Connectivism: Adopting Quantum Holism in International Relations

The current scientific context of both quantum science and an ever-increasingly connected global citizenry has set the conditions for a new perspective whereby the social sciences are on the cusp of adopting a quantum approach of probability and potentiality versus the clockwork mechanistic determinism of cause-and-effect Newtonian mechanics. While a scientific realist approach toward the application of quantum science to the social sciences is germane, there is a valid reason international relations should also consider and adopt the philosophical worldviews outside the genealogical canon of our early western forbears, as well as the philosophical explorations of consciousness and humanism which have evolved over the years. Marrying the quantum physics of consciousness and reality with the philosophy of phenomenology and humanism will lead toward a deeper, more holistic understanding of our connection to each other as human beings, and our connection to the world of our creation through this conscious experience of each other and our surroundings. This unifying reorientation away from classical science toward a more holistic quantum application of science and philosophy is what I term Connectivism. Rather than privileging a Hobbesian view of nature as a war of all against all Connectivism will privilege the unifying principles which connect us all to each other. This relational social ontology will highlight the more cooperative and interconnected aspects of the human experience versus the Newtonian dynamics which separates humans from their environments and turns them into simply another material variable upon which external forces exert their impact on the human dimension. A quantum holist ontology, on the other hand, will destroy the dichotomy between agents and structures, individuals and societal collectivities. This unified ‘whole’ which is instantiated through conscious individual, interrelational, and interactional processes of potentiality (i.e., wave functions) and realization (wave function collapse, or decoherence) privileges and situates human agency and its creative impact on the environment in a more comprehensive and cooperative way

A Hearty Yes … And!

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A Review of Cavitation Uses and Problems in Medicine; Invited Lecture

There are an increasing number of biological and bioengineering contexts in which cavitation is either utilized to create some desired effect or occurs as a byproduct of some other process. In this review an attempt will be made to describe a cross-section of these cavitation phenomena. In the byproduct category we describe some of the cavitation generated by head injuries and in artifical heart valves. In the utilization category we review the cavitation produced during lithotripsy and phacoemulsification. As an additional example we describe the nucleation suppression phenomena encountered in supersaturated oxygen solution injection. Virtually all of these cavitation and nucleation phenomena are critically dependent on the existence of nucleation sites. In most conventional engineering contexts, the prediction and control of nucleation sites is very uncertain even when dealing with a simple liquid like water. In complex biological fluids, there is a much greater dearth of information. Moreover, all these biological contexts seem to involve transient, unsteady cavitation. Consequently they involve the difficult issue of the statistical coincidence of nucleation sites and transient low pressures. The unsteady, transient nature of the phenomena means that one must be aware of the role of system dynamics in vivo and in vitro. For example, the artificial heart valve problem clearly demonstrates the importance of structural flexibility in determining cavitation occurrence and cavitation damage. Other system issues are very important in the design of in vitro systems for the study of cavitation consequences. Another common feature of these phenomena is that often the cavitation occurs in the form of a cloud of bubbles and thus involves bubble interactions and bubble cloud phenomena. In this review we summarize these issues and some of the other characteristics of biological cavitation phenomena

Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up

A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe

Quantum machine learning: a classical perspective

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde