuit of creative behavior in machines, and seeks inspiration from both AI and from human psychology. As a practical engineering endeavor, CC can afford to adopt a cafeteria approach to theories of creativity, taking what it needs from different theories and frameworks. In this paper we present a vision for CC research in the age of the Web, in which CC is provided on tap, via a suite of Web services, to any third-party application that needs it. We argue that this notion of Creativity as a Service – which is already a popular business model for human organizations – will allow CC researchers and developers to build ad-hoc mash-ups of whatever processes and representations are most suited to a given application. By offering CC as a centralized service, we can collect statistics on the most useful mash-ups, and therein obtain a new empirical basis for theorizing about creativity in humans and in machines.

We believe that current research in creativity (especially in artificial intelligence and to a great extent psychology) focuses too much on the product and on exceptional (big-C) creativity. In this paper we want to argue that creative thinking and creative behavior result from the continuation of typical human cognitive development and that by looking into the early stages of this development, we can learn more about creativity. Furthermore, we wish to see analogy as a core mechanism in human cognitive development rather than a special skill among many. Some developmental psychology results that support this claim are reviewed. Analogy and metaphor are also seen as central for the creative process. Whereas mainstream research in artificial creativity and computational models of reasoning by analogy stresses the importance of matching the structure between the source and the target domains, we suggest that perceptual similarities play a much more important role, at least when it comes to creative problem solving. We provide some empirical data to support these claims and discuss their consequences.

Cognitive structures are the foundation of Jean Piaget’s Genetic Epistemology. Yet the elusive question remains: “What are Piaget’s cognitive structures?” and more importantly, “How can they be represented computationally?” Piaget described the monad as an immaterial, weightless, dimensionless entity, while he referred to a scheme as both process and structure. This paper explores an approach to combining the notions of monad and scheme to create a simple knowledge representation. Building upon the work of several authors, notably Jean Piaget, Ryszard Michalski, and Roland Hausser, the neural proposition is the central cognitive structure of the PAM-P2 cognitive system.

In this paper, we will consider creativity as a necessary psychological property for the individual’s development and survival. This standpoint is definitely in opposition to the empiricist behavioral theory on the one side, and to the innate gestalt theory on the other. As a constructivist, Piaget considered that understanding is a process of invention, or re-construction by invention, and that any knowledge emerging only from the environment’s constraints was not true knowledge even if its owner believed so. Within the theory of equilibrations, what interests us here is spontaneous creativity, when the child discovers new means of interacting with the environment in order to reduce his internal conflicts, thus keeping his thoughts harmonious.

This paper reports a model of a developmental agent. It is inspired by some characteristics of Piagets sensorimotor stage. During this stage essential skills for creative thinking are developed. Our computational model attempts to shed some light about how these abilities arise and, in this way, contribute to the study of the developmental side of computational creativity.