Big Data is too complex to be skillfully handled by traditional processing techniques and Artificial Intelligence is one key area it has found solace in. While Data will soon replace Gold as the most important asset to mankind, putting it for appropriate use has transformed seeking information. Not to miss, the biggest of impactful technologies be it Internet Of Things, Machine Learning or even the Decentralized Apps, Big Data drives them all. Here's what the trends have it in 2018 for Big Data innovations. Starbucks have deployed Chatbots to take orders in text while MasterCard lets them reply to your transactional queries.

Founded by the former chief executive of electronics manufacturer Flextronics, Katerra handles everything from design to construction to manufacturing of glued wood beams, while building prefabricated walls and fixtures in indoor factories. The Menlo Park, Calif., startup calls itself a "technology company," emphasizing software aimed at making the process more efficient. A main innovation appears to be a business model that puts architects and construction workers on the same payroll. The deal, which values Katerra above $3 billion, marks the latest investment by the Vision Fund in ground rarely tread upon by tech investors. The fund, anchored by a $45 billion commitment from Saudi Arabia, was initially described by SoftBank as a vehicle to invest in technologies of the future such as robotics and artificial intelligence.

Venture capital firm IDG Capital was the third lead investor in the funding, and the funds are slated for both research and production costs, Xiaopeng, also known as Xpeng, said in a release Monday. Two other Chinese internet giants, Baidu Inc. and Tencent Holdings Ltd., have led fundings of at least $2.5 billion into Chinese electric-car companies including NiO and WM Motor Technology. Beyond China, Tencent also invested $1.8 billion for a 5% stake in Tesla Inc. last March. Alibaba, Tencent and Baidu want to see their technology incorporated into cars as autonomous vehicles edge into the market, giving them captive audiences for online gaming, streaming video, internet shopping and social media, said Bill Russo, founder of Shanghai consultancy Automobility Ltd. Alibaba and Foxconn executives said in the release that Xpeng gives the company the ability to tap into the intersection of the internet, artificial intelligence and traditional manufacturing, an area with a lot of promise.

If you've been waiting to hear more on Samsung's next Galaxy flagship, we've got you covered. As well as everything else that happened over the weekend, naturally. Intelligent Scan would work day or night. Samsung has hinted that the Galaxy S9 might include more advanced face recognition, but we're now getting clues of what's involved. Deep inside the Galaxy Note 8's Oreo beta software, there's a hidden Intelligent Scan feature that uses both camera-based face detection and the iris scanner for "better accuracy and security" and improved results in "low or very bright" lighting.

Recently 2017 came to a glittering end and as we look back through the lens of technology, the winner was probably Artificial Intelligence aka AI. It received tremendous success as much as some of the industry leaders commented that 2017 was the'Year of AI'. This write-up is an attempt to collate the achievements under the academic and industry. Starting off with academics, the sheer volume of papers published is increasing every year. To give you some statistics, in 2017 it was 9 times more than 1996.

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.

Grouping objects into clusters based on similarities or weights between them is one of the most important problems in science and engineering. In this work, by extending message passing algorithms and spectral algorithms proposed for unweighted community detection problem, we develop a non-parametric method based on statistical physics, by mapping the problem to Potts model at the critical temperature of spin glass transition and applying belief propagation to solve the marginals corresponding to the Boltzmann distribution. Our algorithm is robust to over-fitting and gives a principled way to determine whether there are significant clusters in the data and how many clusters there are. We apply our method to different clustering tasks and use extensive numerical experiments to illustrate the advantage of our method over existing algorithms. In the community detection problem in weighted and directed networks, we show that our algorithm significantly outperforms existing algorithms. In the clustering problem when the data was generated by mixture models in the sparse regime we show that our method works to the theoretical limit of detectability and gives accuracy very close to that of the optimal Bayesian inference. In the semi-supervised clustering problem, our method only needs several labels to work perfectly in classic datasets. Finally, we further develop Thouless-Anderson-Palmer equations which reduce heavily the computation complexity in dense-networks but gives almost the same performance as belief propagation.

Crisis responders are increasingly using social media, data and other digital sources of information to build a situational understanding of a crisis situation in order to design an effective response. However with the increased availability of such data, the challenge of identifying relevant information from it also increases. This paper presents a successful automatic approach to handling this problem. Messages are filtered for informativeness based on a definition of the concept drawn from prior research and crisis response experts. Informative messages are tagged for actionable data -- for example, people in need, threats to rescue efforts, changes in environment, and so on. In all, eight categories of actionability are identified. The two components -- informativeness and actionability classification -- are packaged together as an openly-available tool called Emina (Emergent Informativeness and Actionability).

The vanishing ideal is a set of polynomials that takes zero value on the given data points. Originally proposed in computer algebra, the vanishing ideal has been recently exploited for extracting the nonlinear structures of data in many applications. To avoid overfitting to noisy data, the polynomials are often designed to approximately rather than exactly equal zero on the designated data. Although such approximations empirically demonstrate high performance, the sound algebraic structure of the vanishing ideal is lost. The present paper proposes a vanishing ideal that is tolerant to noisy data and also pursued to have a better algebraic structure. As a new problem, we simultaneously find a set of polynomials and data points for which the polynomials approximately vanish on the input data points, and almost exactly vanish on the discovered data points. In experimental classification tests, our method discovered much fewer and lower-degree polynomials than an existing state-of-the-art method. Consequently, our method accelerated the runtime of the classification tasks without degrading the classification accuracy.

It is well-known that the robustness of artificial neural networks (ANNs) is important for their wide ranges of applications. In this paper, we focus on the robustness of the classification ability of a spiking neural network which receives perturbed inputs. Actually, the perturbation is allowed to be arbitrary styles. However, Gaussian perturbation and other regular ones have been rarely investigated. For classification problems, the closer to the desired point, the more perturbed points there are in the input space. In addition, the perturbation may be periodic. Based on these facts, we only consider sinusoidal and Gaussian perturbations in this paper. With the SpikeProp algorithm, we perform extensive experiments on the classical XOR problem and other three benchmark datasets. The numerical results show that there is not significant reduction in the classification ability of the network if the input signals are subject to sinusoidal and Gaussian perturbations.