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We further establish a number of pitfalls that can arise from naive nesting of MC estimators, provide guidelines about how these can be avoided, and lay out novel methods for reformulating certain classes of nested expectation problems into single expectations, leading to improved convergence rates. We demonstrate the applicability of our work by using our results to develop a new estimator for discrete Bayesian experimental design problems and derive error bounds for a class of variational objectives.
Our approach relies on the efficiency of sequential Monte Carlo SMC for performing inference in structured probabilistic models and the flexibility of deep neural networks to model complex conditional probability distributions.
We develop additional theoretical insights and experiment with a new training procedure which can improve both model and proposal learning. We demonstrate that our approach provides a fast, easy-to-implement and scalable means for simultaneous model learning and proposal adaptation in deep generative models.
Abstract BibTeX arXiv This paper is an attempt to bridge the conceptual gaps between researchers working on the two widely used approaches based on positive definite kernels: Bayesian learning or inference using Gaussian processes on the one side, and frequentist kernel methods based on reproducing kernel Hilbert spaces on the other.
It is widely known in machine learning that these two formalisms are closely related; for instance, the estimator of kernel ridge regression is identical to the posterior mean of Gaussian process regression. However, they have been studied and developed almost independently by two essentially separate communities, and this makes it difficult to seamlessly transfer results between them.
Our aim is to overcome this potential difficulty. To this end, we review several old and new results and concepts from either side, and juxtapose algorithmic quantities from each framework to highlight close similarities. We also provide discussions on subtle philosophical and theoretical differences between the two approaches. Kernels allow data to be mapped into high-dimensional feature spaces and can thus extend simple linear additive methods to nonlinear methods with higher order interactions.
However, until recently, there has been a strong reliance on a limited class of stationary kernels such as the Matern or squared exponential, limiting the expressiveness of these modelling approaches. Recent machine learning research has focused on spectral representations to model arbitrary stationary kernels and introduced more general representations that include classes of nonstationary kernels.
In this paper, we exploit the connections between Fourier feature representations, Gaussian processes and neural networks to generalise previous approaches and develop a simple and efficient framework to learn arbitrarily complex nonstationary kernel functions directly from the data, while taking care to avoid overfitting using state-of-the-art methods from deep learning.
We highlight the very broad array of kernel classes that could be created within this framework. We apply this to a time series dataset and a remote sensing problem involving land surface temperature in Eastern Africa. We show that without increasing the computational or storage complexity, nonstationary kernels can be used to improve generalisation performance and provide more interpretable results.
Abstract BibTeX arXiv Project: bigbayes Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level.
Current approaches, however, do not propagate the uncertainty in observations due to sampling variability in the groups. This effectively assumes that small and large groups are estimated equally well, and should have equal weight in the final regression. We account for this uncertainty with a Bayesian distribution regression formalism, improving the robustness and performance of the model when group sizes vary.
We frame our models in a neural network style, allowing for simple MAP inference using backpropagation to learn the parameters, as well as MCMC-based inference which can fully propagate uncertainty. We demonstrate our approach on illustrative toy datasets, as well as on a challenging problem of predicting age from images.
For neural latent variable models, optimizing the ELBO jointly in the variational posterior and model parameters produces state-of-the-art results. We show that the tightness of such bounds is asymptotically related to the variance of the underlying estimator.
We introduce a special case, the filtering variational objectives FIVOs , which takes the same arguments as the ELBO and passes them through a particle filter to form a tighter bound. FIVOs can be optimized tractably with stochastic gradients, and are particularly suited to MLE in sequential latent variable models.
In standard sequential generative modeling tasks we present uniform improvements over models trained with ELBO, including some whole nat-per-timestep improvements.
Firstly, we propose stochastic natural gradient expectation propagation SNEP , a novel alternative to expectation propagation EP , a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions.
Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a dataset is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Monte Carlo sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster.
This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks. As datasets in applications grow larger and larger, scalability has emerged as a central problem for MCMC methods. While these methods are asymptotically unbiased if the stepsizes are reduced in an appropriate fashion, in practice constant stepsizes are used.
This introduces a bias that is often ignored. In this paper we study the mean squared error of Lipschitz functionals in strongly log- concave models with i. Using a control variate approach, the cost can be reduced dramatically. The analysis is performed by considering the algorithms as noisy discretisations of the Langevin SDE which correspond to the Euler method if the full data set is used.
