CYberinfrastructure for COmparative effectiveness REsearch (CYCORE): improving data from cancer clinical trials

Improved approaches and methodologies are needed to conduct comparative effectiveness research (CER) in oncology. While cancer therapies continue to emerge at a rapid pace, the review, synthesis, and dissemination of evidence-based interventions across clinical trials lag in comparison. Rigorous and systematic testing of competing therapies has been clouded by age-old problems: poor patient adherence, inability to objectively measure the environmental influences on health, lack of knowledge about patients’ lifestyle behaviors that may affect cancer’s progression and recurrence, and limited ability to compile and interpret the wide range of variables that must be considered in the cancer treatment. This lack of data integration limits the potential for patients and clinicians to engage in fully informed decision-making regarding cancer prevention, treatment, and survivorship care, and the translation of research results into mainstream medical care. Particularly important, as noted in a 2009 report on CER to the President and Congress, the limited focus on health behavior-change interventions was a major hindrance in this research landscape (DHHS 2009). This paper describes an initiative to improve CER for cancer by addressing several of these limitations. The Cyberinfrastructure for Comparative Effectiveness Research (CYCORE) project, informed by the National Science Foundation’s 2007 report “Cyberinfrastructure Vision for 21st Century Discovery” has, as its central aim, the creation of a prototype for a user-friendly, open-source cyberinfrastructure (CI) that supports acquisition, storage, visualization, analysis, and sharing of data important for cancer-related CER. Although still under development, the process of gathering requirements for CYCORE has revealed new ways in which CI design can significantly improve the collection and analysis of a wide variety of data types, and has resulted in new and important partnerships among cancer researchers engaged in advancing health-related CI

Framework and Implications of Virtual Neurorobotics

Despite decades of societal investment in artificial learning systems, truly “intelligent” systems have yet to be realized. These traditional models are based on input-output pattern optimization and/or cognitive production rule modeling. One response has been social robotics, using the interaction of human and robot to capture important cognitive dynamics such as cooperation and emotion; to date, these systems still incorporate traditional learning algorithms. More recently, investigators are focusing on the core assumptions of the brain “algorithm” itself—trying to replicate uniquely “neuromorphic” dynamics such as action potential spiking and synaptic learning. Only now are large-scale neuromorphic models becoming feasible, due to the availability of powerful supercomputers and an expanding supply of parameters derived from research into the brain's interdependent electrophysiological, metabolomic and genomic networks. Personal computer technology has also led to the acceptance of computer-generated humanoid images, or “avatars”, to represent intelligent actors in virtual realities. In a recent paper, we proposed a method of virtual neurorobotics (VNR) in which the approaches above (social-emotional robotics, neuromorphic brain architectures, and virtual reality projection) are hybridized to rapidly forward-engineer and develop increasingly complex, intrinsically intelligent systems. In this paper, we synthesize our research and related work in the field and provide a framework for VNR, with wider implications for research and practical applications

CYberinfrastructure for COmparative effectiveness REsearch (CYCORE): improving data from cancer clinical trials

Improved approaches and methodologies are needed to conduct comparative effectiveness research (CER) in oncology. While cancer therapies continue to emerge at a rapid pace, the review, synthesis, and dissemination of evidence-based interventions across clinical trials lag in comparison. Rigorous and systematic testing of competing therapies has been clouded by age-old problems: poor patient adherence, inability to objectively measure the environmental influences on health, lack of knowledge about patients’ lifestyle behaviors that may affect cancer’s progression and recurrence, and limited ability to compile and interpret the wide range of variables that must be considered in the cancer treatment. This lack of data integration limits the potential for patients and clinicians to engage in fully informed decision-making regarding cancer prevention, treatment, and survivorship care, and the translation of research results into mainstream medical care. Particularly important, as noted in a 2009 report on CER to the President and Congress, the limited focus on health behavior-change interventions was a major hindrance in this research landscape (DHHS 2009). This paper describes an initiative to improve CER for cancer by addressing several of these limitations. The Cyberinfrastructure for Comparative Effectiveness Research (CYCORE) project, informed by the National Science Foundation’s 2007 report “Cyberinfrastructure Vision for 21st Century Discovery” has, as its central aim, the creation of a prototype for a user-friendly, open-source cyberinfrastructure (CI) that supports acquisition, storage, visualization, analysis, and sharing of data important for cancer-related CER. Although still under development, the process of gathering requirements for CYCORE has revealed new ways in which CI design can significantly improve the collection and analysis of a wide variety of data types, and has resulted in new and important partnerships among cancer researchers engaged in advancing health-related CI

Virtual Neurorobotics (VNR) to Accelerate Development of Plausible Neuromorphic Brain Architectures

