Over the past few months, high-profile incidents in the United Kingdom, one of the most surveilled societies in the world, forced people to consider how facial recognition will be used there. Brexit taking up most of the oxygen in the room hasn't made that debate any easier, but in conversations with VentureBeat, three experts from different backgrounds -- Ada Lovelace Institute director Carly Kind, the U.K.'s surveillance camera commissioner Tony Porter, and University of Essex professor Daragh Murray, who studies police use of facial recognition -- all agree that the U.K. needs to find a middle ground. All three agree that years of Brexit debate have stifled necessary reform, and that leaving the European Union could carry consequences for years to come as police and businesses continue experiments with facial recognition in the U.K. They also worry that an inability to take action could lead to calls for a ban or overregulation, or far more dystopian scenarios of facial recognition everywhere. The Terminator's got serious competition for symbolizing the fear of technology trampling human rights. Facial recognition has become a major issue around the globe due both to its deeply personal and pervasive nature as well as advances in AI that now make it work in real time. In democratic societies worldwide, facial recognition is challenging lawmakers to confront how AI will shape society and is redefining attitudes toward artificial intelligence.

The artificial intelligence in manufacturing market is poised to hike from USD 1 billion in 2018 to over USD 16 billion by 2025, according to a 2019 Global Market Insights, Inc. report. The AI in manufacturing market is driven by the rapid adoption of industry 4.0 technologies. The growing need among the manufacturers to reduce the cost of operation and enhance operational efficiency is the primary factor driving the adoption of Industry 4.0. The new technology solutions are enhancing operational efficiency and reducing the time to market the products. It allows enterprises to analyze the customer demand, align their operations to meet the customer's requirement, and analyze the process in real-time.

Of the world's 10 million people in the world diagnosed with both tuberculosis (TB) and drug-resistant tuberculosis in 2017, 2.7 million live in India, making us a country with the highest burden of the disease, according to the World Health Organization (WHO). Using the contact-free sensor that's placed under your mattress, Dozee tracks and analyzes your heart health, respiration, sleep quality, stress levels and more. What's worse, many remain undiagnosed and those who are detected with TB are only diagnosed weeks after they get it. With this delay, these unsuspecting carriers spread the disease to others in their homes or workplaces. This is particularly a risk for young children.

Artificial Intelligence (AI) is the buzzword these days, and for good reason. Businesses around the world are taking up AI technologies to try and reduce operational costs, increase efficiency, grow revenue and improve customer experience. Businesses are also looking at putting a full range of smart technologies such as machine learning, natural language processing and more, into their processes and products. However can businesses that are new to AI, reap major rewards? When it comes to artificial intelligence, most people are well aware of the tropes from popular entertainment: the malevolent computer, the android gone rogue.

Abstract--Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in many real-world applications such as monitoring urban traffic and air quality . Making predictions on these time series has become a critical challenge due to not only the large-scale and high-dimensional nature but also the considerable amount of missing data. In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series--in particular spatiotemporal data--in the presence of missing values. By integrating low-rank matrix/tensor factorization and vector autoregressive (VAR) process into a single probabilistic graphical model, this framework can characterize both global and local consistencies in large-scale time series data. The graphical model allows us to effectively perform probabilistic predictions and produce uncertainty estimates without imputing those missing values. We develop efficient Gibbs sampling algorithms for model inference and test the proposed BTF framework on several real-world spatiotemporal data sets for both missing data imputation and short-term/long-term rolling prediction tasks. The numerical experiments demonstrate the superiority of the proposed BTF approaches over many state-of-the-art techniques. With recent advances in sensing technologies, large-scale and multidimensional time series data--in particular spatiotemporal data--are collected on a continuous basis from various types of sensors and applications. Making predictions on these time series, such as forecasting urban traffic states and regional air quality, serves as a foundation to many real-world applications and benefits many scientific fields [1], [2]. For example, predicting the demand and states (e.g., speed, flow) of urban traffic is essential to a wide range of intelligent transportation systems (ITS) applications, such trip planning, travel time estimation, route planning, traffic signal control, to name but a few [3]. However, given the complex spatiotemporal dependencies in these data sets, making efficient and reliable predictions for real-time applications has been a longstanding and fundamental research challenge. Despite the vast body of literature on time series analysis from many scientific areas, three emerging issues in modern sensing technologies are constantly challenging the classical modeling frameworks. First, modern time series data are often large-scale, collected from a large number of subjects/locations/sensors simultaneously .

