The Chinese government, who recently announced they want to build AI based cruise missiles and a new nationwide Social Credit Scoring system, has approved a plan to create a next generation artificial intelligence (AI) national laboratory network which is expected to help China close the gap with its Western counterparts, many of whom now seem to be driving at full speed towards a world where artificial intelligence is the norm, not the exception. The National Development and Reform Commission (NDRC) approved plans for a national engineering lab to support the research and development of deep learning technologies last week, but in a twist the lab will be online only, and won't have a physical presence. The NDRC commissioned Baidu, the Chinese search engine giant, to create the lab in collaboration with Tsinghua University and Peking University, as well as the China Academy of Information and Communications Technology, and the China Electronics Standardization Institute. The project will be led by Baidu's deep learning institute chief Lin Yuanqing and scientist Xu Wei, along with academics from the Chinese Academy of Sciences, Zhang Bo and Li Wei. Baidu, who by all accounts are rapidly becoming the Google of China, certainly with respect to AI, will also provide the deep learning computing, algorithms and big data for the project. The lab will focus on seven different areas of the field: deep learning, computer vision and sensing, computer listening, biometric identification, new forms of human-computer interaction, standardized services, and deep learning intellectual property rights.

At a media conference held in China, Xiaomi CEO Lei Jun hinted that artificial intelligence (AI) will soon become mainstream and will be a standard inclusion in consumer products in the future. The conference with Lei Jun was first reported by MyDrivers which reveals the company's push for artificial intelligence. Jun explained how the future of technology companies is AI. The CEO explained that a number of major breakthroughs have been made and thanks to this, Xiaomi has in store a product line up for the near future that is based on artificial intelligence. Also confirmed at the conference, was the company's AI laboratory.

Artificial intelligence experts point to looming danger amid unpredictable technology and fears that technology could'seduce us into warfare' Experts in artificial intelligence, lawyers and activists organized by the Campaign to Stop Killer Robots gathered at the United Nations on Tuesday to warn against a growing reliance on cheap drones and "stupid AI" that can be unpredictable in the real world. "Terminator always comes up," Toby Walsh, a professor of artificial intelligence at the University of New South Wales, told reporters on Tuesday, referring to the sci-fi cyborg on a mission to wipe out mankind. "But it's not really Terminator that we're worried about at the moment. I think that Terminator is perhaps 50 or so years away." But there are concerning technologies "only a few years, at best, away", Walsh said, and with semi-autonomous systems, such as drones, "it would take very little to remove the human from that loop and replace them with a computer".

Competitive advantage in business today means having the computing power to analyze oceans of data on the back end while providing a seamless customer experience up front. Artificial intelligence, machine learning and cloud computing are the new tools of the trade, and companies everywhere are striving to adopt them--with varying degrees of success. Wall Street Journal reporter Steven Norton discussed the challenges with Dheeraj Pandey, co-founder of Nutanix, a cloud-based platform that helps companies transform their IT infrastructure, and Lise Buyer, a partner at the IPO consultancy Class V Group. Edited excerpts of their conversation follow. NORTON: What are some common obstacles to getting companies to transform their IT architecture to artificial intelligence, machine learning and the cloud?

Imaging and understanding the brain is getting so good we are on the cusp of truly enhancing it. This article originally appeared on SOSV.) As I start to look at the emerging trends of 2017 from the vantage of IndieBio, where we see hundreds of biotech startup applications and technologies per year, a few key themes are already emerging. Even as political landscapes change, science and technology continue to push forward. Most of us have seen science fiction shows that show future doctors regrowing and replacing entire organs.

IBM's Watson may be most famous for winning at the game show "Jeopardy!" In a room at IBM offices, software developers and business customers can query the famous computer and see a demonstration of its work as a research partner in fields ranging from land use to medicine. The room itself has a display wall on one side and a touch screen in the center and near the window. In a recent demonstration of how the machine approaches search queries, Rachel Liddell, a "Watson Experience Leader," used the central touch screen to search through a series of TED talks. As she touched the screen to look up lectures on human psychology, Watson created a set of associated topics, such as "education," and touching one of those words generated more specific topics that appeared in the talk.

We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations---random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning. For flexibility, Edward makes it easy to fit the same model using a variety of composable inference methods, ranging from point estimation to variational inference to MCMC. In addition, Edward can reuse the modeling representation as part of inference, facilitating the design of rich variational models and generative adversarial networks. For efficiency, Edward is integrated into TensorFlow, providing significant speedups over existing probabilistic systems. For example, we show on a benchmark logistic regression task that Edward is at least 35x faster than Stan and 6x faster than PyMC3. Further, Edward incurs no runtime overhead: it is as fast as handwritten TensorFlow.

We propose the spatiotemporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride-hailing service provider in China. In the current study, 199,584 time-slots describing the spatiotemporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE 16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).

This paper presents a novel method for structural data recognition using a large number of graph models. In general, prevalent methods for structural data recognition have two shortcomings: 1) Only a single model is used to capture structural variation. 2) Naive recognition methods are used, such as the nearest neighbor method. In this paper, we propose strengthening the recognition performance of these models as well as their ability to capture structural variation. The proposed method constructs a large number of graph models and trains decision trees using the models. This paper makes two main contributions. The first is a novel graph model that can quickly perform calculations, which allows us to construct several models in a feasible amount of time. The second contribution is a novel approach to structural data recognition: graph model boosting. Comprehensive structural variations can be captured with a large number of graph models constructed in a boosting framework, and a sophisticated classifier can be formed by aggregating the decision trees. Consequently, we can carry out structural data recognition with powerful recognition capability in the face of comprehensive structural variation. The experiments shows that the proposed method achieves impressive results and outperforms existing methods on datasets of IAM graph database repository.

Microsoft is showcasing its latest innovations and e-government solutions at the fourth Qatar ICT Conference and Exhibition (Qitcom), which opened yesterday at the Qatar National Convention Centre. The three-day event, patronised by HH the Emir Sheikh Tamim bin Hamad al-Thani and HE the Prime Minister and Interior Minister Sheikh Abdullah bin Nasser bin Khalifa al-Thani, is organised by Qatar's Ministry of Transport and Communications (MoTC). Microsoft is demonstrating a range of solutions, covering artificial intelligence, mixed reality, the Internet of Things, machine learning, cyber-security, the cloud and big data, aimed at empowering Qatari organisations – regardless of size or industry – to achieve more through digital transformation. "Microsoft has a strong relationship with MoTC and we stand firmly behind the ministry and the government of Qatar as they work towards the Qatar National Vision 2030, launched by HH the Father Emir Sheikh Hamad bin Khalifa al-Thani," said Lana Khalaf, public sector director and acting country manager, Microsoft Qatar. The company, in conjunction with its strategic partners, crafted nine pilot solutions customised for Qatar and the challenges the nation faces as it builds a smart society.