Traditional research in artificial intelligence and machine learning has viewed the brain as a specially adapted information-processing system. More recently the field of social robotics has been advanced to capture the important dynamics of human cognition and interaction. An overarching societal goal of this research is to incorporate the resultant knowledge about intelligence into technology for prosthetic, assistive, security, and decision support applications. However, despite many decades of investment in learning and classification systems, this paradigm has yet to yield truly “intelligent” systems. For this reason, many investigators are now attempting to incorporate more realistic neuromorphic properties into machine learning systems, encouraged by over two decades of neuroscience research that has provided parameters that characterize the brain's interdependent genomic, proteomic, metabolomic, anatomic, and electrophysiological networks. Given the complexity of neural systems, developing tenable models to capture the essence of natural intelligence for real-time application requires that we discriminate features underlying information processing and intrinsic motivation from those reflecting biological constraints (such as maintaining structural integrity and transporting metabolic products). We propose herein a conceptual framework and an iterative method of virtual neurorobotics (VNR) intended to rapidly forward-engineer and test progressively more complex putative neuromorphic brain prototypes for their ability to support intrinsically intelligent, intentional interaction with humans. The VNR system is based on the viewpoint that a truly intelligent system must be driven by emotion rather than programmed tasking, incorporating intrinsic motivation and intentionality. We report pilot results of a closed-loop, real-time interactive VNR system with a spiking neural brain, and provide a video demonstration as online supplemental material

A McKibben muscle arm learning equilibrium postures

In designing artificial systems for studying motor control in humans and other organisms a key point to consider is the complexity reached by brain and body in their developmental stages. An artificial system whose brain and body complexity is shaped according to developmental stages might allow understanding weather, for example, newborn infants, infants, and adults use different neural mechanisms to cope with the same motor control problems. This article proposes an artificial system which aims at becoming a tool to study this type of problems. The system has a brain and body endowed with a set of minimal bio-mimetic features: (a) neural maps activated by receptive fields; (b) connections plasticity changed by Hebbian rule; (c) robotic arm actuated by a McKibben muscle. The arm autonomously learns to reach specific positions in space under the effect of gravity and for different load conditions. The results suggest that a fast and incremental goalaction mapping formation could constitute the computational mechanism underlying the neural growth and plasticity of an early developed brain at the onset of reaching. The same mechanism also allows a first approximate solution for load compensation avoiding the use of more sophisticated internal models (developed in further brain and body developmental stages). This paper aims to be a preliminary study on the feasibility of this approach

Cognitive Computing: Collected Papers

Cognitive Computing' has initiated a new era in computer science. Cognitive computers are not rigidly programmed computers anymore, but they learn from their interactions with humans, from the environment and from information. They are thus able to perform amazing tasks on their own, such as driving a car in dense traffic, piloting an aircraft in difficult conditions, taking complex financial investment decisions, analysing medical-imaging data, and assist medical doctors in diagnosis and therapy. Cognitive computing is based on artificial intelligence, image processing, pattern recognition, robotics, adaptive software, networks and other modern computer science areas, but also includes sensors and actuators to interact with the physical world. Cognitive computers – also called 'intelligent machines' – are emulating the human cognitive, mental and intellectual capabilities. They aim to do for human mental power (the ability to use our brain in understanding and influencing our physical and information environment) what the steam engine and combustion motor did for muscle power. We can expect a massive impact of cognitive computing on life and work. Many modern complex infrastructures, such as the electricity distribution grid, railway networks, the road traffic structure, information analysis (big data), the health care system, and many more will rely on intelligent decisions taken by cognitive computers. A drawback of cognitive computers will be a shift in employment opportunities: A raising number of tasks will be taken over by intelligent machines, thus erasing entire job categories (such as cashiers, mail clerks, call and customer assistance centres, taxi and bus drivers, pilots, grid operators, air traffic controllers, …). A possibly dangerous risk of cognitive computing is the threat by “super intelligent machines” to mankind. As soon as they are sufficiently intelligent, deeply networked and have access to the physical world they may endanger many areas of human supremacy, even possibly eliminate humans. Cognitive computing technology is based on new software architectures – the “cognitive computing architectures”. Cognitive architectures enable the development of systems that exhibit intelligent behaviour.:Introduction 5 1. Applying the Subsumption Architecture to the Genesis Story Understanding System – A Notion and Nexus of Cognition Hypotheses (Felix Mai) 9 2. Benefits and Drawbacks of Hardware Architectures Developed Specifically for Cognitive Computing (Philipp Schröppe)l 19 3. Language Workbench Technology For Cognitive Systems (Tobias Nett) 29 4. Networked Brain-based Architectures for more Efficient Learning (Tyler Butler) 41 5. Developing Better Pharmaceuticals – Using the Virtual Physiological Human (Ben Blau) 51 6. Management of existential Risks of Applications leveraged through Cognitive Computing (Robert Richter) 6