The universe as quantum computer

This article reviews the history of digital computation, and investigates just how far the concept of computation can be taken. In particular, I address the question of whether the universe itself is in fact a giant computer, and if so, just what kind of computer it is. I will show that the universe can be regarded as a giant quantum computer. The quantum computational model of the universe explains a variety of observed phenomena not encompassed by the ordinary laws of physics. In particular, the model shows that the the quantum computational universe automatically gives rise to a mix of randomness and order, and to both simple and complex systems.Comment: 16 pages, LaTe

Ambient Sound-Based Collaborative Localization of Indeterministic Devices

Localization is essential in wireless sensor networks. To our knowledge, no prior work has utilized low-cost devices for collaborative localization based on only ambient sound, without the support of local infrastructure. The reason may be the fact that most low-cost devices are indeterministic and suffer from uncertain input latencies. This uncertainty makes accurate localization challenging. Therefore, we present a collaborative localization algorithm (Cooperative Localization on Android with ambient Sound Sources (CLASS)) that simultaneously localizes the position of indeterministic devices and ambient sound sources without local infrastructure. The CLASS algorithm deals with the uncertainty by splitting the devices into subsets so that outliers can be removed from the time difference of arrival values and localization results. Since Android is indeterministic, we select Android devices to evaluate our approach. The algorithm is evaluated with an outdoor experiment and achieves a mean Root Mean Square Error (RMSE) of 2.18 m with a standard deviation of 0.22 m. Estimated directions towards the sound sources have a mean RMSE of 17.5 ° and a standard deviation of 2.3 °. These results show that it is feasible to simultaneously achieve a relative positioning of both devices and sound sources with sufficient accuracy, even when using non-deterministic devices and platforms, such as Android

Demystifying Big Data Adoption: Beyond IT Fashion and Relative Advantage

There is a paradox in big data adoption: a peak of hype and simultaneously an unexpectedly low deployment rate. The present multiple case study research develops a Big Data Adoption (Big2) model that helps to explain this paradox and sheds light on the “whether”, “why”, and “how” questions regarding big data adoption. The Big2 model extends beyond the existing Relative Advantage and IT Fashion theories to include organizational, environmental, social variables as well as new psychological factors that are unique to big data adoption. Our analysis reveals that the outcome of big data adoption is indeterministic, which defies the implicit assumption of most simplistic “rational-calculus” models of innovation adoption: Relative Advantage is a necessary but not sufficient condition for big data adoption. Most importantly, our study uncovered a “Deployment Gap” and a “Limbo Stage” where companies continuously experiment for a long time and do not proceed to deployment despite the intent to adopt big data. As a result there are four big data adoption categories: Not adopting, Experimented but Not Adopting, Not Yet Deployed, Deployed. Our Big2 model contributes to provide a Paradigm Shift and Complexity Tolerance perspective to understand the “why” in each of the 4 adoption categories. This study further identifies 9 complexity tolerance strategies to help narrow the Deployment Gap but also shows that big data is not for everyone

Deterministic and indeterministic morality and duality. Quantum and philosophical approach

Quantum mechanics is a fundamental theory in physics that describes the behavior of subatomic particles and systems at very small scales. Unlike classical theories, quantum mechanics introduces elements of indeterminism in the description of physical phenomena. There are fundamental limits to the precision with which certain physical properties, such as the position and momentum of a particle, can be measured simultaneously. This implies that, even if all the initial conditions of a quantum system are known, its future behavior cannot be predicted with absolute certainty. This quantum indeterminacy raises philosophical questions about determinism and free will, as well as their relationship to morality. Determinism holds that all actions and events are predetermined by prior causes, which challenges the traditional notion of moral responsibility based on control and predictability. In contrast, indeterminism posits that there are elements of randomness and variability in the workings of the universe. From a moral and philosophical point of view, the presence of indeterminism in quantum mechanics poses challenges to the attribution of moral responsibility. If our actions are influenced by random events, how can we be morally responsible for them

Emerging Technologies for Hydrologic and Water Quality Modeling Research

During the past two decades there has been a dramatic increase in the development and application of hydrologic and water quality models to evaluate complex environmental processes and to assess nonpoint source pollution of soil and water resources. Recognizing that advancements in modeling continue to be driven by developments in computer technology, it is worthwhile to examine some of the current and emerging computer technologies that hold great promise for advancing the use of hydrologic and water quality models. An attempt is made to forecast and briefly discuss the impact that technologies such as geographic information systems, global positioning systems, and scientific visualization will have on the future of hydrologic and water quality modeling. Forecasting is a very risky business, not because of our chronic inability to predict what will happen in the future but also because such speculation raises questions about what we modelers and model users desire and value. The thesis of this article is that some current and most of the emerging technologies will facilitate development and widespread use of hydrologic and water quality models for water resources management and decision making in the future

Conversational AI Agents: Investigating AI-Specific Characteristics that Induce Anthropomorphism and Trust in Human-AI Interaction

The investment in AI agents has steadily increased over the past few years, yet the adoption of these agents has been uneven. Industry reports show that the majority of people do not trust AI agents with important tasks. While the existing IS theories explain users’ trust in IT artifacts, several new studies have raised doubts about the applicability of current theories in the context of AI agents. At first glance, an AI agent might seem like any other technological artifact. However, a more in-depth assessment exposes some fundamental characteristics that make AI agents different from previous IT artifacts. The aim of this dissertation, therefore, is to identify the AI-specific characteristics and behaviors that hinder and contribute to trust and distrust, thereby shaping users’ behavior in human-AI interaction. Using a custom-developed conversational AI agent, this dissertation extends the human-AI literature by introducing and empirically testing six new constructs, namely, AI indeterminacy, task fulfillment indeterminacy, verbal indeterminacy, AI inheritability, AI trainability, and AI freewill

Order in Spontaneous Behavior

Brains are usually described as input/output systems: they transform sensory input into motor output. However, the motor output of brains (behavior) is notoriously variable, even under identical sensory conditions. The question of whether this behavioral variability merely reflects residual deviations due to extrinsic random noise in such otherwise deterministic systems or an intrinsic, adaptive indeterminacy trait is central for the basic understanding of brain function. Instead of random noise, we find a fractal order (resembling Lévy flights) in the temporal structure of spontaneous flight maneuvers in tethered Drosophila fruit flies. Lévy-like probabilistic behavior patterns are evolutionarily conserved, suggesting a general neural mechanism underlying spontaneous behavior. Drosophila can produce these patterns endogenously, without any external cues. The fly's behavior is controlled by brain circuits which operate as a nonlinear system with unstable dynamics far from equilibrium. These findings suggest that both general models of brain function and autonomous agents ought to include biologically relevant nonlinear, endogenous behavior-initiating mechanisms if they strive to realistically simulate biological brains or out-compete other agents

Freshness and Reactivity Analysis in Globally Asynchronous Locally Time-Triggered Systems

International audienceCritical embedded systems are often designed as a set of real-time tasks, running on shared computing modules, and communicating through networks. Because of their critical nature, such systems have to meet timing properties. To help the designers to prove the correctness of their system, the real-time systems community has developed numerous approaches for analyzing the worst case times either on the processors (e.g. worst case execution time of a task) or on the networks (e.g. worst case traversal time of a message). However, there is a growing need to consider the complete system and to be able to determine end-to-end properties. Such properties apply to a functional chain which describes the behavior of a sequence of functions, not necessarily hosted on a shared module, from an input until the production of an output. This paper explores two end-to-end properties: freshness and reactivity, and presents an analysis method based on Mixed Integer Linear Programming (MILP). This work is supported by the French National Research Agency within the Satrimmap project