Many real-world graphs or networks are temporal, e.g., in a social network persons only interact at specific points in time. This information directs dissemination processes on the network, such as the spread of rumors, fake news, or diseases. However, the current state-of-the-art methods for supervised graph classification are designed mainly for static graphs and may not be able to capture temporal information. Hence, they are not powerful enough to distinguish between graphs modeling different dissemination processes. To address this, we introduce a framework to lift standard graph kernels to the temporal domain. Specifically, we explore three different approaches and investigate the trade-offs between loss of temporal information and efficiency. Moreover, to handle large-scale graphs, we propose stochastic variants of our kernels with provable approximation guarantees. We evaluate our methods on a wide range of real-world social networks. Our methods beat static kernels by a large margin in terms of accuracy while still being scalable to large graphs and data sets. Hence, we confirm that taking temporal information into account is crucial for the successful classification of dissemination processes.

Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth. Biases identified with the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol optical depth (AOD) dataset, against the Aerosol Robotic Network (AERONET) ground measurements in previous studies, motivated the development of a deep learning based AOD prediction model globally. This study combines a convolutional neural network (CNN) with MERRA-2, tested against all AERONET sites. The new hybrid CNN-based model provides better estimates validated versus AERONET ground truth, than only using MERRA-2 reanalysis.

Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, molecular chemistry, and experimental design. We introduce BoTorch, a modern programming framework for Bayesian optimization. Enabled by Monte-Carlo (MC) acquisition functions and auto-differentiation, BoTorch's modular design facilitates flexible specification and optimization of probabilistic models written in PyTorch, radically simplifying implementation of novel acquisition functions. Our MC approach is made practical by a distinctive algorithmic foundation that leverages fast predictive distributions and hardware acceleration. In experiments, we demonstrate the improved sample efficiency of BoTorch relative to other popular libraries. BoTorch is open source and available at https://github.com/pytorch/botorch.

The breakthrough of deep Q-Learning on different types of environments revolutionized the algorithmic design of Reinforcement Learning to introduce more stable and robust algorithms, to that end many extensions to deep Q-Learning algorithm have been proposed to reduce the variance of the target values and the overestimation phenomena. In this paper, we examine new methodology to solve these issues, we propose using Dropout techniques on deep Q-Learning algorithm as a way to reduce variance and overestimation. We further present experiments on some of the benchmark environments that demonstrate significant improvement of the stability of the performance and a reduction in variance and overestimation.

Emergent properties of the local geometry of neural loss landscapesStanislav Fort Surya Ganguli Stanford University Stanford, CA, USA Stanford University Stanford, CA, USA Abstract The local geometry of high dimensional neural network loss landscapes can both challenge our cherished theoretical intuitions as well as dramatically impact the practical success of neural network training. Indeed recent works have observed 4 striking local properties of neural loss landscapes on classification tasks: (1) the landscape exhibits exactly C directions of high positive curvature, where C is the number of classes; (2) gradient directions are largely confined to this extremely low dimensional subspace of positive Hessian curvature, leaving the vast majority of directions in weight space unexplored; (3) gradient descent transiently explores intermediate regions of higher positive curvature before eventually finding flatter minima; (4) training can be successful even when confined to low dimensional random affine hy-perplanes, as long as these hyperplanes intersect a Goldilocks zone of higher than average curvature. We develop a simple theoretical model of gradients and Hessians, justified by numerical experiments on architectures and datasets used in practice, that simultaneously accounts for all 4 of these surprising and seemingly unrelated properties. Our unified model provides conceptual insights into the emergence of these properties and makes connections with diverse topics in neural networks, random matrix theory, and spin glasses, including the neural tangent kernel, BBP phase transitions, and Derrida's random energy model. 1 Introduction The geometry of neural network loss landscapes and the implications of this geometry for both optimization and generalization have been subjects of intense interest in many works, ranging from studies on the lack of local minima at significantly higher loss than that of the global minimum [1, 2] to studies debating relations between the curvature of local minima and their generalization properties [3, 4, 5, 6]. Fundamentally, the neural network loss landscape is a scalar loss function over a very high D dimensional parameter space that could depend a priori in highly nontrivial ways on the very structure of real-world data itself as well as intricate properties of the neural network architecture. Moreover, the regions of this loss landscape explored by gradient descent could themselves have highly atypical geometric properties relative to randomly chosen points in the landscape.