An important observation is that the 1scale of the step size is determined by the stability criterion if the accuracy is required for consistent credible intervals. Experimental results confirm our theoretical findings.
Although NMD-induced loss-of-function has been shown to contribute to the genesis of particular cancers, its global functional consequence in tumours has not been characterized. NMD-elicit mutations in tumour suppressor genes TSGs are associated with significant reduction in gene expression. Our analysis reveals a previously unrecognized dependence of hypermutated tumours on hypofunction of genes that are involved in chromatin remodelling and translation.
Our results unravel strong therapeutic opportunities by targeting tumour dependencies on NMD-elicit mutations. This paper contributes to the methodology of Bayesian nonparametric inference for Dn l. Specifically, under the general framework of Gibbs-type priors we show how to derive credible intervals for a Bayesian nonparametric estimation of Dn l , and we investigate the large n asymptotic behaviour of such an estimator.
Of particular interest are special cases of our results obtained under the specification of the two parameter Poisson—Dirichlet prior and the normalized generalized Gamma prior, which are two of the most commonly used Gibbs-type priors.
With respect to these two prior specifications, the proposed results are illustrated through a simulation study and a benchmark Expressed Sequence Tags dataset. To the best our knowledge, this illustration provides the first comparative study between the two parameter Poisson—Dirichlet prior and the normalized generalized Gamma prior in the context of Bayesian nonparemetric inference for Dn l.
However, HMC is sensitive to large time discretizations and performs poorly if there is a mismatch between the spatial geometry of the target distribution and the scales of the momentum distribution. In particular the mass matrix of HMC is hard to tune well. In order to alleviate these problems we propose relativistic Hamiltonian Monte Carlo, a version of HMC based on relativistic dynamics that introduce a maximum velocity on particles.
We also derive stochastic gradient versions of the algorithm and show that the resulting algorithms bear interesting relationships to gradient clipping, RMSprop, Adagrad and Adam, popular optimisation methods in deep learning. Based on this, we develop relativistic stochastic gradient descent by taking the zero-temperature limit of relativistic stochastic gradient Hamiltonian Monte Carlo.
In experiments we show that the relativistic algorithms perform better than classical Newtonian variants and Adam. Caron and E. Fox, "Sparse graphs using exchangeable random measures. The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution. After refactoring, the gradients of the loss propagated by the chain rule through the graph are low variance unbiased estimators of the gradients of the expected loss.
While many continuous random variables have such reparameterizations, discrete random variables lack continuous reparameterizations due to the discontinuous nature of discrete states. In this work we introduce concrete random variables — continuous relaxations of discrete random variables.
The concrete distribution is a new family of distributions with closed form densities and a simple reparameterization. Whenever a discrete stochastic node of a computation graph can be refactored into a one-hot bit representation that is treated continuously, concrete stochastic nodes can be used with automatic differentiation to produce low-variance biased gradients of objectives including objectives that depend on the log-probability of latent stochastic nodes on the corresponding discrete graph.
We demonstrate effectiveness of concrete relaxations on density estimation and structured prediction tasks using neural networks. Moreover, we show that the BEP permits simplified inference. The utility of this prior is demonstrated on synthetic and real world data. Abstract BibTeX PDF Imagine a world where computational simulations can be inverted as easily as running them forwards, where data can be used to refine models automatically, and where the only expertise one needs to carry out powerful statistical analysis is a basic proficiency in scientific coding.
Creating such a world is the ambitious long-term aim of probabilistic programming. Probabilistic programming systems PPSs help alleviate this bottleneck by providing an expressive and accessible modeling framework, then automating the required computation to draw inferences from the model, for example finding the model parameters likely to give rise to a certain output. By decoupling model specification and inference, PPSs streamline the process of developing and drawing inferences from new models, while opening up powerful statistical methods to non-experts.
Many systems further provide the flexibility to write new and exciting models which would be hard, or even impossible, to convey using conventional statistical frameworks. In particular, we will make advancements to the underlying inference engines and increase the range of problems which can be tackled.
For example, we will extend PPSs to a mixed inference-optimization framework, thereby providing automation of tasks such as model learning and engineering design.
Meanwhile, we make inroads into constructing systems for automating adaptive sequential design problems, providing potential applications across the sciences. Furthermore, the contributions of the work reach far beyond probabilistic programming, as achieving our goal will require us to make advancements in a number of related fields such as particle Markov chain Monte Carlo methods, Bayesian optimization, and Monte Carlo fundamentals.
