Just like other Asian countries, Vietnam in a bid to compete with its Asian counterparts is now spurring economic innovation with AI. To emerge as an AI leader in Southeast Asia, and become a pioneer in AI products, the country has adopted a distinctive breakthrough strategy and drew up a plan on AI research and development. According to the Boston Global Forum, Vietnam starts at a low point for AI, and faces a difficult environment and challenging conditions, minimal investment, weak technology sector and lack of human resources. However, through its breakthrough strategy, the country has built a vibrant ecosystem with a talent pool and is also attracting intellectual elites from around the globe.

WASHINGTON - China's desire to dominate new wireless technology poses a global threat that should be thwarted by a new, secure network, according to a former Trump administration official whose call for an enlarged U.S. government role caused an uproar last year. China will gain a capability for mayhem and mass surveillance if it dominates advanced 5G networks that link billions of devices, retired Air Force Brig. Gen. Robert Spalding said in a memo that was obtained by Bloomberg News. "The more connected we are, and 5G will make us the most connected by far, the more vulnerable we become," Spalding, who left the National Security Council last year, said in the memo. Spalding, who is now retired, confirmed in an interview Friday his authorship of the memo.

A.I. is this year's buzzword of choice across the Tech industry, and speculation about what this field can achieve is already running rife. Let's separate fact from fiction and make some sense of all the hype. As we start the new year, the Tech propaganda machine is already ramping up its next generation of buzzwords, promising paradigm shifts and silver bullets that will make whole industries obsolete, enable huge efficiency gains, and make the world a better place. Blockchain, which used to top keyword search trends and social media posts, suffered a significant decline in interest, partly due to the fact that its initial hype was residual from the Bitcoin bubble. It seems that this year's buzzword of choice is going to be Artificial Intelligence.

Artificial intelligence created by Intel is to be used in cameras to detect poachers entering wildlife reserves and alert park rangers before they can kill endangered animals. The technology firm has announced its software is to be used in TrailGuard AI cameras that are capable of object detection and image classification remotely, and which can alert rangers should a person or vehicle be detected. The cameras are to be distributed around wildlife reserves by non-profit organisation Resolve, and have been built in partnership with the National Geographic Society and the Leonardo DiCaprio Foundation. They will be deployed in African wildlife reserves and throughout Southeast Asia in early 2019, the technology firm said. The pencil-sized devices contain a long-life battery, which can last up to a year and a half without needing to be charged.

It is predicted that, by 2025, robots and machines driven by artificial intelligence (AI) will perform half of all productive functions in the workplace – companies already use robots across many industries, but the sheer scale is likely to prompt some new moral and legal questions. Machines currently have no protected legal rights but, as they become more intelligent and act more like humans, will the legal standards at play need to change? To answer this question, we need to take a good hard look at the nature of robotics and our own system of ethics, tackling a situation unlike anything the human race has ever known. The state of robotics at the moment is so comparatively underdeveloped that most of these questions will just be hypotheticals that will be nearly impossible to answer. Can, and should, robots be compensated for their work, and could they be represented by unions (and, if so, could a human union truly stand up for robot working rights, or would there always be an inherent tension)?

The CES trade show is powering up again in Vegas. Most of the biggest names in tech and stacks of start-ups you've never heard of will compete for attention over the next week. Some products may launch new categories - past events presented a first look at video cassette recorders (VCRs), organic light-emitting diode (OLED) TVs and Android tablets. But many more will flop or never even make it to market. We've scoured the internet for hints about what will be on show... One of the biggest developments at the last few CES expos has been Amazon Alexa and Google Assistant's rival efforts to extend their reach in the home and beyond.

When the Apollo 11 spacecraft was orbiting the Moon prior to the first lunar landing, Nasa officials told the astronauts on board to look out for the'lovely girl with a big rabbit'. They were jokingly referring to a story from Chinese mythology in which the goddess Chang'e escapes Earth to live on the Moon with her pet, Jade Rabbit. This week, almost 50 years on from that'giant leap for mankind', the legend of Chang'e resurfaced -- and this time the joke is on the Americans as China announced it had became the first nation to land a spacecraft on the'dark side of the moon'. The robotic probe was named Chang'e 4, a product of China's £3.9 billion a year space exploration project. This week, almost 50 years on from that'giant leap for mankind', the legend of Chang'e resurfaced -- and this time the joke is on the Americans as China announced it had became the first nation to land a spacecraft on the'dark side of the moon' If ever there was a metaphor for the Communist super-power's obsessive secrecy and soaring global ambition, then this audacious secret mission provides it.

In this study, a novel control algorithm for a P-300 based brain-computer interface is fully developed to control a 2-DoF robotic arm. Eight subjects including 5 men and 3 women, perform a 2-dimensional target tracking task in a simulated environment. Their EEG signals from visual cortex are recorded and P-300 components are extracted and evaluated to perform a real-time BCI based controller. The volunteer's intention is recognized and will be decoded as an appropriate command to control the cursor. The final goal of the system is to control a simulated robotic arm in a 2-dimensional space for writing some English letters. The results show that the system allows the robot end-effector to move between arbitrary positions in a point-to-point session with the desired accuracy. This model is tested on and compared with Dataset II of the BCI Competition. The best result is obtained with a multi-class SVM solution as the classifier, with a recognition rate of 97 percent, without pre-channel selection.

Various applications in different fields, such as gene expression analysis or computer vision, suffer from data sets with high-dimensional low-sample-size (HDLSS), which has posed significant challenges for standard statistical and modern machine learning methods. In this paper, we propose a novel linear binary classifier, denoted by population-guided large margin classifier (PGLMC), which is applicable to any sorts of data, including HDLSS. PGLMC is conceived with a projecting direction w given by the comprehensive consideration of local structural information of the hyperplane and the statistics of the training samples. Our proposed model has several advantages compared to those widely used approaches. First, it is not sensitive to the intercept term b. Second, it operates well with imbalanced data. Third, it is relatively simple to be implemented based on Quadratic Programming. Fourth, it is robust to the model specification for various real applications. The theoretical properties of PGLMC are proven. We conduct a series of evaluations on two simulated and six real-world benchmark data sets, including DNA classification, digit recognition, medical image analysis, and face recognition. PGLMC outperforms the state-of-the-art classification methods in most cases, or at least obtains comparable results.

Accurate annotation of medical image is the crucial step for image AI clinical application. However, annotating medical image will incur a great deal of annotation effort and expense due to its high complexity and needing experienced doctors. To alleviate annotation cost, some active learning methods are proposed. But such methods just cut the number of annotation candidates and do not study how many efforts the doctor will exactly take, which is not enough since even annotating a small amount of medical data will take a lot of time for the doctor. In this paper, we propose a new criterion to evaluate efforts of doctors annotating medical image. First, by coming active learning and U-shape network, we employ a suggestive annotation strategy to choose the most effective annotation candidates. Then we exploit a fine annotation platform to alleviate annotating efforts on each candidate and first utilize a new criterion to quantitatively calculate the efforts taken by doctors. In our work, we take MR brain tissue segmentation as an example to evaluate the proposed method. Extensive experiments on the well-known IBSR18 dataset and MRBrainS18 Challenge dataset show that, using proposed strategy, state-of-the-art segmentation performance can be achieved by using only 60% annotation candidates and annotation efforts can be alleviated by at least 44%, 44%, 47% on CSF, GM, WM separately.