China has approved a plan to create a next-generation national laboratory for deep learning. The lab is expected to help China close the gap with Western counterparts in the field of competitive artificial intelligence applications. The National Development and Reform Commission (NDRC) approved plans for a national engineering lab to support the research and development of deep learning technologies. The lab will be online only, without a physical presence. The NDRC commissioned Baidu, the Chinese search engine giant, to create the lab in collaboration with Tsinghua and Beijing Universities, 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.

Ben-Gurion University of the Negev and the Israel Police aim to develop advanced cyber, big-data and artificial intelligence tools that may eventually be able to predict and prevent crime. In a joint initiative with the police, the university launched the Center for Computational Criminology this week at BGU's Advanced Technologies Park in the presence of Police Commissioner Insp.-Gen.

Visual events are usually accompanied by sounds in our daily lives. We pose the question: Can the machine learn the correspondence between visual scene and the sound, and localize the sound source only by observing sound and visual scene pairs like human? In this paper, we propose a novel unsupervised algorithm to address the problem of localizing the sound source in visual scenes. A two-stream network structure which handles each modality, with attention mechanism is developed for sound source localization. Moreover, although our network is formulated within the unsupervised learning framework, it can be extended to a unified architecture with a simple modification for the supervised and semi-supervised learning settings as well. Meanwhile, a new sound source dataset is developed for performance evaluation. Our empirical evaluation shows that the unsupervised method eventually go through false conclusion in some cases. We show that even with a few supervision, false conclusion is able to be corrected and the source of sound in a visual scene can be localized effectively.

Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise, and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.

Gated Recurrent Unit (GRU) is a recently-developed variation of the long short-term memory (LSTM) unit, both of which are types of recurrent neural network (RNN). Through empirical evidence, both models have been proven to be effective in a wide variety of machine learning tasks such as natural language processing (Wen et al., 2015), speech recognition (Chorowski et al., 2015), and text classification (Yang et al., 2016). Conventionally, like most neural networks, both of the aforementioned RNN variants employ the Softmax function as its final output layer for its prediction, and the cross-entropy function for computing its loss. In this paper, we present an amendment to this norm by introducing linear support vector machine (SVM) as the replacement for Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. While there have been similar studies (Alalshekmubarak & Smith, 2013; Tang, 2013), this proposal is primarily intended for binary classification on intrusion detection using the 2013 network traffic data from the honeypot systems of Kyoto University. Results show that the GRU-SVM model performs relatively higher than the conventional GRU-Softmax model. The proposed model reached a training accuracy of ~81.54% and a testing accuracy of ~84.15%, while the latter was able to reach a training accuracy of ~63.07% and a testing accuracy of ~70.75%. In addition, the juxtaposition of these two final output layers indicate that the SVM would outperform Softmax in prediction time - a theoretical implication which was supported by the actual training and testing time in the study.

Tech giants Apple (AAPL), Alphabet (GOOGL), Facebook (FB), and Microsoft (MSFT) have raced to apply artificial intelligence to their businesses, and the oil industry is starting to seize on AI's benefits too. The reason interest is surging now is because artificial intelligence is "actually doable," he said in an interview with IBD at CERAWeek, explaining that advancements in cloud computing and infrastructure have made AI more affordable and accessible. "The industrial world is waking up to best practices," he said. "They are all waking up to it." Several heavyweights in the energy industry are already investors in his company, including General Electric (GE), Chevron (CVX), Royal Dutch Shell (RDSA) and Saudi Aramco.

The autonomous vehicle division of Google's parent company will start hauling cargo using self-driving trucks, capping a busy week for next-generation shipping technology. Waymo, the driverless vehicle unit of Alphabet, announced a pilot programme that will have self-driving big rigs transport cargo to the company's data centres in Georgia. Several companies are vying to dominate the nascent self-driving vehicle industry, believing the technology will reshape how humans and goods travel. Waymo has already extensively tested autonomous cars intended to ferry people around. "Now we're turning our attention to things as well", the company said in a blog post, noting that driverless trucks pose unique tech challenges.

In 2017, investments in startups riding the wave of AI across the world leapt up by 141% in a year, amounting to 15.2 billion dollars. Some 1,100 new startups came into existence. The fourth industrial revolution is very much underway and has sent shockwaves through all sectors, from agriculture to cybersecurity as well as business and even healthcare. Some observers even believe that the economic growth of a country soon be assessed, not by its capital but by its level of maturity in terms of AI. These predictions will no doubt delight some and terrify others.

Scientists are recruiting members of the public to help clean up the shores of Great Britain. Plastic waste is scattered across the beaches of the UK and a computer programme is being developed to help spot the litter. The charity campaign needs human volunteers to help train an artificial intelligence algorithm that will automatically spot plastic in pictures taken by drones. Plastic Tide, the charity behind the project, hopes to harness cutting edge drone and algorithm technology to create an open source map of the plastic pollution problem. Peter Kohler, founder and director of The Plastic Tide, said: 'Marine creatures die each year through starvation due to eating plastic that stays in their stomach, making them feel full.

– I wish you all a very happy New Year 2017. Payments especially Digital-Payments look really easy; which is why innovation is so hard. In last few years we have seen tsunami kind of disruption in payments services, which led eCommerce, wallet services, Digital-Payments, and remittances to just explode. Artificial intelligence with its subsets like Machine Learning, Deep Learning and Artificial Neural Networks made this industry almost to an explosion point. Artificial Intelligence and any discussion around on how this has gotten to so much deeper in Fintech and what benefits it has provided; unfortunately beside Digital-Payments discussion rest of the points will remain out of scope for this post and we will discuss about AI and its merger with FinTech in other posts in later year.