China's science and technology minister said on Saturday that the government had made an open-source platform to boost the development of artificial intelligence (AI), as part of a plan to make China a world-leader in this field by 2030. He said that offering AI on open-source platforms would help with its scientific development and help it rapidly expand, allowing the creation of a new generation of AI. "Open-source platforms are needed because AI can play a bigger role in development and make it easier for entrepreneurs to have access to resources," Wan Gang said in a press conference to mark a session of the National People's Assembly. The minister said that the technology had been an area of development in China since 1980, and now the country wanted to lead the sector through the national plan – started by President Xi Jinping – which involves multiple ministries in order to apply the technology in all fields. He also highlighted the achievements made in the area in the last few years, such as the development of intelligent vehicles, facial recognition at airports and train stations or some companies developing robots that improve people's' quality of life. Wan said promotion of scientific research and development has been a key Chinese policy, with increased state investment and incentives.

On Tuesday, a privacy and security report published by Gizmodo revealed that Google and the Pentagon are collaborating on developing drones. Known as Project Maven, the Department of Defense pilot project involves analyzing, combing through, defining, and categorizing visual data amassed by aerial drones. It wouldn't be too far off to say the project would function as the Pentagon's all-seeing eye. According to Greg Allen, a Center for a New American Society adjunct fellow, the current amount of obtained footage is so vast it isn't possible for human analysts at the defense agency to sift through it and correctly define objects in the footage. As it stands, the United States' drone strike program is already criticized by human rights groups like Reprieve for reportedly killing hundreds of civilians in Pakistan, Afghanistan, Yemen, and beyond in spite of claims of "surgical" precision from former CIA director John Brennan in 2011.

A program called Project Maven is utilising the technology to automate the analysis of objects in the enormous amount of images that are captured by the Department of Defence's surveillance drones - also known as unmanned aerial vehicles (UAVs). Gizmodo reported that some Google employees were "outraged that the company would offer resources to the military for surveillance technology involved in drone operations". There have been almost 30,000 coalition strikes against targets in Iraq and Syria since the US-led intervention in 2014, the intelligence behind many of which is developed by analysis of UAV surveillance footage. Google confirmed that software called TensorFlow was being used by the Pentagon for a pilot and said it had "long worked with government agencies to provide technology solutions". A Google spokesperson said: "The technology flags images for human review, and is for non-offensive uses only.

Mutex groups are defined in the context of STRIPS planning as sets of facts out of which, maximally, one can be true in any state reachable from the initial state. The importance of computing and exploiting mutex groups was repeatedly pointed out in many studies. However, the theoretical analysis of mutex groups is sparse in current literature. This work provides a complexity analysis showing that inference of mutex groups is as hard as planning itself (PSPACE-Complete) and it also shows a tight relationship between mutex groups and graph cliques. This result motivates us to propose a new type of mutex group called a fact-alternating mutex group (fam-group) of which inference is NP-Complete. Moreover, we introduce an algorithm for the inference of fam-groups based on integer linear programming that is complete with respect to the maximal fam-groups and we demonstrate how beneficial fam-groups can be in the translation of planning tasks into finite domain representation. Finally, we show that fam-groups can be used for the detection of dead-end states and we propose a simple algorithm for the pruning of operators and facts as a preprocessing step that takes advantage of the properties of fam-groups. The experimental evaluation of the pruning algorithm shows a substantial increase in a number of solved tasks in domains from the optimal deterministic track of the last two planning competitions (IPC 2011 and 2014).

In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). However, practical IDSs generally update their decision module by feeding new data then retraining learning models in a periodical way. Hence, some attacks that comprise the data for training or testing classifiers significantly challenge the detecting capability of machine learning-based IDSs. Poisoning attack, which is one of the most recognized security threats towards machine learning-based IDSs, injects some adversarial samples into the training phase, inducing data drifting of training data and a significant performance decrease of target IDSs over testing data. In this paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel poisoning method that attack against several machine learning algorithms used in IDSs. Specifically, we propose a boundary pattern detection algorithm to efficiently generate the points that are near to abnormal data but considered to be normal ones by current classifiers. Then, we introduce a Batch-EPD Boundary Pattern (BEBP) detection algorithm to overcome the limitation of the number of edge pattern points generated by EPD and to obtain more useful adversarial samples. Based on BEBP, we further present a moderate but effective poisoning method called chronic poisoning attack. Extensive experiments on synthetic and three real network data sets demonstrate the performance of the proposed poisoning method against several well-known machine learning algorithms and a practical intrusion detection method named FMIFS-LSSVM-IDS.

The analysis of cancer genomic data has long suffered "the curse of dimensionality". Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more effectively analyze cancer genomic data. Most of the methods focus on testing marginal significance of the associations between pathways and clinical phenotypes. They can identify relevant pathways, but do not involve predictive modeling. In this article, we propose a Pathway-based Kernel Boosting (PKB) method for integrating gene pathway information for sample classification, where we use kernel functions calculated from each pathway as base learners and learn the weights through iterative optimization of the classification loss function. We apply PKB and several competing methods to three cancer studies with pathological and clinical information, including tumor grade, stage, tumor sites, and metastasis status. Our results show that PKB outperforms other methods, and identifies pathways relevant to the outcome variables.

China is by far the world's largest consumer of microchips and semiconductor circuits, importing $200 billion worth of these products annually. But the Chinese authorities worry that the country's reliance on import threatens national security and hampers the development of a thriving technology sector. The state-backed China IC Industry Investment Fund, created three years ago in a bid to support domestic chipmakers, is reportedly in talks with government agencies and corporations to raise at least 150 billion yuan for its second fund vehicle and intends to begin deploying capital in the second half of the year. The country envisions spending those funds over 10 years, investing in a wide range of sectors, from processor design and manufacturing to chip testing and packaging, in hopes to achieve a leading position in semiconductor industry. Internet technology expert Liu Xingliang believes China has a chance to reach the goal.

Imagine bidding on a masterpiece by Leonardo da Vinci via a tap of your fingertips while lying in bed. A decade ago, this scenario would have sounded crazy to a lot of people, but there are plenty of tech companies keen to make it happen. The art market is famously resistant to change, but even established art businesses are feeling the force of new technologies. High rents in art market centres are one reason, as mid-range galleries are increasingly interested in selling online. There is also the need to cater to the next generation of collectors, many of whom are more comfortable browsing a desired collectible on their smartphone rather than walking in to a gallery.

In 2015, 1.7 million voice-first devices were shipped across the U.S. But the number soon rose up to 6.5 million in 2016. The increase in the trend captured the growing demand for voice-search in the coming years. Voice-search technology has existed for many years but its evolution has just begun. From automated voice recognition phone system to simplified voice to text dictaphones, voice technology was adopted in different forms all across the globe.

For years, we have been warned that the day will come when machines will be able to do our jobs better than we can. Now a leading Chinese economist is offering a time frame. Cai Fang, vice-president of the Chinese Academy of Social Sciences, the country's top think tank, and former head of its Population and Labour Economic Research Institute, robots will "definitely" surpass humans in many job skills in 10 to 20 years. Like Microsoft founder Bill Gates and other technology titans, Cai is an advocate of tax policies and other measures to keep robots from putting human workers out of jobs. In February, Gates said governments should levy a tax on the use of robots to fund retraining of those who lose their jobs and to slow down automation.