We present a state-of-the-art approach and software toolbox allowing such DARC experiments to be run in a highly efficient way. We demonstrate that this leads to improvements over alternative experimental paradigms. The key to releasing our real time and automatic performance is a number of advances over current Bayesian adaptive design methodology. In particular, we derive an improved estimator for discrete output problems and design a novel algorithm for automating sequential adaptive design.
We provide a number of pre-prepared DARC tools for researchers to use, but a key contribution is an adaptive experiment toolbox that can be extended to virtually any 2-alternative-choice tasks.
In particular, to carry out custom adaptive experiments using our toolbox, the user need only encode their behavioural model and design space — both the subsequent inference and sequential design optimisation are automated for arbitrary models the user might write. Abstract BibTeX URL Project: bigbayes We consider the problem of sampling a sequence from a discrete random probability measure RPM with countable support, under probabilistic constraints of finite memory and computation.
A canonical example is sampling from the Dirichlet Process, which can be accomplished using its stick-breaking representation and lazy initialization of its atoms. We show that efficiently lazy initialization is possible if and only if a size-biased representation of the discrete RPM is used. For models constructed from such discrete RPMs, we consider the implications for generic particle-based inference methods in probabilistic programming systems.
Abstract BibTeX URL Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns.
We introduce the OrMachine, a probabilistic generative model for Boolean matrix factorisation and derive a Metropolised Gibbs sampler that facilitates efficient parallel posterior inference. On real world and simulated data, our method outperforms all currently existing approaches for Boolean matrix factorisation and completion.
This is the first method to provide full posterior inference for Boolean Matrix factorisation which is relevant in applications, e. The proposed algorithm scales to large datasets as we demonstrate by analysing single cell gene expression data in 1.
Abstract BibTeX URL Project: bigbayes Despite the fundamental nature of the inhomogeneous Pois- son process in the theory and application of stochastic processes, and its attractive generalizations e.
Cox process , few tractable nonparametric modeling approaches of intensity functions exist, especially when observed points lie in a high-dimensional space. We model the square root of the intensity as an RKHS function. Whereas RKHS models used in su- pervised learning rely on the so-called representer theorem, the form of the inhomogeneous Poisson process likelihood means that the representer theorem does not apply.
However, we prove that the representer theorem does hold in an appropriately transformed RKHS, guaranteeing that the optimization of the penalized likelihood can be cast as a tractable finite- dimensional problem. The resulting approach is simple to implement, and readily scales to high dimensions and large-scale datasets. Abstract BibTeX URL Software Project: bigbayes The algorithms for causal discovery and more broadly for learning the structure of graphical models require well calibrated and consistent conditional independence CI tests.
We revisit the CI tests which are based on two-step procedures and involve regression with subsequent unconditional independence test RESIT on regression residuals and investigate the assumptions under which these tests operate. In particular, we demonstrate that when going beyond simple functional relationships with additive noise, such tests can lead to an inflated number of false discoveries. When used as a component of the PC algorithm, the proposed test is more robust to the case where hidden variables induce a switching behaviour in the associations present in the data.
Abstract BibTeX URL Project: bigbayes While deep neural networks have achieved state-of-the-art performance on many tasks across varied domains, they still remain black boxes whose inner workings are hard to interpret and understand.
In this paper, we develop a novel method for efficiently capturing the behaviour of deep neural networks using kernels. In particular, we construct a hierarchy of increasingly complex kernels that encode individual hidden layers of the network. Furthermore, we discuss how our framework motivates a novel supervised weight initialization method that discovers highly discriminative features already at initialization.
However, it is rarely that all possible differences between samples are of interest — discovered differences can be due to different types of measurement noise, data collection artefacts or other irrelevant sources of variability. We propose distances between distributions which encode invariance to additive symmetric noise, aimed at testing whether the assumed true underlying processes differ.
Moreover, we construct invariant features of distributions, leading to learning algorithms robust to the impairment of the input distributions with symmetric additive noise.
Such features lend themselves to a straightforward neural network implementation and can thus also be learned given a supervised signal. Abstract BibTeX arXiv Software Detecting causal associations in time series datasets is a key challenge for novel insights into complex dynamical systems such as the Earth system or the human brain. Interactions in high-dimensional dynamical systems often involve time-delays, nonlinearity, and strong autocorrelations.
