The modern space race is heating up, and the European Union is acutely aware that it needs to keep pace. Space chief Thierry Breton told Reuters in an interview that the EU is accelerating its plans in light of rapid progress by private companies like SpaceX as well as China's successes. It's moving the deployment of its Galileo navigation satellites ahead by three years, to 2024, and will use its budget for the first time to support reusable rockets and other new launch tech. The EU is also forging a €1 billion deal with Arianespace to spur innovation, and will propose a €1 billion European Space Fund and competitions to foster startups. Breton also hoped to launch a pan-European satellite broadband network as well as a system to avoid collisions with satellites and other items in orbit.

Urbint, a startup developing AI-powered solutions for infrastructure and utility safety, today announced it has raised a $20 million round. The company will use the capital to scale products and expand into new markets and countries, with the goal of delivering "quantifiable" improvements in worker well-being. In the early days of the COVID-19 pandemic, energy companies around the U.S. were forced to send workers home to reduce the spread of infection. This exacerbated many of the industry's longstanding challenges, like how to minimize risks from severe weather, aging infrastructure, and workforce turnover while identifying threats that could cause high-consequence outages to facilities like hospitals and nursing homes. Urbint taps AI to anticipate and prevent catastrophic power failures, with models of the world and machine learning that enable risk-driven decision-making.

When it comes to Covid-19, one big question is how are we using artificial intelligence (AI) to help? While there is much focus on contact tracing and virus research, there are other big problems resulting from the coronavirus. Going beyond this, there are five key ares for people to focus AI on.

No one was prepared for COVID-19. No one but a handful of epidemiologists and visionaries -- including, most famously, Bill Gates in his now-viral TED talk -- had an inkling that the next global pandemic was a matter of when and not if. In retrospect, our unpreparedness is embarrassing and almost inexcusable. All the more so considering everything we know about HIV, SARS, MERS, and, most recently, the Ebola and Zika epidemics. We've all been talking about AI's superpower for the last decade.

During boom times, companies focus on growth. In tough times, they seek to improve efficiency. History shows us that after every major economic downturn since the 1980s, businesses relied on digital technology and, specifically, innovations in software technology to return to full productivity with fewer repetitive jobs and less bloat. The years I've spent as a VC have convinced me that this is the best time to start an AI-first enterprise, not despite the recession, but because of it. The next economic recovery will both be driven by artificial intelligence and accelerate its adoption.

The Morrison Government is investing $19 million in transformative medical research projects using game-changing applied artificial intelligence (AI) technologies, to improve the ways we prevent, diagnose and treat a wide range of health conditions. The Government is providing more than $8 million for two projects that will use AI to improve mental health treatments for Australians. The University of Sydney will receive more than $3 million to improve youth mental health care through the development of new tools to guide clinical decisions about the appropriate interventions and treatments for individuals presenting for care. This project will use AI to test and quantify the impacts of youth mental health interventions and as a result support the development of an ethical clinical decision-support tool that identifies how to target assessment and interventions to optimise outcomes for individuals presenting for mental health care. The University of New South Wales will receive almost $5 million to use AI to understand and optimise the treatments for stress, anxiety and depression.

These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivalled pace has left the internet and technology watchers aghast. Artificial intelligence has become the byword for technological progress and is being used in everything from helping us combat the COVID-19 pandemic to nudging our attention in different directions as we all spend increasingly larger amounts of time online. It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other. With this inaugural edition of the State of AI Ethics we hope to bring forward the most important developments that caught our attention at the Montreal AI Ethics Institute this past quarter. Our goal is to help you navigate this ever-evolving field swiftly and allow you and your organization to make informed decisions. This pulse-check for the state of discourse, research, and development is geared towards researchers and practitioners alike who are making decisions on behalf of their organizations in considering the societal impacts of AI-enabled solutions. We cover a wide set of areas in this report spanning Agency and Responsibility, Security and Risk, Disinformation, Jobs and Labor, the Future of AI Ethics, and more. Our staff has worked tirelessly over the past quarter surfacing signal from the noise so that you are equipped with the right tools and knowledge to confidently tread this complex yet consequential domain.

Vector autoregressive (VAR) models assume linearity between the endogenous variables and their lags. This linearity assumption might be overly restrictive and could have a deleterious impact on forecasting accuracy. As a solution, we propose combining VAR with Bayesian additive regression tree (BART) models. The resulting Bayesian additive vector autoregressive tree (BAVART) model is capable of capturing arbitrary non-linear relations between the endogenous variables and the covariates without much input from the researcher. Since controlling for heteroscedasticity is key for producing precise density forecasts, our model allows for stochastic volatility in the errors. Using synthetic and real data, we demonstrate the advantages of our methods. For Eurozone data, we show that our nonparametric approach improves upon commonly used forecasting models and that it produces impulse responses to an uncertainty shock that are consistent with established findings in the literature.

In this work, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in non-convex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning the solutions of elliptic PDE, which demonstrate the accuracy and efficiency of present work.

Retrosynthetic planning is a critical task in organic chemistry which identifies a series of reactions that can lead to the synthesis of a target product. The vast number of possible chemical transformations makes the size of the search space very big, and retrosynthetic planning is challenging even for experienced chemists. However, existing methods either require expensive return estimation by rollout with high variance, or optimize for search speed rather than the quality. In this paper, we propose Retro*, a neural-based A*-like algorithm that finds high-quality synthetic routes efficiently. It maintains the search as an AND-OR tree, and learns a neural search bias with off-policy data. Then guided by this neural network, it performs best-first search efficiently during new planning episodes. Experiments on benchmark USPTO datasets show that, our proposed method outperforms existing state-of-the-art with respect to both the success rate and solution quality, while being more efficient at the same time.