The Shenzhen-based company already makes the Kirin series of smartphone chips used in its high-end phones, and the Ascend series of chipsets for artificial intelligence computing launched in October. It said its latest 7 nanometre, 64-core central processing unit (CPU) would provide much higher computing performance for data centers and slash power consumption. It is based on the architecture of British chip design firm ARM - owned by Japan's SoftBank Group Corp - which is seeking to challenge the dominance in server CPUs of U.S. maker Intel Corp. Huawei aims to "drive the development of the ARM ecosystem", said Chief Marketing Officer William Xu. He said the chip has "unique advantages in performance and power consumption". Xu also said Huawei will continue its "long-term strategic partnership" with Intel.

Whill released its first personal electric vehicle in 2016 (in Japan, it came west the following year). Since then it has released new models with different audiences in mind. Here at CES 2019, it's showing its Autonomous Drive System (ADS), which as the name suggests, will shuttle you to where you want to go on its own. Thanks to the addition of front- and rear-mounted cameras, the Autonomous Drive System can navigate public (indoor or outdoor) spaces on its own. A spokesperson told me that the ADS was partly its answer to scooters and bikes offered by the likes of Lime and Bird.

The clash reflects American anxiety about China's rise as a potential competitor in telecommunications and other technology. Trump wants Beijing to roll back initiatives like "Made in China 2025," which calls for the state-led creation of global competitors in such fields as robotics and artificial intelligence. American officials worry those might erode U.S. industrial leadership.

Facial recognition software is making its way into the mainstream, with consumer applications such as the ability to unlock one's smartphone with their face. The banking sector has been at the forefront of enterprise adoption of AI since machine learning became the hot topic of the business world in the early years of the decade; as such, it makes sense that facial recognition technology would start to make its way into banking. There are a handful of companies offering facial recognition software to banks that at face value seem to have the requisite talent in their C-suite that we look for when vetting a company on their claims to leveraging AI. These companies offer software with applications ranging from physical security to the ability for customers to make withdrawals with their faces. That said, what we've found is that the application is relatively nascent in banking.

How does one teach machines and robots, basically a bunch of circuitry and binary code, to behave ethically? Artificial intelligence isn't evil, but how its creators wield and use and apply AI, may make it seem so. Two bodies so far, Singapore's IMDA and the European Commission seem to believe that like everything else that is built into machines and robots, ethics too can be programmed into robots. More specifically, ethical-guided settings can be built in make AI behave ethically. According to an ethics guideline draft authored by the European Commission's AI high-level expert group (HLEG), trustworthy AI has two components.

Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture a fine-grained location profile powered by a diverse range of location data sources, such as transportation profile (e.g., distance to nearest train station), education profile (e.g., school zones and ranking), suburb profile based on census data, facility profile (e.g., nearby hospitals, supermarkets). Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently. However, such modeling ignores the relatedness between partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and each partition obtained is aligned with a task. Furthermore, we select specific MTL-based methods with different regularization terms to capture and exploit the relatedness between tasks. Based on real-world house transaction data collected in Melbourne, Australia. We design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.

Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. We propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penalizing two costs: the original cost the policy seeks to optimize, and an uncertainty cost which represents its divergence from the states it is trained on. We measure this second cost by using the uncertainty of the dynamics model about its own predictions, using recent ideas from uncertainty estimation for deep networks. We evaluate our approach using a large-scale observational dataset of driving behavior recorded from traffic cameras, and show that we are able to learn effective driving policies from purely observational data, with no environment interaction.

Building on a specific formalization of analogical relationships of the form "A relates to B as C relates to D", we establish a connection between two important subfields of artificial intelligence, namely analogical reasoning and kernel-based machine learning. More specifically, we show that so-called analogical proportions are closely connected to kernel functions on pairs of objects. Based on this result, we introduce the analogy kernel, which can be seen as a measure of how strongly four objects are in analogical relationship. As an application, we consider the problem of object ranking in the realm of preference learning, for which we develop a new method based on support vector machines trained with the analogy kernel. Our first experimental results for data sets from different domains (sports, education, tourism, etc.) are promising and suggest that our approach is competitive to state-of-the-art algorithms in terms of predictive accuracy.

For example, in unsupervised machine learning, to evaluate theperformance of a clustering method, researchers typically assess agreement between a reference standard partition that purports to represent the true cluster structure of the objects (golden standard), and a trial partition produced by the method that is being evaluated (Wallace 1983; Halkidi, Batiskis and Vazirgiannis 2002; Jain 2010). High agreement between the two partitions may indicate good recovery of the true cluster structure. Agreement between partitions can be assessed with so-called external validity indices (Albatineh, Niewiadomska-Bugaj and Mihalko 2006; Brun et al. 2007; Warrens 2008a,2008b; Pfitzner et al. 2009). External validity indices can be roughly categorized into three approaches, namely 1) counting object pairs, 2) information theory (Vinh, Epps and Bailey 2010; Lei et al. 2016), and 3) matching sets (Rezaei and Fränti 2016). Most external validity indices are of the pair-counting approach, which is based on counting pairs of objects placed in identical and different clusters.

Stochastic computation graphs (SCGs) provide a formalism to represent structured optimization problems arising in artificial intelligence, including supervised, unsupervised, and reinforcement learning. Previous work has shown that an unbiased estimator of the gradient of the expected loss of SCGs can be derived from a single principle. However, this estimator often has high variance and requires a full model evaluation per data point, making this algorithm costly in large graphs. In this work, we address these problems by generalizing concepts from the reinforcement learning literature. We introduce the concepts of value functions, baselines and critics for arbitrary SCGs, and show how to use them to derive lower-variance gradient estimates from partial model evaluations, paving the way towards general and efficient credit assignment for gradient-based optimization. In doing so, we demonstrate how our results unify recent advances in the probabilistic inference and reinforcement learning literature.