These present major challenges for causal discovery techniques such as Granger causality leading to low detection power, biases, and unreliable hypothesis tests. Here we introduce a reliable and fast method that outperforms current approaches in detection power and scales up to high-dimensional datasets. It overcomes detection biases, especially when strong autocorrelations are present, and allows ranking associations in large-scale analyses by their causal strength.
We provide mathematical proofs, evaluate our method in extensive numerical experiments, and illustrate its capabilities in a large-scale analysis of the global surface-pressure system where we unravel spurious associations and find several potentially causal links that are difficult to detect with standard methods.
The broadly applicable method promises to discover novel causal insights also in many other fields of science. Abstract BibTeX arXiv Bayesian posterior inference with Monte Carlo methods has a fundamental role in statistics and probabilistic machine learning. Target posterior distributions arising in increasingly complex models often exhibit high degrees of nonlinearity and multimodality and pose substantial challenges to traditional samplers.
KSMC is applicable when gradients are unknown or prohibitively expensive and inherits the superior performance of SMC on multi-modal targets and its ability to estimate model evidence. Strengths of the proposed methodology are demonstrated on a series of challenging synthetic and real-world examples.
Cox process , few tractable nonparametric modeling approaches of intensity functions exist, especially in high dimensional settings. The modeling challenge is that the usual representer theorem arguments no longer apply due to the form of the inhomogeneous Poisson process likelihood.
However, we prove that the representer theorem does hold in an appropriately transformed RKHS, guaranteeing that the optimization of the penalized likelihood can be cast as a tractable finite-dimensional problem. Abstract BibTeX arXiv PDF Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks.
In practice, however, this is not usually observed, because gradients from different tasks can interfere negatively, making learning unstable and sometimes even less data efficient. Another issue is the different reward schemes between tasks, which can easily lead to one task dominating the learning of a shared model. Instead of sharing parameters between the different workers, we propose to share a "distilled" policy that captures common behaviour across tasks. Each worker is trained to solve its own task while constrained to stay close to the shared policy, while the shared policy is trained by distillation to be the centroid of all task policies.
Both aspects of the learning process are derived by optimizing a joint objective function. We show that our approach supports efficient transfer on complex 3D environments, outperforming several related methods. Moreover, the proposed learning process is more robust and more stable—attributes that are critical in deep reinforcement learning.
Yet, in some cases agents may wish to emphasize the low or high returns regardless of their probability. Borrowing from the economics and control literature, we review the risk-sensitive value function that arises from an exponential utility and illustrate its effects on an example.
We illustrate the benefit of the policy gradients of this objective in Cliffworld. This is a large class of discrete RPMs which encompasses most of the popular discrete RPMs used in Bayesian nonparametrics, such as the Dirichlet process, Pitman-Yor process, the normalized inverse Gaussian process and the normalized generalized Gamma process.
Specifically, we introduce a novel and efficient MCMC sampling scheme in an augmented space that has a fixed number of auxiliary variables per iteration. We apply our sampling scheme to a density estimation and clustering tasks with unidimensional and multidimensional datasets, and compare it against competing MCMC sampling schemes.
Abstract BibTeX PDF URL Maps of infectious disease—charting spatial variations in the force of infection, degree of endemicity, and the burden on human health—provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is that of Gaussian process regression, a mathematical framework comprised of two components: a mean function harnessing the predictive power of multiple independent variables, and a covariance function yielding spatio-temporal shrinkage against residual variation from the mean.
Though many techniques have been developed to improve the flexibility and fitting of the covariance function, models for the mean function have typically been restricted to simple linear terms. For infectious diseases, known to be driven by complex interactions between environmental and socio-economic factors, improved modelling of the mean function can greatly boost predictive power.
Here we present an ensemble approach based on stacked generalisation that allows for multiple, non-linear algorithmic mean functions to be jointly embedded within the Gaussian process framework. We propose a conceptual framework to separate the rhetoric of the patient-manager from the notion of patient empowerment.
During the past decade, technology developers, policy makers, researchers, and doctors have increasingly approached patients in terms of managers of their health. This conceptual shift is linked to the rise of smart technology. Eric Topol, a practicing cardiologist and professor of genomics, writes extensively about patient empowerment through digital technology.
He expects smartphones to lead to a more democratic version of medicine, that allows patients to organize and control their own healthcare Topol, We take these observations as our starting point. Databased artificial intelligence AI offers a plethora of opportunities to monitor patients at home, such as intelligent video monitoring that can also include wearable cameras.
