The last thing you want to see when you pull into a gas station is some doofus lighting up a smoke. Whether they missed the warning notices or, perhaps, the science class back at high school about open flames and flammable vapor is, in that moment at least, largely immaterial. As for your own course of action upon seeing such reckless behavior, you can either put your foot down and hightail it out of there before the whole place goes up, or yell at the smoker to put it the hell out. Tackling the very same issue, Shell has been working with Microsoft on a solution that aims to make all future visits to gas stations stress-free, at least in terms of potential explosive activity. The system uses Microsoft's Azure IoT Edge cloud intelligence system to quickly identify and deal with smokers at a gas station, and it's already being tested at two Shell stations in Thailand and Singapore.

If you are a senior data scientist or pro in predictive analytics, you would probably be using both R & Python, and maybe other tools like SAS, SQL etc. But, what if you are a beginner or just thinking about to start a career in data science, machine learning, and business analytics? Which one should you learn – R or Python? It has always been a topic of great debate among data scientists, researchers and analytics professionals. In this article, we will discuss R vs Python – usability, popularity index, advantages & limitations, job opportunities, and salaries. R is a statistical and visualization language which is deep and huge and mathematical.

Today's artificial intelligence systems, including the artificial neural networks broadly inspired by the neurons and connections of the nervous system, perform wonderfully at tasks with known constraints. They also tend to require a lot of computational power and vast quantities of training data. That all serves to make them great at playing chess or Go, at detecting if there's a car in an image, at differentiating between depictions of cats and dogs. "But they are rather pathetic at composing music or writing short stories," said Konrad Kording, a computational neuroscientist at the University of Pennsylvania. "They have great trouble reasoning meaningfully in the world." Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

Executives from Amazon, Apple, AT&T, Charter Communications, Google, and Twitter are heading to Washington Wednesday to testify before the Senate Commerce Committee on the topic of privacy. As ever, the main question will be: Are these companies doing enough to protect consumer privacy, and if not, what should Congress do about it? It has been the backdrop to just about every hearing with tech leaders over the last year--and there have been many. And yet, the threat of regulation carries new weight this time around. Over the summer, California passed the country's first data privacy bill, giving residents unprecedented control over their data.

Abstract--Future communications and data networks are expected to be largely cognitive self-organizing networks (CSON). Such networks will have the essential property of cognitive selforganization, which can be achieved using machine learning techniques (e.g., deep learning). Despite the potential of these techniques, these techniques in their current form are vulnerable to adversarial attacks that can cause cascaded damages with detrimental consequences for the whole network. In this paper, we explore the effect of adversarial attacks on CSON. Our experiments highlight the level of threat that CSON have to deal with in order to meet the challenges of next-generation networks and point out promising directions for future work. The idea that networks should learn to drive themselves is gaining traction [11], taking inspiration from self-driving cars where driving and related functionality do not require human intervention. The networking community wants to build a similar cognitive control in networks where networks are able to configure, manage, and protect themselves by interacting with the dynamic networking environment.We refer to such networks as cognitive self-organizing networks CSON. The expected complexity and heterogeneity of CSON makes machine learning (ML) a reasonable choice for realizing this ambitious goal. Recently artificial intelligence (AI) based CSON have attained a lot of attention in industry and academia.

Network representation aims to represent the nodes in a network as continuous and compact vectors, and has attracted much attention in recent years due to its ability to capture complex structure relationships inside networks. However, existing network representation methods are commonly designed for homogeneous information networks where all the nodes (entities) of a network are of the same type, e.g., papers in a citation network. In this paper, we propose a universal network representation approach (UNRA), that represents different types of nodes in heterogeneous information networks in a continuous and common vector space. The UNRA is built on our latest mutually updated neural language module, which simultaneously captures inter-relationship among homogeneous nodes and node-content correlation. Relationships between different types of nodes are also assembled and learned in a unified framework. Experiments validate that the UNRA achieves outstanding performance, compared to six other state-of-the-art algorithms, in node representation, node classification, and network visualization. In node classification, the UNRA achieves a 3\% to 132\% performance improvement in terms of accuracy.

