The education ministry plans to establish a system that allows students at some 50 high schools to take university courses and earn credits, with the aim of developing human resources in view of expected advances in artificial intelligence technologies. Advanced differential equation, data mining and other subjects in the mathematics and science fields will be covered by the planned version of the advanced placement system, which is used in the United States and other countries, according to ministry officials. The ministry is set to choose at least one high school from each of Japan's 47 prefectures over the next decade or so for the program. The planned initiative was included in a report compiled by a ministry panel discussing education policies for coming generations. Through the system, the ministry hopes to allow highly motivated high school students with excellent academic performances to receive even higher levels of education after they advance to university.

TEMPO.CO, Jakarta - Google has reportedly developed an artificial intelligence (AI) that is able to predict a person's death with an accuracy level of 95 percent, as stated in a journal published by Nature Research and Futurism.com. The records include clinical trial results, readmission, the use of hospital facilities, and patient diagnostics. The Google team reportedly used a medical brain algorithm to construct the AI. The same algorithm is said to be similar to the one used to predict the death of a breast cancer patient, which was considered to be extremely accurate. The main goal of this AI is to be able to utilize it in numerous clinical scenarios to calculate which patient would need the highest priority.

It wasn't clear if Alimama actually conducted a Turing test--in which humans judged whether copy came from a person or AI--or if Alimama means more generally that its AI-generated copy sounds like it was written by a person. Alibaba wouldn't provide any details about how the test was conducted as a company rep declined to comment on the record. In a blog post, Alimama said its AI copywriter uses deep learning and natural language processing to learn from "millions of top-quality existing samples [on ecommerce sites Tmall and Taobao] to generate copy." To use the AI copywriter, brands insert a link to a product page and click the "Produce Smart Copy" button for suggestions. They can adjust the length and tone with options like promotional, functional, fun, poetic or heartwarming.

Background: Early and appropriate empiric antibiotic treatment of patients suspected of having sepsis is associated with reduced mortality. This problem is particularly severe for children in developing country settings. We hypothesized that by applying machine learning approaches to readily collected patient data, it would be possible to obtain actionable and patient-specific predictions for antibiotic-susceptibility. If sufficient discriminatory power can be achieved, such predictions could lead to substantial improvements in the chances of choosing an appropriate antibiotic for empiric therapy, while minimizing the risk of increased selection for resistance due to use of antibiotics usually held in reserve. Methods and Findings: We analyzed blood culture data collected from a 100-bed children's hospital in North-West Cambodia between February 2013 and January 2016. Clinical, demographic and living condition information for each child was captured with 35 independent variables.

Beijing firm Megvii, which is being backed by the Chinese government, is expanding the places in which its powerful Face software is rolled out. The company secured a distributor in Thailand after Chinese police were able to use the software in China to locate and arrest more than 3,000 fugitives. The technology works by identifying 106 different features on people's faces, so even if the face is partially covered, by a balaclava for example, it should still work. Face is already worth an estimated £1.5billion, according to Business Insider, and Megvii are in talks with commercial banks and building managers to deploy it for security purposes.

Traditional information retrieval systems represent documents and queries by keyword sets. However, the content of a document or a query is mainly defined by both keywords and named entities occurring in it. Named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. Besides, the meaning of a query may imply latent named entities that are related to the apparent ones in the query. We propose an ontology-based generalized vector space model to semantic text search. It exploits ontological features of named entities and their latently related ones to reveal the semantics of documents and queries. We also propose a framework to combine different ontologies to take their complementary advantages for semantic annotation and searching.

This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that gives a time-evolving representation of the interdependencies between the components of the sparse signal of interest. A hierarchical Gaussian process describes such structure and the interdependencies are represented via the covariance matrices of the prior distributions. The inference is based on the expectation propagation method and the theoretical derivation of the posterior distribution is provided in the paper. The inference framework is thoroughly evaluated over synthetic, real video and electroencephalography (EEG) data where the spatio-temporal evolving patterns need to be reconstructed with high accuracy. It is shown that it achieves 15% improvement of the F-measure compared with the alternating direction method of multipliers, spatio-temporal sparse Bayesian learning method and one-level Gaussian process model. Additionally, the required memory for the proposed algorithm is less than in the one-level Gaussian process model. This structured sparse regression framework is of broad applicability to source localisation and object detection problems with sparse signals.

Abstract-- Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results. I. INTRODUCTION Even there is still room to improve pedestrian detection and tracking, the state-of-the-art is sufficiently mature [1], [2], [3] as to allow for increasingly focusing more on higher level tasks which are crucial in terms of (assisted or automated) driving safety and comfort. In particular, knowing the intention of a pedestrian to cross the road in front of the ego-vehicle, i.e. before the pedestrian has actually entered the road, would allow the vehicle to warn the driver or automatically perform maneuvers which are smoother and more respectful with pedestrians; it even significantly reduces the chance of injury requiring hospitalization when a vehicleto-pedestrian crash is not fully avoidable [4]. The idea can be illustrated with the support of Figure 1.

Experts said the trade conflict with the US will have no effect on China's development in AI technology, after an annual report released on Friday said China has become the most attractive country for investment and financing in AI technology, which accounts for 60 percent in the world. The report on China AI Development 2018 was released by Tsinghua University on Friday, which said from 2013 to the first quarter of 2018, the amount of investment and financing in AI technology in China accounts for 60 percent in the world, valued at $27 billion in 2017, Chinese news portal thepaper.com AI is an emerging area for investors with substantial "hot" money in the Chinese financial market, Xiang Yang, an industry expert at Beijing-based CCID Consulting, told the Global Times on Friday. "Many emerging industries will enjoy an investment boom at the beginning, but when the boom subsides, investors will be more rational, and survivors will be very qualified. The quality of China's AI will not suffer and will continue to attract more talent to return to China from the US," Wan said.

A Chinese AI system diagnosed brain tumors and other conditions more accurately and faster than a team of top Chinese physicians, according to Xinhua. The AI, called BioMind, was developed by the Artificial Intelligence Research Centre for Neurological Disorders at Beijing Tiantan Hospital. The AI correctly diagnosed 87 percent of 225 cases in just 15 minutes, while a team of 15 senior physicians diagnosed 66-percent of the cases accurately. When it came to predicting cases of brain hematoma expansion, the AI won yet again: with correction predictions in 83 percent of cases, whereas doctors only achieved 63-percent. And that's not because the doctors were slacking.