The Fraunhofer Institute for Laser Technology (Fraunhofer ILT; Aachen, Germany) will be hosting its AI for Laser Technology Conference on November 6-7, 2019, in Aachen. There, Fraunhofer ILT will address the question of how lasers can be used more efficiently with the help of artificial intelligence (AI), and conference topics will range from machine learning in industrial settings to the use of augmented reality and analysis of neural networks. The idea of holding a conference on the use of AI in laser systems originated at the Process Control group at Fraunhofer ILT, which has been researching AI for a number of years. The scientists are interested in using AI methods to draw one-to-one conclusions on, for example, detecting errors in laser welding. The conference agenda has a practical focus that reflects these first applied uses of AI in laser material processing.

UNISOC today announced the launch of its new AI-enabled edge computing platform, Tiger T710. Designed for a comprehensive range of AI applications in industrial, commerce, medical care, home, and education, the platform will help to accelerate the transition from the traditional industrial society to a smart society. Representing a new generation of UNISOC AI solutions, Tiger T710 is based on octa-core architecture that consists of four 2.0GHz ARM Cortex-A75 and four 1.8GHz ARM Cortex-A55 cores. Using next-generation chip architectures, the UNISOC Tiger T710 boasts high-performance computing, high energy efficiency and reduced development time. UNISOC Tiger T710 is the first chipset platform that adopts the innovative heterogeneous dual-core architecture NPU, allowing it to cope with increasingly complex application scenarios and meet greater demand for computing power. According to the latest AI Benchmark chip testing list released by the Swiss Federal Institute of Technology Zurich, the UNISOC Tiger T710 tops the leaderboard with an outstanding score of 28,097.

It's easy to feel overwhelmed by the change and the infinite number of strategic options. But embracing this change is manageable with the right tools and the right partner. Deloitte is helping clients navigate this increasingly complex, digital world by leveraging the combined strength of our technology capabilities from our Consulting and Tax & Legal practices, and by placing a continued emphasis on technology investment and skills development to prepare talent to meet the evolving needs of the business. Businesses in all sectors and regions are experiencing the opportunities and challenges that come with the immense changes of the Fourth Industrial Revolution. Even the most traditional business areas, such as tax and legal, are not immune.

The agency is anticipating the need to adjust some policies as more inventions are created (or at least assisted) by artificial intelligence. Here are two savvy practitioners' takes on four potential flash points.

Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity. However, the decoder of these parsers has a sequential structure, which does not yield the most appropriate inductive bias for deriving tree structures. In this paper, we propose hierarchical pointer network parsers, and apply them to dependency and sentence-level discourse parsing tasks. Our results on standard benchmark datasets demonstrate the effectiveness of our approach, outperforming existing methods and setting a new state-of-the-art.

Popular culture has contemplated societies of thinking machines for generations, envisioning futures from utopian to dystopian. These futures are, arguably, here now-we find ourselves at the doorstep of technology that can at least simulate the appearance of thinking, acting, and feeling. The real question is: now what?

We introduce a flexible framework for making inferences about general linear forms of a large matrix based on noisy observations of a subset of its entries. In particular, under mild regularity conditions, we develop a universal procedure to construct asymptotically normal estimators of its linear forms through double-sample debiasing and low-rank projection whenever an entry-wise consistent estimator of the matrix is available. These estimators allow us to subsequently construct confidence intervals for and test hypotheses about the linear forms. Our proposal was motivated by a careful perturbation analysis of the empirical singular spaces under the noisy matrix completion model which might be of independent interest.

Generative Adversarial Networks have been crucial in the developments made in unsupervised learning in recent times. Exemplars of image synthesis from text or other images, these networks have shown remarkable improvements over conventional methods in terms of performance. Trained on the adversarial training philosophy, these networks aim to estimate the potential distribution from the real data and then use this as input to generate the synthetic data. Based on this fundamental principle, several frameworks can be generated that are paragon implementations in several real-life applications such as art synthesis, generation of high resolution outputs and synthesis of images from human drawn sketches, to name a few. While theoretically GANs present better results and prove to be an improvement over conventional methods in many factors, the implementation of these frameworks for dedicated applications remains a challenge. This study explores and presents a taxonomy of these frameworks and their use in various image to image synthesis and text to image synthesis applications. The basic GANs, as well as a variety of different niche frameworks, are critically analyzed. The advantages of GANs for image generation over conventional methods as well their disadvantages amongst other frameworks are presented. The future applications of GANs in industries such as healthcare, art and entertainment are also discussed.

Stochastic AUC maximization has garnered an increasing interest due to better fit to imbalanced data classification. However, existing works are limited to stochastic AUC maximization with a linear predictive model, which restricts its predictive power when dealing with extremely complex data. In this paper, we consider stochastic AUC maximization problem with a deep neural network as the predictive model. Building on the saddle point reformulation of a surrogated loss of AUC, the problem can be cast into a {\it non-convex concave} min-max problem. The main contribution made in this paper is to make stochastic AUC maximization more practical for deep neural networks and big data with theoretical insights as well. In particular, we propose to explore Polyak-\L{}ojasiewicz (PL) condition that has been proved and observed in deep learning, which enables us to develop new stochastic algorithms with even faster convergence rate and more practical step size scheme. An AdaGrad-style algorithm is also analyzed under the PL condition with adaptive convergence rate. Our experimental results demonstrate the effectiveness of the proposed algorithms.

Neural network models have been very successful at achieving high accuracy on natural language inference (NLI) tasks. However, as demonstrated in recent literature, when tested on some simple adversarial examples, most of the models suffer a significant drop in performance. This raises the concern about the robustness of NLI models. In this paper, we propose to make NLI models robust by incorporating external knowledge to the attention mechanism using a simple transformation. We apply the new attention to two popular types of NLI models: one is Transformer encoder, and the other is a decomposable model, and show that our method can significantly improve their robustness. Moreover, when combined with BERT pretraining, our method achieves the human-level performance on the adversarial SNLI data set.