China's military is working to adopt artificial intelligence and autonomous technology as quickly as it can. That's because Chinese President Xi Jinping believes that AI and other cutting-edge technologies, especially when applied to military systems, are the key to keeping up and leveling the playing field between China and countries like the U.S. that had a head start on its industrial development, according to a new report published Wednesday by a think tank called the Center for a New American Security. The push for advanced Chinese AI is focused on more than military might -- the new report highlights how China is taking steps to move away from importing foreign technology. In October, Xi was quoted as saying that China should strive to "achieve world-leading levels" in artificial intelligence tech while also eliminating its "external dependence for key technologies and advanced equipment," according to the report.

YEREVAN, Armenia – Armenia has sent a team of experts to Syria on a Russia-backed mission to help clear mines and provide medical assistance. Armenian Defense Ministry spokesman Artsrun Hovhannisyan said Saturday the team of 83 includes de-mining experts, medical personnel and security officers. He said it will defuse mines and provide medical help to residents of Aleppo, in northern Syria. Before the war, Aleppo was home to 110,000 ethnic Armenians, one of the world's largest Armenian diasporas. About 22,000 have since moved to Armenia.

China may lag behind the US military on metrics like the number of aircraft carriers it has, but it may be able to seize a "leapfrog opportunity" and invest in newer, cheaper weapons that could make carriers obsolete. Similar to how some countries never developed extensive landline infrastructure and instead skipped directly to building mobile phone networks, China is capitalizing on the opportunity to develop AI-based technology, including autonomous submarines that could confront hulking US carriers. At the same time, the United States could end up spending "too much to maintain and upgrade mature systems,'" according to one Chinese scholar quoted in the report by the Center for a New American Security (CNAS). The United States, China, and Russia have all stressed the importance of AI-based military technologies and are making notable investments. China is investing tens of billions of dollars in AI development, according to the CNAS report, with the government viewing it as a key strategy to "protect national security."

The Singapore government has released an artificial intelligence (AI) governance framework to help businesses tackle the ethical and governance challenges arising from the growing use of AI across industries. Want to know what will dominate the world of IT in 2019? You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

Dr Ramasamy Kim is looking at the inside of an eyeball. There is nothing particularly surprising about that: he is head of retina services at an eye hospital in southern India. The image on his computer screen shows the first blush of a condition linked to diabetes that affects millions of Indians – and can lead to blindness. The diagnosis was made not by him, or any other doctor, but by an algorithm. Over the past five years, Kim and his team at the Aravind eye hospital in Madurai have examined about 15,000 images from across the country showing the interior surface of the eyeball, known as the fundus.

Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants of the original GAN focusing on various aspects of deep learning. GAN are perceived as the most impactful direction of machine learning in the last decade. This paper focuses on the application of GAN in autonomous driving including topics such as advanced data augmentation, loss function learning, semi-supervised learning, etc. We formalize and review key applications of adversarial techniques and discuss challenges and open problems to be addressed.

We study the problem of estimating a nonparametric probability distribution under a family of losses called Besov IPMs. This family is quite large, including, for example, $L^p$ distances, total variation distance, and generalizations of both Wasserstein (earthmover's) and Kolmogorov-Smirnov distances. For a wide variety of settings, we provide both lower and upper bounds, identifying precisely how the choice of loss function and assumptions on the data distribution interact to determine the mini-max optimal convergence rate. We also show that, in many cases, linear distribution estimates, such as the empirical distribution or kernel density estimator, cannot converge at the optimal rate. These bounds generalize, unify, or improve on several recent and classical results. Moreover, IPMs can be used to formalize a statistical model of generative adversarial networks (GANs). Thus, we show how our results imply bounds on the statistical error of a GAN, showing, for example, that, in many cases, GANs can strictly outperform the best linear estimator.

We show that fundamental learning tasks, such as finding an approximate linear separator or linear regression, require memory at least \emph{quadratic} in the dimension, in a natural streaming setting. This implies that such problems cannot be solved (at least in this setting) by scalable memory-efficient streaming algorithms. Our results build on a memory lower bound for a simple linear-algebraic problem -- finding orthogonal vectors -- and utilize the estimates on the packing of the Grassmannian, the manifold of all linear subspaces of fixed dimension.

Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed and the underlying structures are difficult to interpret. In recent years, a promising technique called algorithm unrolling has been developed that has helped connect iterative algorithms such as those for sparse coding to neural network architectures. However, such connections have not been made yet for blind image deblurring. In this paper, we propose a neural network architecture based on this idea. We first present an iterative algorithm that may be considered as a generalization of the traditional total-variation regularization method in the gradient domain. We then unroll the algorithm to construct a neural network for image deblurring which we refer to as Deep Unrolling for Blind Deblurring (DUBLID). Key algorithm parameters are learned with the help of training images. Our proposed deep network DUBLID achieves significant practical performance gains while enjoying interpretability at the same time. Extensive experimental results show that DUBLID outperforms many state-of-the-art methods and in addition is computationally faster.

We determine the sample complexity of pure exploration bandit problems with multiple good answers. We derive a lower bound using a new game equilibrium argument. We show how continuity and convexity properties of single-answer problems ensures that the Track-and-Stop algorithm has asymptotically optimal sample complexity. However, that convexity is lost when going to the multiple-answer setting. We present a new algorithm which extends Track-and-Stop to the multiple-answer case and has asymptotic sample complexity matching the lower bound.