The COVID-19 pandemic has profound global consequences on health, economic, social, political, and almost every major aspect of human life. Therefore, it is of great importance to model COVID-19 and other pandemics in terms of the broader social contexts in which they take place. We present the architecture of AICov, which provides an integrative deep learning framework for COVID-19 forecasting with population covariates, some of which may serve as putative risk factors. We have integrated multiple different strategies into AICov, including the ability to use deep learning strategies based on LSTM and even modeling. To demonstrate our approach, we have conducted a pilot that integrates population covariates from multiple sources. Thus, AICov not only includes data on COVID-19 cases and deaths but, more importantly, the population's socioeconomic, health and behavioral risk factors at a local level. The compiled data are fed into AICov, and thus we obtain improved prediction by integration of the data to our model as compared to one that only uses case and death data.

We explore the effects of architecture and training objective choice on amortized posterior predictive inference in probabilistic conditional generative models. We aim this work to be a counterpoint to a recent trend in the literature that stresses achieving good samples when the amount of conditioning data is large. We instead focus our attention on the case where the amount of conditioning data is small. We highlight specific architecture and objective choices that we find lead to qualitative and quantitative improvement to posterior inference in this low data regime. Specifically we explore the effects of choices of pooling operator and variational family on posterior quality in neural processes. Superior posterior predictive samples drawn from our novel neural process architectures are demonstrated via image completion/in-painting experiments.

Floods are one of the major climate-related disasters, leading to substantial economic loss and social safety issue. However, the confidence in predicting changes in fluvial floods remains low due to limited evidence and complex causes of regional climate change. The recent development in machine learning techniques has the potential to improve traditional hydrological models by using monitoring data. Although Recurrent Neural Networks (RNN) perform remarkably with multivariate time series data, these models are blinded to the underlying mechanisms represented in a process-based model for flood prediction. While both process-based models and deep learning networks have their strength, understanding the fundamental mechanisms intrinsic to geo-spatiotemporal information is crucial to improve the prediction accuracy of flood occurrence. This paper demonstrates a neural network architecture (HydroDeep) that couples a process-based hydro-ecological model with a combination of Deep Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to build a hybrid baseline model. HydroDeep outperforms the performance of both the independent networks by 4.8% and 31.8% respectively in Nash-Sutcliffe efficiency. A trained HydroDeep can transfer its knowledge and can learn the Geo-spatiotemporal features of any new region in minimal training iterations.

Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role for the adoption and impact of said technologies. In this work, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems. We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse. Then, we provide an overview of the prospective research directions towards which the community may engage, challenging existing assumptions and making explicit connections to other ethical challenges such as security, privacy, and fairness.

Moody's Analytics chief markets economist John Lonski on whether or not the U.S. needs another stimulus. A leader of the bipartisan Problem Solvers Caucus lamented the White House calling off coronavirus stimulus talks and urged leaders to get back to the negotiating table because a deal is within reach. Rep. Tom Reed, R-N.Y., urged President Trump and congressional leaders to continue to fight for a broad relief package, rather than a piecemeal deal. "We are within inches of getting this done," Reed, R-N.Y., said Wednesday. "Let's not walk away now."

The Pittsburgh space robotics company Astrobotic has delivered its CubeRover to NASA's Kennedy Space Center in Florida, where the robot will undergo a battery of mobility and drop tests in a simulated lunar terrain. Co-developed by Astrobotic and Carnegie Mellon University with input from a NASA Kennedy team, CubeRover is a small, light robotic rover designed as an affordable mobile platform for scientific instruments and other payloads to operate on the surface of the moon. "Because our CubeRover is so light -- in the four-kilogram range -- it dramatically reduces flight cost, making the moon more accessible to more customers," said Mike Provenzano, Astrobotic's director of planetary mobility. CubeRover is a commercial version of Iris, a CMU-built rover scheduled to land on the moon as early as next summer.

With more users now banking online, the risk of being duped by fraudsters is higher than before. A May survey by ACI Worldwide and YouGov found that 32% Indians were using digital payments more, while 31% were recently targeted by a card or digital payments fraud or know someone who was. According to a 2019 RBI report, losses due to banking frauds have grown by 73.8%. Dilip Asbe, MD, CEO, National payments Corporation of India (NPCI), said that many banks in India have already launched fraud detection robo advisory services for investments. Asbe was speaking at the global AI summit RAISE (Responsible AI for social Empowerment), being held online from October 5-9. Also, speaking at the summit, T Rabi Shankar, executive director at the Reserve Bank of India (RBI), agreed that AI backed robo advisory services have a lot of potential.

And yet rapid tests like the Abbott test have led to reports among the general population of false negatives (reports that you don't have the virus when you really do). That means some people may have been unknowingly spreading the virus to others. The White House outbreak is a very good illustration of the limitations of rapid testing. But it should not deter us from the strategy entirely--we just need to use the technology properly. No test is 100% accurate, but the gold standard for diagnosing covid-19 is a PCR test.

The US Army is looking to build an autonomous charging system that can support hundreds of drones. It has funded a four-year research project with the ultimate aim of kitting out ground-based vehicles with charging stations that swarms of drones can fly to by themselves. The University of Illinois Chicago landed an $8 million contract from the Combat Capabilities Development Command's Army Research Laboratory. Researchers will work on a system that will enable small drones to determine the location of the closest charging station, travel there and juice up before returning to their mission. The university is working on algorithms to help the drones determine the best route to a charging port.

Bengaluru: India will inevitably be one of the world's artificial intelligence (AI) superpowers, Brad Smith, president and chief legal officer, Microsoft Corp. said at the ongoing Responsible AI for Social Empowerment (RAISE 2020) event. Smith indicated that India's national strategy on AI is the right foundation, not just to advance AI but also topromote responsible AI built on firm ethical principles. The government of India is in the process of finalizing a national strategy on AI which was released in June 2018. The strategy outlines the proposed efforts in research, development, adoption and skilling in AI. "AI can revolutionize virtually every part of the economy, and I think in so many ways the countries that move the fastest to deploy AI more quickly than others, will find that they will be accelerating economic growth," Smith said. If applied in the right way, AI will not be a competitor to the thinking or work of human beings but a tool that can augment and add to what humans can accomplish.