Smart technologies and self-management go hand in hand. But why has the patient increasingly become a manager over the last decade? The discourse of the patient-manager seems first and foremost driven by cost-efficiency. Many academic and policy papers refer to the aging population and increase in chronic diseases to warn about our healthcare system becoming unaffordable see e.
Smart technology plays a crucial role here: it should assist patients in their health management, in order to reach a higher degree of autonomy. But does this concept entail autonomy or empowerment in an ethical meaning? In our opinion, the definition of the patient as manager focuses on three ideals or goals related to empowerment that deserve a critical analysis. First, there is a focus on choice: a patient should first and foremost be able to choose, much like consumers can choose which products they buy.
The emphasis on choice risks creating a burden on patients by transferring responsibility to a great extent to them. As a patient, you become responsible for your choices. Second, there is an emphasis on self- management of health and disease. Although this may be seen as freedom, it can be challenging for patients to take up such responsibility.
Again, this implies that patients are responsible for their decisions and outcomes. This focus on self- management also relates to cost efficiency. The ultimate goal of self-management in the rhetoric of the patient as manager is the reduction of health services utilization through self-management interventions, without compromising health outcomes in patients Panagioti et al.
However, self-management often includes interventions that cost money: patient education, support for decision-making, and psychological and social support, amongst others. But how autonomous and empowered is the patient-manager in the end? The emphasis on choice, self-management, and cost efficiency raises compelling concerns about autonomy and empowerment. And to what extent do we risk a new form of paternalism, namely technological paternalism, when smart technology is a prerequisite for the patient as health manager?
When we speak of patient empowerment, paternalism is to be avoided and patients have to be able to make autonomous choices. Also, many self-tracking applications have a persuasive design. Mort et al.
Journal of Medical Internet Research 17 8. Anderson, R. Patient Empowerment: Myths and Misconceptions. Patient Education and Counseling 79 3 , pp. Aujoulat, I. Patient Education and Counseling 66 1 , pp. Beauchamp, T. Principles of Biomedical Ethics. Seventh Edition. Oxford: Oxford University Press. Gurdus, L.
CNBC, 8 January Science and Engineering Ethics 9 3 , pp. Mather, M. Aging in the United States. Population Bulletin 70 2. Mort, M. Ageing with Telecare: Care or Coercion in Austerity?
Panagioti, M. Topol, E. New York: Basic Books. In an economy, the PM is the basic tool for interchanging or acquiring goods and services by transferring monetary assets. There are two kinds of PM: Banknotes and Coins. The process for monetary assets transferring is simple, first the available balance of the individual and second, the mechanism to make the financial transaction hard cash. However, the mechanisms for paying methods have been evolved, such as debit or credit cards, cashier or personal checks, electronic transfer, mobile apps, etc.
For the proper functioning of payment systems, it is essential to have reliable, efficient, and secure financial instruments. These instruments must provide certainty to economic agents that their financial transactions will be completely secure. The trend in this context has been the adoption of a complicated telecommunications infrastructure and specialized software.
In this sense, a problem can be observed on the one hand, developing and emerging countries have difficulties in their implementation, on the other hand, the vague or null financial education of these countries generates a lot of mistrust among economic agents.
According to the report "Financial Inclusion Panorama ", the cash transactions in Mexico have increased during the period from December to December The report also shows that the balance of banknotes and coins has experienced an average annual growth rate of 7.
The paragraph above shows the continuing popularity of using cash to transact. When a minor transaction is made in cash the resources can be used immediately. However, when a transaction is of a larger amount, the use of cash represents certain disadvantages, such as an increase in the probability of illegal attacks. There is another implicit cost of using cash that can have negative effects on economic activity. This cost refers to the loss or deceleration of transactions, on the one hand, due to the lack of cash in the establishments and on the other hand the non-acceptance of these payment methods by them.
In recent times, two situations have intensified the use of digital financial services in the world: technological advances, supported by generational gaps and the pandemic situation caused by covid- This is some way to force banking institutions to exploit the internal market through transfer systems and internet-based payment methods.
The most used are credit and debit cards mostly to make transactions online. In turn, it points out that the main barriers for non-users are lack of liquidity, as well as the perception of not needing any service and lack of trust towards financial institutions. Users of financial services want to get from their financial service lower commissions, complete security, and the fulfilment of promises.
In the academic literature, some factors that explain technology acceptance have been evaluated across time. Most of the studies of acceptance consider some technological applied constructs, such as performance and effort expectancy, Job relevance, output quality, etc.
