TechSee provides a visual engagement platform powered by deep learning and computer vision, enabling the auto-recognition of devices and issues in order to offer proven resolutions. Customers receive precise AR visual guidance in both assisted service and self-service modes at every stage of the journey, from sales, registration and onboarding to claims and upsell. They serve tier 1 companies and global groups in 24 markets around the world, including leading P&C insurers, telecoms and consumer electronics manufacturers. The Israeli startup was founded to help businesses better support their operations from all perspectives: customers, contact center agents, field technicians, and self-service channels. Over two decades of experience across CX technologies, visual computing, augmented reality, and big data enables TechSee to follow through on this commitment.

SWITZERLAND, GENEVA – Singapore announced that its work in Artificial Intelligence (AI) Governance and Ethics has won a top award at the prestigious World Summit on the Information Society (WSIS) Prizes1. The winners of the 18 categories2 of the WSIS Prizes were announced today during an award ceremony at the annual WSIS Forum held in Geneva, Switzerland. Singapore won in the "Ethical Dimensions of the Information Society" category, beating four other finalists3 across the globe. Info-communications Media Development Authority (IMDA) Assistant Chief Executive (Data Protection and Innovation) Yeong Zee Kin received the prize at the ceremony in Geneva. Singapore's AI Governance and Ethics initiatives aim to build an ecosystem of trust to support AI adoption. These initiatives advance Singapore's vision to be a leading Digital Economy and Smart Nation through balancing business innovation and consumer trust and confidence in adopting AI.

ABU DHABI - President Donald Trump's national security adviser warned Iran on Wednesday that any attacks in the Persian Gulf will draw a "very strong response" from the U.S., taking a hard-line approach with Tehran after his boss only two days earlier said America wasn't "looking to hurt Iran at all." John Bolton's comments are the latest amid heightened tensions between Washington and Tehran that have been playing out in the Middle East. Bolton spoke to journalists in Abu Dhabi, the capital of the United Arab Emirates, which only days earlier saw former Defense Secretary Jim Mattis warn there that "unilateralism will not work" in confronting the Islamic Republic. The dueling approaches highlight the divide over Iran within American politics. The U.S. has accused Tehran of being behind a string of incidents this month, including the alleged sabotage of oil tankers off the Emirati coast, a rocket strike near the U.S. Embassy in Baghdad and a coordinated drone attack on Saudi Arabia by Yemen's Iran-allied Houthi rebels. On Wednesday, Bolton told journalists that there had been a previously unknown attempt to attack the Saudi oil port of Yanbu as well, which he also blamed on Iran.

As lawmakers, citizens, and company's debate the use of facial recognition software in the U.S., tech giants in America and China have been busy hawking products to eager surveillance states abroad. Among the burgeoning markets, according to a report by Buzzfeed News, are monarchies in the United Arab Emirates (UAE), particularly in Dubai, where political leaders have often jailed citizens and journalists that they deem to be political dissidents. Critics of the UAE include Human Rights Watch (HRW) who has frequently derided the country for its authoritarian tendencies. Private companies like IBM are looking to governments accused of violating human rights as a market for facial recognition software. 'UAE authorities have launched a sustained assault on freedom of expression and association since 2011,' says HRW in its analysis.

Computing the Wasserstein barycenter of a set of probability measures under the optimal transport metric can quickly become prohibitive for traditional second-order algorithms, such as interior-point methods, as the support size of the measures increases. In this paper, we overcome the difficulty by developing a new adapted interior-point method that fully exploits the problem's special matrix structure to reduce the iteration complexity and speed up the Newton procedure. Different from regularization approaches, our method achieves a well-balanced tradeoff between accuracy and speed. A numerical comparison on various distributions with existing algorithms exhibits the computational advantages of our approach. Moreover, we demonstrate the practicality of our algorithm on image benchmark problems including MNIST and Fashion-MNIST.

Differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy in the past decade. This privacy definition and its divergence based relaxations, however, have several acknowledged weaknesses, either in handling composition of private algorithms or in analyzing important primitives like privacy amplification by subsampling. Inspired by the hypothesis testing formulation of privacy, this paper proposes a new relaxation, which we term `$f$-differential privacy' ($f$-DP). This notion of privacy has a number of appealing properties and, in particular, avoids difficulties associated with divergence based relaxations. First, $f$-DP preserves the hypothesis testing interpretation. In addition, $f$-DP allows for lossless reasoning about composition in an algebraic fashion. Moreover, we provide a powerful technique to import existing results proven for original DP to $f$-DP and, as an application, obtain a simple subsampling theorem for $f$-DP. In addition to the above findings, we introduce a canonical single-parameter family of privacy notions within the $f$-DP class that is referred to as `Gaussian differential privacy' (GDP), defined based on testing two shifted Gaussians. GDP is focal among the $f$-DP class because of a central limit theorem we prove. More precisely, the privacy guarantees of \emph{any} hypothesis testing based definition of privacy (including original DP) converges to GDP in the limit under composition. The CLT also yields a computationally inexpensive tool for analyzing the exact composition of private algorithms. Taken together, this collection of attractive properties render $f$-DP a mathematically coherent, analytically tractable, and versatile framework for private data analysis. Finally, we demonstrate the use of the tools we develop by giving an improved privacy analysis of noisy stochastic gradient descent.

Integrating deep learning with latent state space models has the potential to yield temporal models that are powerful, yet tractable and interpretable. Unfortunately, current models are not designed to handle missing data or multiple data modalities, which are both prevalent in real-world data. In this work, we introduce a factorized inference method for Multimodal Deep Markov Models (MDMMs), allowing us to filter and smooth in the presence of missing data, while also performing uncertainty-aware multimodal fusion. We derive this method by factorizing the posterior p(z|x) for non-linear state space models, and develop a variational backward-forward algorithm for inference. Because our method handles incompleteness over both time and modalities, it is capable of interpolation, extrapolation, conditional generation, and label prediction in multimodal time series. We demonstrate these capabilities on both synthetic and real-world multimodal data under high levels of data deletion. Our method performs well even with more than 50% missing data, and outperforms existing deep approaches to inference in latent time series.

Given unpaired data from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain. Variants of this problem have been studied in many contexts, such as cross-domain translation and domain adaptation. We propose AlignFlow, a generative modeling framework for learning from multiple domains via normalizing flows. The use of normalizing flows in AlignFlow allows for a) flexibility in specifying learning objectives via adversarial training, maximum likelihood estimation, or a hybrid of the two methods; and b) exact inference of the shared latent factors across domains at test time. We derive theoretical results for the conditions under which AlignFlow guarantees marginal consistency for the different learning objectives. Furthermore, we show that AlignFlow guarantees exact cycle consistency in mapping datapoints from one domain to another. Empirically, AlignFlow can be used for data-efficient density estimation given multiple data sources and shows significant improvements over relevant baselines on unsupervised domain adaptation.

The data arising in many important big-data applications, ranging from social networks to network medicine, consist of high-dimensional data points related by an intrinsic (complex) network structure. In order to jointly leverage the information conveyed in the network structure as well as the statistical power contained in high-dimensional data points, we propose networked exponential families. We apply the network Lasso to learn networked exponential families as a probabilistic model for heterogeneous datasets with intrinsic network structure. In order to allow for accurate learning from high-dimensional data, we borrow statistical strength, via the intrinsic network structure, across the dataset. The resulting method aims at regularized empirical risk minimization using the total variation of the model parameters as regularizer. This minimization problem is a non-smooth convex optimization problem which we solve using a primal-dual splitting method. This method is appealing for big data applications as it can be implemented as a highly scalable message passing algorithm.

Using a low-dimensional parametrization of signals is a generic and powerful way to enhance performance in signal processing and statistical inference. A very popular and widely explored type of dimensionality reduction is sparsity; another type is generative modelling of signal distributions. Generative models based on neural networks, such as GANs or variational auto-encoders, are particularly performant and are gaining on applicability. In this paper we study spiked matrix models, where a low-rank matrix is observed through a noisy channel. This problem with sparse structure of the spikes has attracted broad attention in the past literature. Here, we replace the sparsity assumption by generative modelling, and investigate the consequences on statistical and algorithmic properties. We analyze the Bayes-optimal performance under specific generative models for the spike. In contrast with the sparsity assumption, we do not observe regions of parameters where statistical performance is superior to the best known algorithmic performance. We show that in the analyzed cases the approximate message passing algorithm is able to reach optimal performance. We also design enhanced spectral algorithms and analyze their performance and thresholds using random matrix theory, showing their superiority to the classical principal component analysis. We complement our theoretical results by illustrating the performance of the spectral algorithms when the spikes come from real datasets.