The present global verve about artificial intelligence (AI) and machine learning technologies has resonated in China as much as anywhere on earth. With the State Council's issuance of the "New Generation Artificial Intelligence Development Plan" (新一代人工智能发展规划) on July 20, China's government set out an ambitious roadmap including targets through 2030. Meanwhile, in China's leading cities, flashy conferences on AI have become commonplace. It seems every mid-sized tech company wants to show off its self-driving car efforts, while numerous financial tech start-ups tout an AI-driven approach. Chatbot startups clog investors' date books, and Shanghai metro ads pitch AI-taught English language learning.

We consider the task of weakly supervised one-shot detection. In this task, we attempt to perform a detection task over a set of unseen classes, when training only using weak binary labels that indicate the existence of a class instance in a given example. The model is conditioned on a single exemplar of an unseen class and a target example that may or may not contain an instance of the same class as the exemplar. A similarity map is computed by using a Siamese neural network to map the exemplar and regions of the target example to a latent representation space and then computing cosine similarity scores between representations. An attention mechanism weights different regions in the target example, and enables learning of the one-shot detection task using the weaker labels alone. The model can be applied to detection tasks from different domains, including computer vision object detection. We evaluate our attention Siamese networks on a one-shot detection task from the audio domain, where it detects audio keywords in spoken utterances. Our model considerably outperforms a baseline approach and yields a 42.6% average precision for detection across 10 unseen classes. Moreover, architectural developments from computer vision object detection models such as a region proposal network can be incorporated into the model architecture, and results show that performance is expected to improve by doing so.

We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies.The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of processed CMB maps that boost the cosmic string detectability. Our proposed classifiers, after training, give results improved over or similar to the claimed detectability levels of the existing methods for string tension, $G\mu$. They can make $3\sigma$ detection of strings with $G\mu \gtrsim 2.1\times 10^{-10}$ for noise-free, $0.9'$-resolution CMB observations. The minimum detectable tension increases to $G\mu \gtrsim 3.0\times 10^{-8}$ for a more realistic, CMB S4-like (II) strategy, still a significant improvement over the previous results.

Drones used to attack two Russian military bases in Syria were so high-tech they were designed to offset jamming technology, were capable of launching precision strikes and could not have been made without foreign assistance, the defence ministry in Moscow has said . The ministry's drone department head Gen Alexander Novikov said the drones used in the weekend's raids differed from the rudimentary craft earlier used by rebels in Syria. The attacks required satellite navigation data that are not available on the internet, complex engineering works and elaborate tests, Gen Novikov said. Analysts say the drones present the biggest military challenge so far to Russia's role in Syria'The creation of drones of such class is impossible in makeshift conditions,' Novikov said. 'Their development and use requires the involvement of experts with special training in the countries that manufacture and use drones.' The ministry said Saturday's raid on the Hemeimeem air base in the province of Lattakia and Russia's naval facility in the port of Tartus involved 13 drones.

In science news around the world, the U.S. National Football League provides $16 million for medical research on concussions and other football-related illnesses, and the World Health Organization approves a new, long-lasting vaccine for typhoid fever. China announces it will build a new research and development park in Beijing to develop artificial intelligence technologies, and South Korean universities refuse to renew their contracts with Elsevier for access to its ScienceDirect database because of a price hike. Scientists install new devices at the South Pole to measure neutrinos, and a volunteer discovers the largest prime number, containing more than 23 million digits. And the U.S. Department of the Interior decides to review certain grants to universities and nonprofit groups to ensure they align with the priorities of President Donald Trump's administration.

A plush, robotic duck may soon become a fixture in the world of children who have cancer. The social robot is modeled after the duck mascot for the insurance company, Aflac, which paid for its development. The My Special Aflac Duck for children facing cancer is seen on display during the CES Unveiled preview event at the Mandalay Bay Convention Center during CES 2018 in Las Vegas. One of the highlights of attending the Consumer Electronics Show in Las Vegas are the robots. But one robot companion stands out as the most heartwarming.

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TOKYO - Sony Corp. on Thursday released its new artificial-intelligence equipped Aibo robot dog in Japan, delivering it to buyers who placed preorders last year. The electronics giant said the new Aibo -- an upgrade on its predecessor discontinued in 2006 -- sold out in all three rounds of preorders, although the company did not disclose the number of units sold. About 30 people attended a commemorative event at Sony's corporate headquarters in Tokyo to receive their Aibos, with some bringing along older models. A woman in her 40s with her son said she was "thrilled" to welcome the Aibo as a "new member of the family." A man in his 50s said he bought the robot dog for his wife to celebrate their 25th wedding anniversary.

All new technologies bring with them new and sometimes unique security concerns. And while some of these concerns can be overblown by security vendors (and, let's face it, tech writers), a couple of recent news items regarding artificial intelligence (AI) raise some genuinely frightening prospects for enterprise IT security pros and enterprise employees. Item No. 1: AI soon may get crazy good at conversing with humans. Researchers at Osaka University have developed a method of dialogue that enables chatbots, voice assistants and other AI-powered platforms to learn information from humans that isn't already in their knowledge base. Right now voice assistants such as Alexa and Google Home, for example, learn from humans "by asking simple repetitive questions," the researchers write.

Sony Corp. on Thursday released its new artificial-intelligence equipped Aibo robot dog in Japan, delivering it to buyers who placed preorders last year. The electronics giant said the new Aibo -- an upgrade on its predecessor discontinued in 2006 -- sold out in all three rounds of preorders, although the company did not disclose the number of units sold. About 30 people attended a commemorative event at Sony's corporate headquarters in Tokyo to receive their Aibos, with some bringing along older models. A woman in her 40s with her son said she was "thrilled" to welcome the Aibo as a "new member of the family." A man in his 50s said he bought the robot dog for his wife to celebrate their 25th wedding anniversary.