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"With each interaction, Alexa is training to hear better, to interpret more precisely, to trigger actions that map to the user's commands more accurately, and to build a more complete model of their preferences, habits and desires. What is required to make this possible? Put simply: each small moment of convenience - be it answering a question, turning on a light, or playing a song - requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data. The scale of resources required is many magnitudes greater than the energy and labor it would take a human to operate a household appliance or flick a switch. A full accounting for these costs is almost impossible, but it is increasingly important that we grasp the scale and scope if we are to understand and govern the technical infrastructures that thread through our lives. III The Salar, the world's largest flat surface, is located in southwest Bolivia at an altitude of 3,656 meters above sea level. It is a high plateau, covered by a few meters of salt crust which are exceptionally rich in lithium, containing 50% to 70% of the world's lithium reserves. 4 The Salar, alongside the neighboring Atacama regions in Chile and Argentina, are major sites for lithium extraction. This soft, silvery metal is currently used to power mobile connected devices, as a crucial material used for the production of lithium-Ion batteries. It is known as 'grey gold.' Smartphone batteries, for example, usually have less than eight grams of this material. 5 Each Tesla car needs approximately seven kilograms of lithium for its battery pack. 6 All these batteries have a limited lifespan, and once consumed they are thrown away as waste. Amazon reminds users that they cannot open up and repair their Echo, because this will void the warranty. The Amazon Echo is wall-powered, and also has a mobile battery base. This also has a limited lifespan and then must be thrown away as waste. According to the Ay

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.

You probably interact with fifty to a hundred machine learning products every day, from your social media feeds and YouTube recommendations to your email spam filter and the updates that the New York Times, CNN, or Fox News decide to push, not to mention the hidden models that place ads on the websites you visit, and that redesign your 'experience' on the fly. Not all models are created equal, however: they operate on different principles, and impact us as individuals and communities in different ways. They differ fundamentally from each other along dimensions such as alignment of incentives between stakeholders, "creep factor", and the nature of how their feedback loops ope !L

"This paper explores the current deficiencies in the way the United States supports children and families and shows how a new economic model-a "next system," based in part on the best practices currently in use somewhere in the world-might better provide for flourishing families and children, both in the United States and around the world. We consider current policies, both international and domestic, that seem to provide the best results in today's global economic system. We then suggest how these "best practices" might be incorporated into a larger model for family and child well-being in "the next system." We proceed to consider new ideas not yet implemented that can improve outcomes, and, finally, suggest some pragmatic, step-by-step strategies for moving toward a world that offers the best possible outcomes for all." https://thenextsystem.org/learn/stories/families-and-children-next-system

"My colleague Jamie Robins and I are working on a book that provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in several disciplines or confined to technical articles. We expect that the book will be of interest to anyone interested in causal inference, e.g., epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists… The book is divided in 3 parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data. We are making drafts of selected book sections available on this website. The idea is that interested readers can submit suggestions or criticisms before the book is published. To share any comments, please email me or visit @causalinference on Facebook. To cite the book, please use "Hernán MA, Robins JM (2018). Causal Inference. Boca Raton: Chapman & Hall/CRC, forthcoming.""

In a collaboration with Italian scientists as part of the European project Ecopotential, EPFL scientists built a model to predict the dynamics of two carabid species across the landscape of Gran Paradiso National Park in the Graian Alps, in Northern Italy, now combining field measurement with advanced remote sensing. The results are published in PNAS and the open-model is available on GitHub. "The main result of this work, which I deem important, is to suggest that an integrated ecohydrological framework blending field evidence, both theoretical and remotely acquired, has contributed substantially to our understanding of key indicators of ecological well-being, carabid beetles, in complex environments like iconic mountains," explains Andrea Rinaldo, who leads the Laboratory of Ecohydrology.

The analysis of questionnaires often involves representing the high-dimensional responses in a low-dimensional space (e.g., PCA, MCA, or t-SNE). However questionnaire data often contains categorical variables and common statistical model assumptions rarely hold. Here we present a non-parametric approach based on Fisher Information which obtains a low-dimensional embedding of a statistical manifold (SM). The SM has deep connections with parametric statistical models and the theory of phase transitions in statistical physics. Firstly we simulate questionnaire responses based on a non-linear SM and validate our method compared to other methods. Secondly we apply our method to two empirical datasets containing largely categorical variables: an anthropological survey of rice farmers in Bali and a cohort study on health inequality in Amsterdam. Compare to previous analysis and known anthropological knowledge we conclude that our method best discriminates between different behaviours, paving the way to dimension reduction as effective as for continuous data.

The complexity of an ecological community can be distilled into a network, where diverse interactions connect species in a web of dependencies. Species interact directly with each other and indirectly through environmental effects, however to our knowledge the role of these ecosystem engineers has not been considered in ecological network models. Here we explore the dynamics of ecosystem assembly, where species colonization and extinction depends on the constraints imposed by trophic, service, and engineering dependencies. We show that our assembly model reproduces many key features of ecological systems, such as the role of generalists during assembly, realistic maximum trophic levels, and increased nestedness with mutualistic interactions. We find that ecosystem engineering has large and nonlinear effects on extinction rates. While small numbers of engineers reduce stability by increasing primary extinctions, larger numbers of engineers increase stability by reducing primary extinctions and extinction cascade magnitude. Our results suggest that ecological engineers may enhance community diversity while increasing persistence by facilitating colonization and limiting competitive exclusion.