This research focuses on the social effects of PM acceptance. Also, this research attempts to find a relation between the factors that measure the social influence of PM acceptance and the disruption on individuals towards using banking technologies to hold transactions.
Social factors in many times can prevent acceptance, this may be due to individual beliefs, lack of information, perceive of risk, among others. In the case of PM considered as a bank service the acceptance must be carried out through the risk context effects in which individuals attempt to use the financial technology and not as a business service or product.
Besides, this research considers as a part of the social determinants of adoption, the degree of the individual ethical perceive. These concerns point to the possibility that malicious people carry out fraud, identity theft, civil liberties, among others.
In Mexico, the Federal Official Gazette provides an ethical code for eCommerce and therefore banking transactions. In the disruption context, the acceptance of banking technologies is transforming the means of making transactions. As is cited in previous paragraphs, Mexico has a lack in the adoption of POS and consumer are mainly familiarized with cash as a method of payment. We can assume that technologies could be a real inconvenience for the strategic positioning of banking institutions due to the increase in people's mistrust of them.
In this sense, it would be relevant to identify the acceptance factors that influence the end-users of these technologies. As a contribution in the theoretical field, a path model will be developed based on statistical methods such as Ordered Probit and Structural Equation Model. Expected outcomes will have important implications in the managerial and economic context. Massachusetts Institute of Technology.
Dowling, G. A novel approach for plug-in electric vehicle planning and electricity load management in presence of a clean disruptive technology. Energy, , Payment transactions, instruments, and systems: A survey. Jacoby, J. The components of perceived risk. Venkatesan Ed. Minsait Payment X Informe Tendencias en Medios de pago. Obaidat, I. Woungang Eds.
Cham: Springer International Publishing. MIS Quarterly, 27 3 , The use of disruptive information technologies for competitive advantage in the banking sector of South Africa.
Such pictures of the future may be stabilized by technology and product roadmaps and the widespread adoption of the corresponding offerings by the market.
Not only the Driverless City, advocated by vendors of semi autonomous cars, but the Smart City is an example of this approach, too.
The focus of all smart systems currently under development is on process efficiency and economic considerations. Many of such imaginaries are techno-deterministic, abstracting from human needs or just taking a certain perspective as the only one possible.
The ethical challenge in these cases is to integrate relevant stakeholders in the process of defining sociotechnical imaginaries. In a pluralistic society it would be appropriate, if the relevant stakeholders, or at least their representatives could participate in the design of technical imaginaries.
This is even more important in times like ours where the concept of an environment worth living in is undergoing fundamental changes due to the pandemic. Ludic sociotechnical imaginaries constitute one option to explore alternative futures.
Serious games could provide a viable alternative to passing ideas around in an informal setting and to brainstorming at the water cooler by experimenting with technical visions. The focus would be less on nudging urbanites to accept the current visions of Driverless or even Smart Cities and the like but on experimenting with new ideas already on the radar, utopias and dystopias.
Current visions of the Smart City primarily focus on the distribution of scarce resources and on enhancing efficiency. Alternative visions could integrate local and regional values. Social anticipation and imagination may give some indication what cities will look like in the future depending on the core values of different societies and their economic possibilities.
The MIT task force on the work of the future warns that there will be too few low-wage jobs Thus, it is in our self-interest as societies to be a smart society and plan for the unknown that lies ahead. Another example could be visions of a future, where we trust technology more than we trust each other.
It is characterized by trust-less interactions between humans who have interdependent interests respectively share goals. Their unique way to deal with uncertainties regarding each other is the binding of the will by code and to the corresponding technology platforms. The most prominent example is the blockchain technology. Non-trusting blockchain users can interact with each other without a trusted human intermediary.
Blockchain serves as the basis to digital currencies as Bitcoin, to a wide variety of distributed ledgers, where cryptographic tokens represent ownership of material goods as diamonds or land smart property , and to self-executing smart contracts. Executing a smart contract is decoupled from concluding such a contract between humans or technical agents.
These contracts may not only be signed by humans but also by software agents. Thus the need for human intermediaries e. This variant of FinTech and Legal Tech accelerates all kinds of digital business flows. In the future decentralized autonomous organizations DAO may be launched based on smart contracts and code.
Their autonomy results from the fact that after the instantiation they no longer need their creators. Moreover, they are self-sufficient in so far that they can accumulate capital both digital currencies and physical assets. Their business model consists in charging for digital services provided. As long as they have sufficient funds they can operate in an independent way. An ill- intentioned DAO may cause irreparable havoc. Thus, it is easily understandable why the legal consequences of smart contract, where and under what circumstances they have a legally binding effects, as well as approaches to distributed governance for DAOs are currently hotly discussed subjects.
