China's commerce ministry said on Thursday that the nation has good potential in cooperation with Germany in sectors such as digitalization, new energy cars, artificial intelligence and driverless cars. The comments were made by ministry spokesman Gao Feng at a regular briefing in Beijing.

Alibaba Group, SenseTime, and the Hong Kong Science and Technology Parks Corp. (HKSTP) have partnered to create a new accelerator program to support Hong Kong's AI startups, Alizila reports. The HKAI Lab program will select startups to provide with up to $100,000 in funding from the Alibaba Entrepreneurs Fund, as well as access to deep-learning resources from SenseTime, and cloud and other technologies from Alababa and its DAMO Academy. The 6-month program will accept about 10 startups into each of two yearly cohorts, also providing them with office space, mentorship, and networking opportunities. "Alibaba sees AI as a fundamental technology that will make a difference to society," said Joe Tsai, executive vice chairman of Alibaba Group. "We envision the Hong Kong AI Lab to be an open platform where researchers, start-ups and industry participants can collaborate and build a culture of innovation."

Some schools in China have incorporated paper-grading artificial intelligence into their classrooms, according to the South China Morning Post. One in every four schools, or about 60,000 institutions, are quietly testing a machine learning-powered system that can score students' work automatically, and even offer suggestions where appropriate. The AI, which can be accessed through various online portals, and which the report describes as similar to the system used by the Education Testing Service in the U.S., uses an evolving "knowledge base" to interpret the "general logic" and "meaning" of pupils' essays and to highlight stylistic, structural, and thematic areas that need improvement. It can read both English and Chinese, and it's reportedly perceptive enough to notice when paragraphs veer too far off topic. The 10-year-old grading software leverages deep learning algorithms to "compare notes" with human teachers' scores, suggestions, and comments.

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Lt. Col. Stanislav Petrov settled into the commander's chair in a secret bunker outside Moscow. His job that night was simple: Monitor the computers that were sifting through satellite data, watching the United States for any sign of a missile launch. It was just after midnight, Sept. 26, 1983. A single word flashed on the screen in front of him. The fear that computers, by mistake or malice, might lead humanity to the brink of nuclear annihilation has haunted imaginations since the earliest days of the Cold War.

In this work, we present an application of domain randomization and generative adversarial networks (GAN) to train a near real-time object detector for industrial electric parts, entirely in a simulated environment. Large scale availability of labelled real world data is typically rare and difficult to obtain in many industrial settings. As such here, only a few hundred of unlabelled real images are used to train a Cyclic-GAN network, in combination with various degree of domain randomization procedures. We demonstrate that this enables robust translation of synthetic images to the real world domain. We show that a combination of the original synthetic (simulation) and GAN translated images, when used for training a Mask-RCNN object detection network achieves greater than 0.95 mean average precision in detecting and classifying a collection of industrial electric parts. We evaluate the performance across different combinations of training data.

Semi-supervised learning on graph structured data has received significant attention with the recent introduction of graph convolution networks (GCN). While traditional methods have focused on optimizing a loss augmented with Laplacian regularization framework, GCNs perform an implicit Laplacian type regularization to capture local graph structure. In this work, we propose Lovasz convolutional network (LCNs) which are capable of incorporating global graph properties. LCNs achieve this by utilizing Lovasz's orthonormal embeddings of the nodes. We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs. We validate the proposed method on standard random graph models such as stochastic block models (SBM) and certain community structure based graphs where LCNs outperform GCNs and learn more intuitive embeddings. We also perform extensive binary and multi-class classification experiments on real world datasets to demonstrate LCN's effectiveness. In addition to simple graphs, we also demonstrate the use of LCNs on hypergraphs by identifying settings where they are expected to work better than GCNs.

Using movement primitive libraries is an effective means to enable robots to solve more complex tasks. In order to build these movement libraries, current algorithms require a prior segmentation of the demonstration trajectories. A promising approach is to model the trajectory as being generated by a set of Switching Linear Dynamical Systems and inferring a meaningful segmentation by inspecting the transition points characterized by the switching dynamics. With respect to the learning, a nonparametric Bayesian approach is employed utilizing a Gibbs sampler.

Intense volatility in financial markets affect humans worldwide. Therefore, relatively accurate prediction of volatility is critical. We suggest that massive data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. First we select 28 key words, which are related to finance as indicators of the public mood and macroeconomic factors. Then those 28 words of the daily search volume based on Baidu index are collected manually, from June 1, 2006 to October 29, 2017. We apply a Long Short-Term Memory neural network to forecast CSI300 volatility using those search volume data. Compared to the benchmark GARCH model, our forecast is more accurate, which demonstrates the effectiveness of the LSTM neural network in volatility forecasting.

Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function (RBF) neural network, general regression neural network (GRNN), support vector machine regression (SVMR), least squares support vector machine regresssion (LS-SVMR). We apply the five models to make price prediction of three individual stocks, namely, Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and average absolute percentage error as criteria, we find BP neural network consistently and robustly outperforms the other four models.