We present a deep generative model, named Monge-Amp\`ere flow, which builds on continuous-time gradient flow arising from the Monge-Amp\`ere equation in optimal transport theory. The generative map from the latent space to the data space follows a dynamical system, where a learnable potential function guides a compressible fluid to flow towards the target density distribution. Training of the model amounts to solving an optimal control problem. The Monge-Amp\`ere flow has tractable likelihoods and supports efficient sampling and inference. One can easily impose symmetry constraints in the generative model by designing suitable scalar potential functions. We apply the approach to unsupervised density estimation of the MNIST dataset and variational calculation of the two-dimensional Ising model at the critical point. This approach brings insights and techniques from Monge-Amp\`ere equation, optimal transport, and fluid dynamics into reversible flow-based generative models.

Abstract--Time-continuous emotion prediction has become an increasingly compelling task in machine learning. Considerable efforts have been made to advance the performance of these systems. Nonetheless, the main focus has been the development of more sophisticated models and the incorporation of different expressive modalities (e. g., speech, face, and physiology). In this paper, motivated by the benefit of difficulty awareness in a human learning procedure, we propose a novel machine learning framework, namely, Dynamic Difficulty Awareness Training (DDAT), which sheds fresh light on the research - directly exploiting the difficulties in learning to boost the machine learning process. The DDAT framework consists of two stages: information retrieval and information exploitation. In the first stage, we make use of the reconstruction error of input features or the annotation uncertainty to estimate the difficulty of learning specific information. The obtained difficulty level is then used in tandem with original features to update the model input in a second learning stage with the expectation that the model can learn to focus on high difficulty regions of the learning process. We perform extensive experiments on a benchmark database (RECOLA) to evaluate the effectiveness of the proposed framework. The experimental results show that our approach outperforms related baselines as well as other well-established time-continuous emotion prediction systems, which suggests that dynamically integrating the difficulty information for neural networks can help enhance the learning process. Time-continuous emotion prediction systems have received widespread interest in the machine learning (ML) community over the past decade [1]-[3]. One of the main reasons for this interest is the fact that time-continuous emotion predictions can analyse subtle and complex affective states of humans over time and play a central role in smart conversational agents that aim to achieve a natural and intuitive interaction between humans and machines [2], [4]-[7]. Great efforts have been made in this field, and most of them can generally be classified into two strands. Z. Zhang is with GLAM - the Group on Language, Audio & Music, Imperial College London (UK).

Abstract--Commonsense knowledge is paramount to enable intelligent systems. Typically, it is characterized as being implicit and ambiguous, hindering thereby the automation of its acquisition. To address these challenges, this paper presents semantically enhanced models to enable reasoning through resolving part of commonsense ambiguity. The proposed models enhance in a knowledge graph embedding framework for knowledge base completion. Experimental results show the effectiveness of the new semantic models in commonsense reasoning. Intelligent systems need to acquire humanlike knowledge in order to perform smart decision making. This type of knowledge which is often termed commonsense knowledge refers to the agreed-upon facts and information about everyday world that is assumed to be shared by everyone.

WorkFusion, the leading AI-powered process automation provider, was awarded top honors in robotic process automation (RPA) and artificial intelligence (AI) by two independent panels of industry judges. Judges of the third annual AIconics selected WorkFusion as offering the Best Innovation in RPA from a shortlist that included three of the largest RPA providers. TechXLR8 Asia judges recognized WorkFusion for the Best Application of AI in Financial Services for helping 25% of the biggest U.S. banks, three of the top 10 insurance companies, and half of the top 10 global financial data providers to transform operations by integrating AI into a single, cost-effective automation platform for business people. The AIconics Awards preceded the AI Summit in San Francisco, and the TechXLR8 Asia Awards took place during the AI Summit in Singapore, both taking place concurrently September 19 – 20. AIconics judges selected WorkFusion Smart Process Automation (SPA), the company's flagship AI-driven process automation product, for the Best Innovation in RPA award for letting its customers automate complex processes from end-to-end on a single platform with the lowest total cost of ownership of any RPA product.