Instead of relying on trust between humans the blockchain based distributed databases intend to provide trustworthy, i. Instead of relying on human institutions to enforce contracts contractual partners may rely on blockchain based platforms for self-executing smart contracts. Instead of trusting central banks users of digital currencies trust free market mechanisms supported by blockchain technology. The adoption of these technologies does not result in a trust-free world but in one where we trust technology more than each other.
Distrust between human interaction partners in these collaborative networks is the default. In these in environments trust is put solely in the blockchain-based platforms.
If these forms of collaborative networks were to prevail, trust-less societies would be the result. In these collaborative networks individual freedom might be maximized possibly to the detriment of others.
Competitive or cooperative games where some of the partly virtual players are decentralized autonomous organizations DAO could alert us on how a future of trust-less humans could look like. Novel sociotechnical imaginaries could be created where speculative futures testing actual technological developments are created in a unique form of gamification and interaction. The process of creating attractive sociotechnical imaginaries does not end in games and role playing, but in the validation of the created imaginaries by their potential users.
Thus, anticipation and speculation in a Smart Society, an alert one to the uncertainties of future developments, could profit from ludic endeavors. Such a new way of engaging with technology could foster the creativity of the users and open up new perspectives, a novel way of futures thinking.
Awad, E. The Moral Machine experiment. Jasanoff, S. Nordmann, A. Siemens Wagner, A. This work uses a machine learning approach to study the emotions of a large group of social media users on Twitter during the Covid19 pandemic and compare the results to our previous research that evaluated 10 million tweets from users between - It is possible to extract emotions of social media users from the text of their status updates as shown by Colneric and Demsar, and Tasoulis et al.
This analysis is based on the work of Colneric and Demsar, who were kind enough to publish the resulting machine learning model. They utilized neural networks to generate a model that is able to detect emotions in English language. Neural networks are a supervised machine learning method and therefore the data needs annotations for the algorithm to learn from.
As the authors learned on a massive dataset of 73 billion tweets it was infeasible to manually annotate the dataset. The authors exploited hashtags as annotations, an approach that was successfully used in several other natural language processing studies for sentiment classification Go et al. As hashtags are selected by the author of a tweet they work well as indicators of their emotions.
The classification for this analysis is done with Ekmans scheme of basic emotions as it covers fear, disgust and anger, which have been identified through comprehensive literature research as the most impactful emotions caused by the use of social media and should be investigated further. An example classification that contains only the relevant columns is shown below in Table 1. The status update regards a sport event where an athlete plays his first game for the team and the detected prevalent emotion is joy, followed by surprise.
In our previous work users have been grouped based on the number of status updates they publish and the amount of followers they have. Grouping users was more effective and showed more distinct results when based on the amount of followers a user has and not based on the number of status updates they publish. The prevalent expressed emotions on twitter were happiness and surprise.
Over the observed period of time, from to , the values for happiness remained consistent, while an increase in anger, disgust and fear could be verified for all user groups. Thus, for this work the collected tweets are classified for the expressed emotions and grouped based on the amount of followers an individual user has to allow the comparison with historic data. Contextualized sarcasm detection on twitter.
Colneric, N. Basic emotions. In Handbook of Cognition and Emotion. Go, A. Twitter Sentiment Classification using Distant Supervision. The effects of active social media engagement with peers on body image in young women. Body Image, 28, Journal of Social and Clinical Psychology, 37 10 , Levenson, J. The association between social media use and sleep disturbance among young adults. Preventive Medicine, 85, Educational and Industrial Testing Service, 3 27 , Mohammad, S.
Computational Intelligence, 31 2 , A psychoevolutionary theory of emotions. Social Science Information, 21 4 , Tasoulis, S. Currently, such technologies are extremely useful in assisting people with disabilities. Among them, the development and social utilisation of a brain-machine interface BMI , or a brain-computer interface BCI , has shown remarkable progress over recent years, and its utilisation by people with disabilities has steadily been trialled and adopted in developed countries.
A BMI promotes communication through signalling from the brain to external devices and vice versa using dedicated hardware and software Orito et al. Using these functions, BMI has been adopted to support people with disabilities, such as amyotrophic lateral sclerosis ALS , muscular dystrophy and cerebral palsy.
For example, a BMI device enables paralysed individuals, who are unable to move their bodies at will, to successfully control digital devices and to communicate with others. However, discussion regarding the utility and ethical issues surrounding the use of BMI systems in social welfare have not been extensive, because BMI devices or systems are not currently widely used by people with disabilities. In addition, it seems that healthy people tend to consider the simple- minded arguments or benefits of cyborg technologies that can be used for supporting people with disabilities and to miss the disadvantages and risks to them that may take place through the use of such technologies.
Of course, there are many beneficial ways in which BMI systems can assist people with disabilities; however, it is not desirable to overestimate these beneficial aspects. To this end, BMI usability for people with disabilities, the appropriateness of purposes and targeted subjects, risks and ethical issues entailed in the operation of the systems should be proactively examined from various viewpoints.
With this in mind, the authors conducted experiments on two healthy social welfare professionals as subjects through fitting them with a non-invasive wearable BMI device and interview survey to investigate the availability, usability and ethical issues of BMI systems for people with disabilities.
This research is positioned in the exploratory phase. In such process, it is meaningful to ask the views and opinions of professionals or experts in related areas, and in this study, two professionals with specialised knowledge and experiences in social welfare were invited.
In the experiments, the subjects were asked to put the non-invasive BMI device EEG input device on their head to operate a robotic arm remotely or without touching it, as if using psychokinesis, as in our previous study Orito et al. The experiments and interviews were conducted in August at Ehime University in Matsuyama, Ehime prefecture, Japan. All procedures performed for this study were in accordance with the ethical standards of the research ethics committee at the Faculty of Medicine, Ehime University.
The attributes of the subjects are shown in Table 1. They were healthy adult professionals; one was a researcher who majored in social security law and social welfare, the second was a social worker. The two subjects were knowledgeable regarding BMI technologies, on the basis of their professional experiences. Weak 0—Strong 7 Yes. I heard about this type of technology in our communities, several Social years ago.
The interview questions were categorised as follows: a privacy and personal data protection, b human autonomy and dignity, c identity development and personal transformation, d the acceptance of body extension in an individual and organisational context, e workplace cyborgisation and f social responsibility and informed consent. In addition to these, questions related to the BMI experiment and support for people with disabilities were added in this study.
Through the experiments, subsequent interviews and follow-up surveys via by e-mail after the experiment regarding BMI devices and their usage, the authors gained insightful findings which are categorised below. Agendas towards experimental and practical use of BMI for people with disabilities A. Appropriate subjects with disabilities and their cases as well as specific movement during the experiment C.
Cautions and important notice for experimental and interview surveys for subjects people with disabilities 2. Possibilities and value of BMI usage for people with disabilities D. Palliative care E. Assistance of support for physical movements aimed to maintain their human dignity F.
Expectations concerning those who wish to use implantable BMI and its usability 3. Risks and ethical concerns regarding the BMI system for people with disabilities G.
Difficulties to maintain equal opportunities; widening gap between the haves and have-nots H. Negative effect of mismanaged body movement and mental status associated with being taken over by a machine I. Mental exhaustion arising from using online tools and the workplace issues J. Privacy, personal data protection 4.
Physical synchronicity Interview results allowed the authors to recognise which categories of people with disabilities the BMI system was effective for, what type of support or communication is required, what kinds of ethical concerns relating to BMI system use are important for them. Ichikawa, ; Yasuda, ; Yuasa, ; Yuasa, We suggest that these discussions will provide insightful implications for the use of cyborg technologies for people with disabilities as well as healthy people.
We also appreciate Dr. Yoshitaka Moritsugu, and all the participants in the experiments and researchers who support our study. It was certified that all procedures performed in the experiments of this study, which involved human participants, were in accordance with the ethical standards of the research ethics committee set up at the Faculty of Medicine, Ehime University issued Jan.
Tokyo: Kodansha in Japanese. Isobe, T. Journal of Information Studies, 84, in Japanese. Murata, K. From a science fiction to reality: Cyborg ethics in Japan. Computers and Society, 47 3 , Cyborg athletes or technodoping: How far can people become cyborgs to play sports? Cross-cultural study about cyborg market acceptance: Japan versus Spain. European Research on Management and Business Economics, 25 3 , Orito, Y. In Arias-Oliva, M. Possibilities and ethical issues surrounding brain-machine interfaces in the realm of social welfare: Potential for use by people with disabilities based on results from psychokinesis experiments.
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